new plots for Diss. Mostly AI gen. Few changes in actual codebase
This commit is contained in:
Silas Oettinghaus
@@ -403,7 +403,7 @@ classdef Signal
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% Divide by 2*pi for the f/fs axis where Nyquist is 0.5.
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end
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p_lin = movmean(p_lin,10);
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p_lin = movmean(p_lin,20);
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if options.normalizeTo0dB
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p_lin = p_lin ./ max(p_lin);
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@@ -1042,6 +1042,7 @@ classdef Signal
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if isempty(finite_eye)
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finite_eye = sig(isfinite(sig));
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end
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if isa(obj,'Opticalsignal')
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amp_min = min(finite_eye);
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amp_max = max(finite_eye);
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amp_center = (amp_max + amp_min) / 2;
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@@ -1052,24 +1053,12 @@ classdef Signal
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amp_margin = 0.08 * amp_span;
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maxA = amp_center + amp_span/2 + amp_margin;
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minA = amp_center - amp_span/2 - amp_margin;
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if ~isa(obj,'Opticalsignal') && minA < 0 && maxA > 0
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targetStep = max(abs([minA maxA])) / 2;
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if targetStep > 0
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stepMagnitude = 10^floor(log10(targetStep));
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normalizedStep = targetStep / stepMagnitude;
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if normalizedStep <= 1
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tickStep = stepMagnitude;
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elseif normalizedStep <= 2
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tickStep = 2 * stepMagnitude;
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elseif normalizedStep <= 5
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tickStep = 5 * stepMagnitude;
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else
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tickStep = 10 * stepMagnitude;
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end
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axisLimit = 2 * tickStep;
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maxA = axisLimit;
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minA = -axisLimit;
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end
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% Normalized electrical/digital eye display range.
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% Samples outside this range are clipped in the
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% histogram image so all eye plots use the same scale.
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minA = -3;
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maxA = 3;
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end
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% maxA = 0.12;
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@@ -1109,13 +1098,13 @@ classdef Signal
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max_ = abs(max(obj.signal(100:end-100)).^2);
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elseif isa(obj,'Electricalsignal')
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title(['Electrical Eye ',options.displayname])
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ylabel("Voltage in V");
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ylabel("Normalized amplitude");
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yTickValues = linspace(maxA,minA,5);
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min_ = min(obj.signal(100:end-100));
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max_ = abs(max(obj.signal(100:end-100)));
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else
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title(['Digital Eye ',options.displayname])
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ylabel("Digital Signal Amplitude");
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ylabel("Normalized amplitude");
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yTickValues = linspace(maxA,minA,5);
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min_ = min(obj.signal(100:end-100));
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max_ = abs(max(obj.signal(100:end-100)));
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@@ -212,6 +212,81 @@ classdef ChannelFreqResp < handle
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end
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function Target = apply(obj,Target,options)
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% Apply the measured magnitude-only channel response.
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arguments
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obj
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Target
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options.fileName = ''
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options.loadPath = ''
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options.maxampdb double = 0
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end
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if isempty(obj.H)
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obj.load("fileName",options.fileName,"loadPath",options.loadPath);
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end
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fstarget = Target.fs;
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N_target = numel(Target.signal);
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% Ignore measured DC attenuation when setting the response
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% reference level.
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H_measured = abs(obj.H(:)).';
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H_measured_valid = isfinite(H_measured);
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H_measured_dB = 20*log10(H_measured);
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idx_old = (obj.faxis > 0) & ...
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(obj.faxis < fstarget/2) & H_measured_valid;
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H_reference_dB = max(H_measured_dB(idx_old));
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H_measured_dB = H_measured_dB - H_reference_dB + options.maxampdb;
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H_measured = 10.^(H_measured_dB/20);
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H_measured(~H_measured_valid) = 0;
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% Positive-frequency FFT bins, excluding DC and Nyquist.
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N_positive = floor((N_target - 1)/2);
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fnew = (1:N_positive)*fstarget/N_target;
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f_old = obj.faxis(idx_old);
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H_old = H_measured(idx_old);
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H_interp = interp1(f_old,H_old,fnew,'linear');
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% Match the edge handling used by the precompensation path.
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nH = find(isfinite(H_interp),1,'first');
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if isempty(nH)
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error('ChannelFreqResp:InterpolationFailed', ...
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'The measured response does not overlap the target frequency grid.');
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end
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H_interp(1:nH-1) = H_interp(nH);
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nH = find(isfinite(H_interp),1,'last');
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H_interp(nH+1:end) = H_interp(nH);
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% Real, zero-phase, conjugate-symmetric FFT response.
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if mod(N_target,2) == 0
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H_apply = [H_interp(1), H_interp, 0, fliplr(H_interp)];
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else
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H_apply = [H_interp(1), H_interp, fliplr(H_interp)];
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end
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H_apply = H_apply(:);
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% Re-anchor the final interpolated response using only the
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% positive-frequency bins, so DC attenuation is ignored.
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H_apply_dB = 20*log10(abs(H_apply));
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positive_bins = 2:(N_positive + 1);
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H_apply_dB = H_apply_dB - max(H_apply_dB(positive_bins)) + options.maxampdb;
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H_apply = 10.^(H_apply_dB/20);
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obj.H_apply = H_apply;
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% Apply the channel in the frequency domain and preserve shape.
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signal_shape = size(Target.signal);
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signal_in = real(Target.signal(:));
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signal_out = real(ifft(fft(signal_in).*H_apply));
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Target.signal = reshape(signal_out,signal_shape);
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end
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function plot(obj)
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figure();
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@@ -45,6 +45,8 @@ classdef ML_MLSE_DUOBINARY < handle
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valid
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valid_to_idx
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valid_from_idx
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incoming_edge_idx
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incoming_from_idx
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w
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% Fast lookup
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@@ -124,6 +126,21 @@ classdef ML_MLSE_DUOBINARY < handle
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end
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[obj.valid_to_idx,obj.valid_from_idx] = find(obj.valid);
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% Precompute compact incoming-transition lookup tables. Every
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% trellis state has obj.S incoming transitions, so compare-select
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% can operate on valid edges only instead of an nStates-by-nStates
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% matrix for every symbol.
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obj.incoming_edge_idx = zeros(obj.S, obj.nStates);
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obj.incoming_from_idx = zeros(obj.S, obj.nStates, 'uint32');
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incoming_count = zeros(obj.nStates, 1);
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for edge_idx = 1:numel(obj.valid_to_idx)
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to_idx = obj.valid_to_idx(edge_idx);
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slot = incoming_count(to_idx) + 1;
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obj.incoming_edge_idx(slot, to_idx) = edge_idx;
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obj.incoming_from_idx(slot, to_idx) = obj.valid_from_idx(edge_idx);
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incoming_count(to_idx) = slot;
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end
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% --- Initialize weights
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if isempty(obj.w) || any(size(obj.w) ~= [obj.Nf+1,obj.nFeasible])
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% obj.w = randn(obj.Nf+1,obj.nFeasible);
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@@ -148,6 +165,8 @@ classdef ML_MLSE_DUOBINARY < handle
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% TRAINING
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% ==============================================================
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fprintf('\n--- Training mode ---\n');
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obj.ber = nan(1, obj.epochs_tr);
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obj.ce = nan(1, obj.epochs_tr);
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obj.equalize(X.signal, D.signal, obj.mu_tr, obj.epochs_tr, obj.len_tr, true);
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obj.e_tr = obj.e;
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@@ -155,6 +174,7 @@ classdef ML_MLSE_DUOBINARY < handle
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% DECISION-DIRECTED / TESTING
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% ==============================================================
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fprintf('--- Decision-directed / detection mode ---\n');
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obj.ber_dd = nan(1, obj.epochs_dd);
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[y, y_vit] = obj.equalize(X.signal, D.signal, obj.mu_dd, obj.epochs_dd, X.length, false);
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X_viterbi = X;
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@@ -173,9 +193,18 @@ classdef ML_MLSE_DUOBINARY < handle
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for epoch = 1:epochs
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pm = zeros(obj.nStates,1);
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needTraceback = ~training || epoch == epochs;
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if needTraceback
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pred = zeros(nSymbols,obj.nStates,'uint32');
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end
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if debug && showPlots
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pm_sto = nan(obj.nStates,nSymbols,'like',pm);
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end
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CE_accum = 0;
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if training
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CE_symbol = zeros(nSymbols,1);
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CE_smooth = zeros(nSymbols,1);
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end
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start_sample = 1;
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end_sample = N;
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@@ -292,14 +321,25 @@ classdef ML_MLSE_DUOBINARY < handle
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% ===================================================================
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% DECODING MODE (Viterbi only)
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% ===================================================================
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% Compare-Select (always executed)
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vmat=inf(obj.nStates,obj.nStates);
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vmat(obj.valid)=v_tilde;
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[pm_next,pred(symbol,:)]=min(vmat,[],2);
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% Compare-select over valid incoming transitions only.
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incoming_metrics = v_tilde(obj.incoming_edge_idx);
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[pm_next, predecessor_slot] = min(incoming_metrics,[],1);
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pm_next = pm_next.';
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pm_next=pm_next-min(pm_next);
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pm=pm_next;
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if needTraceback
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linear_idx = predecessor_slot + (0:obj.nStates-1) .* obj.S;
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pred(symbol,:) = obj.incoming_from_idx(linear_idx);
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end
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if debug && showPlots
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pm_sto(:,symbol)=pm;
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end
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end
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if training && ~needTraceback
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obj.ce(epoch)=CE_accum/symbol;
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continue
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end
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% --- Traceback
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[~,s_end]=min(pm);
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@@ -14,6 +14,21 @@ classdef clr
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% Paired colormap
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Paired = struct( ...
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'lblue', [0.6510, 0.8078, 0.8902], ...
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'dblue', [0.1216, 0.4706, 0.7059], ...
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'mblue', [0.3863, 0.6392, 0.7981], ...
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'lgreen', [0.6980, 0.8745, 0.5412], ...
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'dgreen', [0.2000, 0.6275, 0.1725], ...
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'mgreen', [0.4490, 0.7510, 0.3569], ...
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'lred', [0.9843, 0.6039, 0.6000], ...
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'dred', [0.8902, 0.1020, 0.1098], ...
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'mred', [0.9373, 0.3530, 0.3549], ...
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'lorange', [0.9922, 0.7490, 0.4353], ...
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'dorange', [1.0000, 0.4980, 0.0000], ...
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'morange', [0.9961, 0.6235, 0.2176], ...
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'llila', [0.7922, 0.6980, 0.8392], ...
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'dlila', [0.4157, 0.2392, 0.6039], ...
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'mlila', [0.6040, 0.4686, 0.7216], ...
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'lightblue', [0.6510, 0.8078, 0.8902], ...
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'blue', [0.1216, 0.4706, 0.7059], ...
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'lightgreen', [0.6980, 0.8745, 0.5412], ...
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@@ -104,13 +104,13 @@ function [db_results] = duobinary_target(eq_, mlse_,M, rx_signal, tx_symbols, tx
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[bits_db,errors_db,ber_db,a] = calc_ber(rx_bits_mlse.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
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burst_db = count_error_bursts(a, 40);
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cols = linspecer(8);
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figure();hold on;
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stem(1:40,burst_db,'LineWidth',1,'Color',cols(4,:),'Marker','_','DisplayName','w/o diff. precoder');
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stem(1:40,burst_db_precoded,'LineWidth',1,'Color',cols(3,:),'Marker','.','LineStyle','-','DisplayName','w diff. precoder');
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xlabel('Bit Error Burst Length')
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ylabel('Occurence')
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set(gca, 'yscale', 'log');
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% cols = linspecer(8);
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% figure();hold on;
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% stem(1:40,burst_db,'LineWidth',1,'Color',cols(4,:),'Marker','_','DisplayName','w/o diff. precoder');
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% stem(1:40,burst_db_precoded,'LineWidth',1,'Color',cols(3,:),'Marker','.','LineStyle','-','DisplayName','w diff. precoder');
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% xlabel('Bit Error Burst Length')
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% ylabel('Occurence')
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% set(gca, 'yscale', 'log');
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end
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% M = numel(unique(tx_symbols.signal));
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@@ -118,6 +118,8 @@ function [db_results] = duobinary_target(eq_, mlse_,M, rx_signal, tx_symbols, tx
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[bits_db,errors_db,ber_db,errorIndice_db] = calc_ber(rx_bits.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
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[snr_db_target, snr_db_target_lvl] = calc_snr(db_ref_sequence.signal, eq_noise.signal);
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alpha = arburg(eq_noise.signal,1);%pf_.coefficients(2);
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alpha = alpha(2);
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@@ -152,6 +154,8 @@ function [db_results] = duobinary_target(eq_, mlse_,M, rx_signal, tx_symbols, tx
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db_results.metrics.AIR = air_mlse;
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db_results.metrics.MLSE_dir = mlse_.DIR;
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db_results.metrics.Alpha = alpha;
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db_results.metrics.SNR = snr_db_target;
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db_results.metrics.SNR_level = snr_db_target_lvl;
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% Create DB results structure
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db_results.config = Equalizerstruct();
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@@ -177,7 +181,10 @@ function [db_results] = duobinary_target(eq_, mlse_,M, rx_signal, tx_symbols, tx
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showEQNoisePSD(eq_noise,"fignum",250,"displayname",'Duobinary Target Noise after Equalization');
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fprintf('DB tgt BER: %.2e \n',ber_db);
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if options.decoding_mode == db_decoder.sequencedetection
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fprintf('DB tgt BER: %.2e \n',ber_db); %not relevant for memoryless, wont work
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end
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fprintf('DB tgt BER precoded: %.2e \n',ber_db_diff_precoded);
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figure(341); clf;
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tx_symbols_uncoded = Duobinary().decode(db_ref_sequence);
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@@ -50,6 +50,11 @@ eq_signal_hd = PAMmapper(M, 0).quantize(eq_signal_sd);
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[tx_symbols_pr,~] = pf_.process(tx_symbols, eq_noise);
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% Calc SNR of Partial Response Signal
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pr_noise = tx_symbols_pr-mlse_sig_sd;
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[snr_partial_response, ~] = calc_snr(tx_symbols_pr.signal, pr_noise.signal);
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if 0 %tx_symbols.fs > 190e9
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if pf_.ncoeff == 1
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if pf_.coefficients(2) < 0
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@@ -83,7 +88,7 @@ mlse_sig_hd = PAMmapper(M, 0, "eth_style", options.eth_style_symbol_mapping).qua
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%% Calculate performance metrics
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% VNLE metrics
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[snr_vnle, snr_vnle_lvl] = calc_snr(tx_symbols.signal, eq_noise.signal);
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[snr_full_response, snr_vnle_lvl] = calc_snr(tx_symbols.signal, eq_noise.signal);
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% [gmi_vnle] = calc_air(eq_signal_sd, tx_symbols, "skip_front", 10000, "skip_end", 10000);
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%calculate bitwise GMI
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@@ -131,7 +136,7 @@ ffe_results.metrics.numBits = numbits.vnle;
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ffe_results.metrics.numBitErr = errors.vnle;
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ffe_results.metrics.BER_precoded = bers.vnle_precoded;
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ffe_results.metrics.numBitErr_precoded = errors.vnle_precoded;
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ffe_results.metrics.SNR = snr_vnle;
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ffe_results.metrics.SNR = snr_full_response;
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ffe_results.metrics.SNR_level = snr_vnle_lvl;
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ffe_results.metrics.STD = std_vnle_total;
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ffe_results.metrics.STD_level = std_vnle_lvl;
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@@ -165,7 +170,7 @@ mlse_results.metrics.numBits = numbits.mlse;
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mlse_results.metrics.numBitErr = errors.mlse;
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mlse_results.metrics.BER_precoded = bers.mlse_precoded;
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mlse_results.metrics.numBitErr_precoded = errors.mlse_precoded;
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% mlse_results.metrics.SNR = NaN;
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mlse_results.metrics.SNR = snr_partial_response;
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mlse_results.metrics.GMI = gmi_mlse;
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mlse_results.metrics.AIR = air_mlse;
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% mlse_results.metrics.EVM = NaN;
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@@ -225,6 +225,7 @@ else
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ml_mlse_db_results.config.comment = 'function: ML-based MLSE; duobinary encoded';
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ml_mlse_db_results.recipe_config = collectRecipeConfig("ml_mlse_db", ml_mlse_db_equalizer, p, options);
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output.mlmlse_db_package = ml_mlse_db_results;
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ml_mlse_db_results.metrics.print;
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end
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if p.run_mlse_db
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@@ -275,10 +276,10 @@ p = struct();
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p.run_ffe = false;
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p.run_vnle = false;
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p.run_dfe = false;
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p.run_vnle_mlse = false;
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p.run_dbtgt = false;
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p.run_ml_mlse = true; % non-encoded and precoded branches
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p.run_ml_mlse_db = true; % db_encoded: ML-based MLSE
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p.run_vnle_mlse = true;
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p.run_dbtgt = true;
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p.run_ml_mlse = false; % non-encoded and precoded branches
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p.run_ml_mlse_db = false; % db_encoded: ML-based MLSE
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p.run_mlse_db = true; % db_encoded: conventional MLSE
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@@ -321,7 +322,7 @@ p.decoding_mode = [];
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p.ml_mlse_mu_tr = 0.03;
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p.ml_mlse_mu_dd = 0.03;
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p.ml_mlse_epochs_tr = 100;
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p.ml_mlse_epochs_tr = 150;
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p.ml_mlse_epochs_dd = 1;
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p.ml_mlse_len_tr = [];
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p.ml_mlse_order = 11;
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@@ -376,20 +377,22 @@ end
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||||
|
||||
function dbtgt_results = runDuobinaryTarget(eq_dbtgt, mlse_db, ...
|
||||
Scpe_sig, Symbols, Tx_bits, options, p)
|
||||
if isempty(p.decoding_mode)
|
||||
|
||||
if isempty(p.decoding_mode)
|
||||
dbtgt_results = duobinary_target(eq_dbtgt, mlse_db, options.M, ...
|
||||
Scpe_sig, Symbols, Tx_bits, ...
|
||||
"precode_mode", options.duob_mode, ...
|
||||
"showAnalysis", options.debug_plots, ...
|
||||
"postFFE", []);
|
||||
else
|
||||
else
|
||||
dbtgt_results = duobinary_target(eq_dbtgt, mlse_db, options.M, ...
|
||||
Scpe_sig, Symbols, Tx_bits, ...
|
||||
"precode_mode", options.duob_mode, ...
|
||||
"showAnalysis", options.debug_plots, ...
|
||||
"postFFE", [], ...
|
||||
"decoding_mode", p.decoding_mode);
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
function mlse = buildMlse(M, duobMode, p, mode)
|
||||
|
||||
@@ -30,7 +30,7 @@ end
|
||||
|
||||
% Ensure the figure is ready before calling spectrum
|
||||
eq_noise = eq_noise - mean(eq_noise.signal);
|
||||
eq_noise.spectrum("displayname", options.displayname, "fignum", fig.Number, "normalizeTo0dB", 0,"color",options.color,"fft_length",4096);
|
||||
eq_noise.spectrum("displayname", options.displayname, "fignum", fig.Number, "normalizeTo0dB", 0,"color",options.color,"fft_length",4096,"normalizeToDC",0);
|
||||
|
||||
if ~isnan(options.postfilter_taps)
|
||||
% Hold on to the figure for further plotting
|
||||
|
||||
@@ -5,7 +5,8 @@ arguments
|
||||
options.fignum (1,1) double = NaN % Default to NaN if not provided
|
||||
options.displayname (1,:) char = '' % Default to an empty string if not provided
|
||||
options.ref_symbol_uncoded = []
|
||||
options.nbins (1,1) double = 1000
|
||||
options.nbins (1,1) double = 200
|
||||
options.legendLabels = []
|
||||
end
|
||||
|
||||
if isa(eq_signal,'Signal')
|
||||
@@ -21,6 +22,7 @@ end
|
||||
eq_signal = eq_signal(:);
|
||||
ref_symbols = ref_symbols(:);
|
||||
ref_symbol_uncoded = options.ref_symbol_uncoded(:);
|
||||
legend_labels = string(options.legendLabels);
|
||||
|
||||
assert(numel(eq_signal) == numel(ref_symbols), ...
|
||||
'showLevelHistogram:LengthMismatch', ...
|
||||
@@ -66,11 +68,16 @@ end
|
||||
for lvl = 1:numel(constellation)
|
||||
intermediate = received_sd(lvl,:);
|
||||
cnt(lvl) = round(numel(intermediate(~isnan(intermediate)))./numel(eq_signal),3).*100;
|
||||
if isempty(legend_labels)
|
||||
display_name = ['Lvl ',num2str(lvl),' ; ',num2str(cnt(lvl)),' '];
|
||||
else
|
||||
display_name = char(legend_labels(lvl));
|
||||
end
|
||||
hold on
|
||||
warning off
|
||||
histogram(received_sd(lvl,:),options.nbins, ...
|
||||
"EdgeAlpha",0, ...
|
||||
"DisplayName",['Lvl ',num2str(lvl),' ; ',num2str(cnt(lvl)),' '], ...
|
||||
"DisplayName",display_name, ...
|
||||
"FaceColor",lvlcol(lvl,:), ...
|
||||
"Normalization","pdf");
|
||||
warning on
|
||||
@@ -90,7 +97,7 @@ end
|
||||
rm_idx = mid + (-1:2); % two before mid and two after (2x2 removal centered)
|
||||
rm_idx = max(1,min(size(lvlcol,1),rm_idx));
|
||||
lvlcol(rm_idx,:) = [];
|
||||
lvlcol = lvlcol(1:numel(sir_group_labels),:);
|
||||
% lvlcol = lvlcol(1:numel(sir_group_labels),:);
|
||||
|
||||
for db_lvl = 1:numel(db_constellation)
|
||||
db_mask = ref_symbols == db_constellation(db_lvl);
|
||||
@@ -99,12 +106,20 @@ end
|
||||
cnt = round(nnz(ref_symbol_uncoded == mapped_class)./numel(eq_signal),3).*100;
|
||||
|
||||
if db_lvl == findFirstMappedDbLevel(ref_symbols,ref_symbol_uncoded,db_constellation,mapped_class)
|
||||
if isempty(legend_labels)
|
||||
display_name = ['p(y|x_',num2str(class_idx),') ; ',num2str(cnt),' '];
|
||||
display_name = ['p(y|x_',num2str(class_idx),')'];
|
||||
else
|
||||
display_name = char(legend_labels(class_idx));
|
||||
end
|
||||
handle_visibility = "on";
|
||||
else
|
||||
if isempty(legend_labels)
|
||||
display_name = ['p(y|x_',num2str(class_idx),') ; ',num2str(cnt),' '];
|
||||
display_name = ['p(y|x_',num2str(class_idx),')'];
|
||||
else
|
||||
display_name = char(legend_labels(class_idx));
|
||||
end
|
||||
handle_visibility = "off";
|
||||
end
|
||||
|
||||
@@ -123,7 +138,7 @@ end
|
||||
end
|
||||
end
|
||||
xlim([-3 3]);
|
||||
legend
|
||||
legend("Interpreter", "latex");
|
||||
grid on
|
||||
|
||||
% view([90 -90]);
|
||||
|
||||
@@ -3,7 +3,11 @@ function [Bits, Symbols, Scpe_cell, found_sync] = loadAndSyncRunSignals(dataTabl
|
||||
%
|
||||
% Inputs:
|
||||
% dataTable - one-row table with run metadata and signal file paths
|
||||
% options - struct with storage_path, start_occurence and max_occurences
|
||||
% options - scalar struct with the following fields:
|
||||
% storage_path - root directory containing the signal files.
|
||||
% start_occurence - first synchronized occurrence to return (default: 1).
|
||||
% max_occurences - maximum number of synchronized occurrences to return
|
||||
% (default: 1).
|
||||
%
|
||||
% Outputs:
|
||||
% Bits - transmitted bit reference
|
||||
@@ -11,6 +15,14 @@ function [Bits, Symbols, Scpe_cell, found_sync] = loadAndSyncRunSignals(dataTabl
|
||||
% Scpe_cell - synchronized received signal occurrences
|
||||
% found_sync - true when a valid synchronization was found
|
||||
|
||||
arguments
|
||||
dataTable
|
||||
options
|
||||
% options.storage_path
|
||||
% options.start_occurence = 1
|
||||
% options.max_occurences = 1
|
||||
end
|
||||
|
||||
found_sync = 0;
|
||||
tempLocalStorage = 1;
|
||||
Scpe_cell = {};
|
||||
@@ -130,7 +142,7 @@ end
|
||||
function Scpe_cell = selectSyncedOccurrences(Scpe_cell, options)
|
||||
available_occurences = length(Scpe_cell);
|
||||
start_occurence = floor(getOption(options, 'start_occurence', 1));
|
||||
max_occurences = floor(getOption(options, 'max_occurences', available_occurences));
|
||||
max_occurences = floor(getOption(options, 'max_occurences', 1));
|
||||
|
||||
if available_occurences < 1
|
||||
warning('loadAndSyncRunSignals:NoSyncedOccurrences', ...
|
||||
|
||||
@@ -29,7 +29,9 @@ function [snr_all, snr_per_level] = calc_snr(tx_signal, eq_noise)
|
||||
|
||||
% Get the unique amplitude levels in the transmitted signal
|
||||
levels = unique(tx_signal);
|
||||
snr_per_level = zeros(numel(levels),1);
|
||||
|
||||
try
|
||||
% Preallocate an array to store the SNR for each unique level
|
||||
% Loop over each unique level to compute the SNR for that level
|
||||
for i = 1:length(levels)
|
||||
@@ -40,4 +42,8 @@ function [snr_all, snr_per_level] = calc_snr(tx_signal, eq_noise)
|
||||
snr_per_level(i) = snr(tx_signal(idx), eq_noise(idx));
|
||||
|
||||
end
|
||||
catch ME
|
||||
% Handle any errors that occur during SNR calculation
|
||||
% warning('Error calculating SNR for level %d: %s', levels(i), ME.message);
|
||||
end
|
||||
end
|
||||
|
||||
24
Functions/frequency_wavelength/calcWavelengthPlan.m
Normal file
24
Functions/frequency_wavelength/calcWavelengthPlan.m
Normal file
@@ -0,0 +1,24 @@
|
||||
function channelplan_nm = calcWavelengthPlan(N, df_hz, center_nm)
|
||||
|
||||
vec = [N:-1:1]-(N/2+0.5);
|
||||
|
||||
channelplan_hz = nm2hz(center_nm) + (vec * df_hz) ;
|
||||
|
||||
channelplan_nm = hz2nm(channelplan_hz);
|
||||
|
||||
% dfft = 1.367053998632946e+07; %hz
|
||||
% a = diff(channelplan_hz)./2./dfft;
|
||||
|
||||
end
|
||||
|
||||
function hz = nm2hz(nm)
|
||||
wavelen_in_m = nm.* 1e-9;
|
||||
hz = (299792458 ./ wavelen_in_m); %frequency in Terahertz
|
||||
end
|
||||
|
||||
function nm = hz2nm(hz)
|
||||
m = (299792458 ./ hz); %wavelength in meter
|
||||
nm = m .* 10^9;
|
||||
end
|
||||
|
||||
|
||||
4
Functions/frequency_wavelength/df2dw.m
Normal file
4
Functions/frequency_wavelength/df2dw.m
Normal file
@@ -0,0 +1,4 @@
|
||||
function dw = df2dw(df,f)
|
||||
%delta frequency to delta wavelength
|
||||
dw = 299792458 .* df ./ f.^2;
|
||||
end
|
||||
4
Functions/frequency_wavelength/dw2df.m
Normal file
4
Functions/frequency_wavelength/dw2df.m
Normal file
@@ -0,0 +1,4 @@
|
||||
function df = dw2df(dw,w)
|
||||
%delta wavelength to delta frequency
|
||||
df = 299792458 .* dw ./ w.^2;
|
||||
end
|
||||
7
Functions/frequency_wavelength/getSweepWavelengths.m
Normal file
7
Functions/frequency_wavelength/getSweepWavelengths.m
Normal file
@@ -0,0 +1,7 @@
|
||||
function positions = getSweepWavelengths(N, df_hz, StartNm)
|
||||
positions = zeros(1, N);
|
||||
for i = 1:N
|
||||
position = (299792458 / (299792458 / (StartNm * 1e-9) + (i - 1) * df_hz)) * 1e9;
|
||||
positions(i) = position;
|
||||
end
|
||||
end
|
||||
4
Functions/frequency_wavelength/hz2nm.m
Normal file
4
Functions/frequency_wavelength/hz2nm.m
Normal file
@@ -0,0 +1,4 @@
|
||||
function nm = hz2nm(hz)
|
||||
m = (299792458 ./ hz); %wavelength in meter
|
||||
nm = m .* 10^9;
|
||||
end
|
||||
4
Functions/frequency_wavelength/nm2hz.m
Normal file
4
Functions/frequency_wavelength/nm2hz.m
Normal file
@@ -0,0 +1,4 @@
|
||||
function hz = nm2hz(nm)
|
||||
wavelen_in_m = nm.* 1e-9;
|
||||
hz = (299792458 ./ wavelen_in_m); %frequency in Hertz
|
||||
end
|
||||
4
Functions/frequency_wavelength/nm2thz.m
Normal file
4
Functions/frequency_wavelength/nm2thz.m
Normal file
@@ -0,0 +1,4 @@
|
||||
function thz = nm2thz(nm)
|
||||
wavelen_in_m = nm.* 1e-9;
|
||||
thz = (299792458 ./ wavelen_in_m) .*1e-12; %frequency in Terahertz
|
||||
end
|
||||
4
Functions/frequency_wavelength/thz2nm.m
Normal file
4
Functions/frequency_wavelength/thz2nm.m
Normal file
@@ -0,0 +1,4 @@
|
||||
function nm = thz2nm(thz)
|
||||
freq_in_hz = thz.* 1e12;
|
||||
nm = (299792458 ./ freq_in_hz) .*1e9; %wavelength in nanometer
|
||||
end
|
||||
@@ -16,6 +16,10 @@ matlab2tikz(char(filename), ...
|
||||
'height', '\fheight', ...
|
||||
'showInfo', false, ...
|
||||
'extraAxisOptions', { ...
|
||||
'clip=true',...
|
||||
'clip marker paths=true',...
|
||||
'grid style={dashed, draw=gray80, line width=0.25pt}',...
|
||||
'minor grid style={densely dotted, draw=gray80, line width=0.25pt}',...
|
||||
'xlabel style={font=\color{white!15!black}\small}', ...
|
||||
'ylabel style={font=\color{white!15!black}\small}', ...
|
||||
'scaled ticks=false', ...
|
||||
|
||||
30
Functions/saveDirectoryInfo.m
Normal file
30
Functions/saveDirectoryInfo.m
Normal file
@@ -0,0 +1,30 @@
|
||||
function outputFile = saveDirectoryInfo(directoryPath)
|
||||
% Save metadata for all files and subdirectories under directoryPath.
|
||||
|
||||
directoryPath = string(directoryPath);
|
||||
|
||||
if ~isfolder(directoryPath)
|
||||
error("Directory does not exist: %s", directoryPath);
|
||||
end
|
||||
|
||||
% Recursively list all files and subdirectories
|
||||
info = dir(fullfile(directoryPath, "**", "*"));
|
||||
|
||||
% Remove "." and ".." entries
|
||||
info = info(~ismember({info.name}, {'.', '..'}));
|
||||
|
||||
% Create an output filename in MATLAB's current folder
|
||||
[~, folderName] = fileparts(char(directoryPath));
|
||||
if isempty(folderName)
|
||||
folderName = 'directory';
|
||||
end
|
||||
|
||||
timestamp = datestr(now, 'yyyymmdd_HHMMSS');
|
||||
outputFile = fullfile(pwd, ...
|
||||
sprintf('directory_info_%s_%s.mat', folderName, timestamp));
|
||||
|
||||
% Save the path and directory information
|
||||
save(outputFile, 'directoryPath', 'info');
|
||||
|
||||
fprintf('Saved information to:\n%s\n', outputFile);
|
||||
end
|
||||
@@ -46,6 +46,27 @@ classdef ML_MLSE_DUOBINARY_test < IMDDTestCase
|
||||
txSymbols.signal(11:end-10), "AbsTol", 1e-12);
|
||||
end
|
||||
|
||||
function intermediateTrainingEpochsKeepWeightsWithoutTracebackBer(testCase)
|
||||
[~, ~, txPrecoded, txEncoded] = makePam4Fixture(512);
|
||||
|
||||
eq = ML_MLSE_DUOBINARY( ...
|
||||
"sps", 1, ...
|
||||
"order", 1, ...
|
||||
"len_tr", length(txEncoded), ...
|
||||
"epochs_tr", 2, ...
|
||||
"epochs_dd", 1, ...
|
||||
"mu_tr", 0.01, ...
|
||||
"mu_dd", 0.01, ...
|
||||
"adaptive_mu", false, ...
|
||||
"L", 1);
|
||||
|
||||
eq.process(txEncoded, txPrecoded);
|
||||
|
||||
testCase.verifyTrue(isnan(eq.ber(1)));
|
||||
testCase.verifyTrue(isfinite(eq.ber(2)));
|
||||
testCase.verifyTrue(all(isfinite(eq.ce)));
|
||||
end
|
||||
|
||||
function resultWrapperReportsBothPrecodedAndOriginalBer(testCase)
|
||||
[txBits, ~, txPrecoded, txEncoded] = makePam4Fixture(32000);
|
||||
|
||||
|
||||
BIN
directory_info_sioe_labor_20260720_114654.mat
Normal file
BIN
directory_info_sioe_labor_20260720_114654.mat
Normal file
Binary file not shown.
@@ -5,25 +5,32 @@ fdac = 256e9;%fsym;
|
||||
fadc = 256e9;
|
||||
fsym = ([32:16:240].*1e9);
|
||||
|
||||
%fsym = 160e9;
|
||||
% 1) PRBS Generation
|
||||
O = 18; %order of prbs
|
||||
N = 2^(O-1); %length of prbs
|
||||
[~,seed] = prbs(O,1); %initialize first seed of prbs
|
||||
bitpattern=[];
|
||||
% %fsym = 160e9;
|
||||
% % 1) PRBS Generation
|
||||
% O = 18; %order of prbs
|
||||
% N = 2^(O-1); %length of prbs
|
||||
% [~,seed] = prbs(O,1); %initialize first seed of prbs
|
||||
% bitpattern=[];
|
||||
%
|
||||
% for i = 1:log2(M)
|
||||
% [bitpattern(:,i),seed] = prbs(O,N,seed);
|
||||
% end
|
||||
%
|
||||
%
|
||||
% if M == 6
|
||||
% bitpattern = reshape(bitpattern,[],1);
|
||||
% bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
|
||||
% end
|
||||
|
||||
for i = 1:log2(M)
|
||||
[bitpattern(:,i),seed] = prbs(O,N,seed);
|
||||
end
|
||||
bits = Signalgenerator( ...
|
||||
"form", signalform.prms, ...
|
||||
"M", M, ...
|
||||
"order", 17).process();
|
||||
% symbols = PAMmapper(4, 0).map(bits);
|
||||
|
||||
|
||||
if M == 6
|
||||
bitpattern = reshape(bitpattern,[],1);
|
||||
bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
|
||||
end
|
||||
|
||||
% 2 ) Build Inf. signal class
|
||||
bits = Informationsignal(bitpattern);
|
||||
% bits = Informationsignal(bitpattern);
|
||||
|
||||
% 5) AWG (lowpass, quantization, sample and hold)
|
||||
kover = 8;
|
||||
@@ -131,7 +138,7 @@ ylabel("Vpp in V")
|
||||
legend
|
||||
ylim([0.3 1.4])
|
||||
|
||||
|
||||
%%
|
||||
figure(7)
|
||||
hold on
|
||||
scatter(fsym.*1e-9,powerlist1,'DisplayName','M8199B','MarkerFaceColor',cols(1,:),'MarkerEdgeColor',cols(1,:),'LineWidth',2);
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||
dsp_options.max_occurences = 1;
|
||||
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
|
||||
dsp_options.max_occurences = 10;
|
||||
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
||||
|
||||
|
||||
@@ -7,34 +7,32 @@ cols = cbrewer2('BuPu',25);
|
||||
cols = [cols(end-10:2:end,:)];
|
||||
cols = cbrewer2('Set1',6);
|
||||
|
||||
fignum = 200;
|
||||
fig=figure(fignum);clf;
|
||||
|
||||
dbmode = 0;
|
||||
|
||||
% 1 - PAM 4 with preemphasis
|
||||
fp = QueryFilter();
|
||||
M = 8;
|
||||
rate = [360e9];
|
||||
M = 4;
|
||||
% rate = [300e9];
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
|
||||
% fp.where('Runs', 'symbolrate','EQUALS',165e9);
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
fp.where('Runs', 'db_mode','EQUALS', dbmode);
|
||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||
|
||||
[dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
|
||||
|
||||
dataTable = queryRunid(dataTable.run_id, database);
|
||||
dataTable = dataTable(1,:);
|
||||
% dataTable = queryRunid(dataTable.run_id, database);
|
||||
fsym = dataTable.symbolrate;
|
||||
M = double(dataTable.pam_level);
|
||||
duob_mode = db_mode(strrep(dataTable.db_mode,'"',''));
|
||||
|
||||
% Load and Sync signal data from DB
|
||||
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncRunSignals(dataTable, dsp_options);
|
||||
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncRunSignals(dataTable,dsp_options);
|
||||
|
||||
Scpe_sig_syncd = Scpe_cell{1};
|
||||
Scpe_sig_syncd.eye(fsym,M,"fignum",rate.*1e-9*M+1,"displayname",' Eye of Signal');
|
||||
% Scpe_sig_syncd.eye(fsym,M,"fignum",rate.*1e-9*M+1,"displayname",' Eye of Signal');
|
||||
%%%%%% SNR CHEAT - Avges the measured signal occurences found after correlation in "tsynch" %%%%%%
|
||||
average_signals = 1;
|
||||
if average_signals
|
||||
@@ -46,16 +44,19 @@ if average_signals
|
||||
scope_mean = scope_mean ./ n;
|
||||
Scpe_sig_avg.signal = scope_mean;
|
||||
|
||||
Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1);
|
||||
Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
|
||||
% Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1);
|
||||
% Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
|
||||
Scpe_sig_avg = Scpe_sig_avg.*1.25;
|
||||
Scpe_sig_avg.eye(fsym,M,"fignum",rate.*1e-9*M,"displayname",' Eye of AVG Signal');
|
||||
% Scpe_sig_avg.eye(fsym,M,"fignum",rate.*1e-9*M,"displayname",' Eye of AVG Signal');
|
||||
Scpe_sig = Scpe_sig_avg.normalize("mode","rms");
|
||||
end
|
||||
|
||||
% Preprocess signal
|
||||
Scpe_sig = preprocessSignal(Scpe_sig_avg, Symbols, fsym);
|
||||
% Scpe_sig = preprocessSignal(Scpe_sig_avg, Symbols, fsym);
|
||||
|
||||
Scpe_sig.eye(fsym,M,"fignum",M*10);
|
||||
|
||||
%% Eye of Preprocess signal
|
||||
|
||||
% Scpe_sig.eye(fsym,M,"fignum",M*10);
|
||||
|
||||
|
||||
|
||||
@@ -71,8 +72,67 @@ Scpe_sig.eye(fsym,M,"fignum",M*10);
|
||||
% 'legend style={font=\footnotesize}', ...
|
||||
% 'legend columns=1' ...
|
||||
% } );
|
||||
%% Simply FFE
|
||||
|
||||
%%
|
||||
len_tr = 4096*2;
|
||||
% combine and minimize repeated params
|
||||
mu_ffe = [0.0001,0.0008,0.001];
|
||||
mu_dd = 0.05;
|
||||
mu_dc = 0.005;
|
||||
|
||||
% single ffe_order used
|
||||
ffe_order = [50];
|
||||
|
||||
% map into p for FFE init (compact)
|
||||
p.epochs_tr = 5;
|
||||
p.epochs_dd = 5;
|
||||
p.len_tr = 4096*2;
|
||||
p.ffe_mu_tr = mu_ffe(1);
|
||||
p.ffe_mu_dd = mu_dd;
|
||||
p.ffe_order = ffe_order;
|
||||
p.eq_sps = 2;
|
||||
p.optimize_mus = true;
|
||||
p.dd_mode = true;
|
||||
p.ffe_adaption = 1;
|
||||
p.mu_dc = mu_dc;
|
||||
|
||||
% Initialize FFE with mapped parameters
|
||||
eq_ffe = FFE("epochs_tr", p.epochs_tr, ...
|
||||
"epochs_dd", p.epochs_dd, ...
|
||||
"len_tr", p.len_tr, ...
|
||||
"mu_dd", p.ffe_mu_dd, ...
|
||||
"mu_tr", p.ffe_mu_tr, ...
|
||||
"order", p.ffe_order(1), ...
|
||||
"sps", p.eq_sps, ...
|
||||
"decide", false, ...
|
||||
"optmize_mus", p.optimize_mus, ...
|
||||
"dd_mode", p.dd_mode, ...
|
||||
"adaption_technique", p.ffe_adaption, ...
|
||||
"dc_tracking_mu", p.mu_dc);
|
||||
|
||||
mu_ffe = [0.0001, 0.0008, 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
eq_ffe = EQ("Ne", [50,0,0], ...
|
||||
"Nb", [0,0,0], ...
|
||||
"training_length", p.len_tr, ...
|
||||
"training_loops", p.epochs_tr, ...
|
||||
"dd_loops", p.epochs_dd, ...
|
||||
"K", 2, ...
|
||||
"DCmu", p.mu_dc, ...
|
||||
"DDmu", [mu_ffe mu_dfe], ...
|
||||
"DFEmu", 0.005, ...
|
||||
"FFEmu", 0, ...
|
||||
"plotfinal", 0, ...
|
||||
"ideal_dfe", false);
|
||||
|
||||
[ffe_results, equalized_signal] = ffe(eq_ffe, M, Scpe_sig, Symbols, Tx_bits, ...
|
||||
"precode_mode", dbmode, ...
|
||||
"showAnalysis", 0, ...
|
||||
"postFFE", [], ...
|
||||
"eth_style_symbol_mapping", 0);
|
||||
|
||||
equalized_signal.eye(fsym,M,"fignum",M*2);
|
||||
%% Duobinary
|
||||
|
||||
if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
|
||||
trellexlusion = 1;
|
||||
@@ -102,178 +162,4 @@ dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
|
||||
'showAnalysis', 1,...
|
||||
"postFFE", []);
|
||||
|
||||
%% === FINAL FIGURE SIZE ===
|
||||
|
||||
% Existing figure numbers
|
||||
figEye = 249;
|
||||
figConst = 341;
|
||||
|
||||
% Find axes in the source figures
|
||||
srcAxEye = findobj(figEye, 'Type', 'axes');
|
||||
srcAxConst = findobj(figConst, 'Type', 'axes');
|
||||
|
||||
% Create new combined figure
|
||||
figCombined = figure;
|
||||
t = tiledlayout(figCombined, 1, 2);
|
||||
t.TileSpacing = 'compact';
|
||||
t.Padding = 'compact';
|
||||
|
||||
% ------------------------------------------------------------
|
||||
% LEFT TILE: EYE DIAGRAM
|
||||
% ------------------------------------------------------------
|
||||
ax1 = nexttile(t, 1);
|
||||
hold(ax1, 'on')
|
||||
|
||||
% Copy children (images, lines, patches, hist objects, etc.)
|
||||
copyobj(srcAxEye.Children, ax1);
|
||||
|
||||
% Copy labels and title
|
||||
ax1.XLabel.String = srcAxEye.XLabel.String;
|
||||
ax1.YLabel.String = srcAxEye.YLabel.String;
|
||||
ax1.Title.String = srcAxEye.Title.String;
|
||||
|
||||
% Copy axis limits
|
||||
ax1.XLim = srcAxEye.XLim;
|
||||
ax1.YLim = srcAxEye.YLim;
|
||||
ax1.YDir = srcAxEye.YDir;
|
||||
|
||||
% Copy ticks + labels EXACTLY (including remapped/scaled ones)
|
||||
ax1.XTick = srcAxEye.XTick;
|
||||
ax1.XTickLabel = srcAxEye.XTickLabel;
|
||||
ax1.YTick = srcAxEye.YTick;
|
||||
ax1.YTickLabel = srcAxEye.YTickLabel;
|
||||
|
||||
% Copy colormap + clim (important for density eye)
|
||||
colormap(ax1, colormap(srcAxEye.Parent));
|
||||
ax1.CLim = srcAxEye.CLim;
|
||||
|
||||
% Copy any style props that matter
|
||||
ax1.TickDir = srcAxEye.TickDir;
|
||||
ax1.TickLength = srcAxEye.TickLength;
|
||||
ax1.FontSize = srcAxEye.FontSize;
|
||||
ax1.Box = srcAxEye.Box;
|
||||
|
||||
grid(ax1,'on');
|
||||
|
||||
|
||||
% ------------------------------------------------------------
|
||||
% RIGHT TILE: CONSTELLATION HISTOGRAM
|
||||
% ------------------------------------------------------------
|
||||
ax2 = nexttile(t, 2);
|
||||
hold(ax2, 'on')
|
||||
|
||||
copyobj(srcAxConst.Children, ax2);
|
||||
|
||||
% Copy labels and title
|
||||
ax2.XLabel.String = srcAxConst.XLabel.String;
|
||||
ax2.YLabel.String = srcAxConst.YLabel.String;
|
||||
ax2.Title.String = srcAxConst.Title.String;
|
||||
|
||||
% The histogram uses the same y-axis as the eye
|
||||
% Extract mapping from eye
|
||||
rawTicks = ax1.YTick;
|
||||
rawLabelsCell = ax1.YTickLabel;
|
||||
trueVoltages = str2double(rawLabelsCell);
|
||||
|
||||
% Apply true voltages to the histogram axis
|
||||
ax2.XTick = flip(trueVoltages);
|
||||
ax2.XTickLabel = flip(rawLabelsCell);
|
||||
|
||||
% Set histogram y-limits to match the actual voltages
|
||||
ax2.XLim = [min(trueVoltages) max(trueVoltages)];
|
||||
|
||||
% Ensure eye diagram prints the same (we *do not* touch ax1.YLim)
|
||||
ax1.XTickLabel = rawLabelsCell;
|
||||
|
||||
|
||||
% Copy colormap (your histogram uses same palette)
|
||||
colormap(ax2, colormap(srcAxConst.Parent));
|
||||
|
||||
% Style properties
|
||||
ax2.TickDir = srcAxConst.TickDir;
|
||||
ax2.TickLength = srcAxConst.TickLength;
|
||||
ax2.FontSize = srcAxConst.FontSize;
|
||||
ax2.Box = srcAxConst.Box;
|
||||
|
||||
grid(ax2,'on');
|
||||
|
||||
% ============================================================
|
||||
% remove right y-axis completely
|
||||
% ============================================================
|
||||
ax2.XAxis.Visible = 'off'; % hides ticks + labels + axis line
|
||||
|
||||
% BUT we still keep the YTick positions internally for alignment:
|
||||
% ax2.YTick = <values already set earlier> ;
|
||||
|
||||
|
||||
% ============================================================
|
||||
% minimize distance between the two plots
|
||||
% ============================================================
|
||||
t.TileSpacing = 'none'; % no space between tiles
|
||||
t.Padding = 'none'; % no outer padding
|
||||
|
||||
% Also reduce internal padding for each axis
|
||||
ax1.Position(3) = ax1.Position(3) + 0.02; % widen eye a bit
|
||||
ax2.Position(1) = ax2.Position(1) - 0.02; % pull histogram closer
|
||||
|
||||
|
||||
% Keep left axis grid visible
|
||||
ax2.YGrid = 'off';
|
||||
|
||||
%
|
||||
% =====================================================================
|
||||
% FINAL POLISHING: unified visual style
|
||||
% =======================================================================
|
||||
|
||||
% --- unified font size ---
|
||||
FS = 12;
|
||||
set([ax1 ax2], 'FontSize', FS);
|
||||
|
||||
% --- unified axis line width (outline stroke thickness) ---
|
||||
LW = 1.0;
|
||||
set([ax1 ax2], 'LineWidth', LW);
|
||||
|
||||
% --- unified tick length ---
|
||||
TL = [.015 .015];
|
||||
set([ax1 ax2], 'TickLength', TL);
|
||||
|
||||
% --- unified grid style ---
|
||||
set([ax1 ax2], 'XGrid', 'on', 'YGrid', 'on');
|
||||
set([ax1 ax2], 'GridLineStyle', '--');
|
||||
set([ax1 ax2], 'GridAlpha', 0.2);
|
||||
|
||||
% --- remove right y-axis ticks and labels ---
|
||||
ax2.YAxis.Visible = 'off';
|
||||
|
||||
% --- copy colormap + CLim from the eye to histogram (synchronize look) ---
|
||||
colormap(ax1, colormap(srcAxEye.Parent));
|
||||
colormap(ax2, colormap(srcAxEye.Parent));
|
||||
ax2.CLim = ax1.CLim;
|
||||
|
||||
% --- minimal spacing between tiles ---
|
||||
t.TileSpacing = 'none';
|
||||
t.Padding = 'none';
|
||||
|
||||
|
||||
% --- pull the panels together (touching boundary effect) ---
|
||||
pos1 = ax1.Position;
|
||||
pos2 = ax2.Position;
|
||||
|
||||
% Shift histogram left until the outlines touch
|
||||
pos2(1) = pos1(1) + pos1(3) - 0.002; % 0.002 = fine overlap control
|
||||
ax2.Position = pos2;
|
||||
|
||||
% Expand histogram slightly, remove white band
|
||||
pos2 = ax2.Position;
|
||||
pos2(3) = pos2(3) + 0.01;
|
||||
ax2.Position = pos2;
|
||||
|
||||
% Ensure the left plot stays correct after the move
|
||||
ax1.Position = pos1;
|
||||
|
||||
% --- enforce same visible outline ---
|
||||
% For ax2, create a fake left spine (since YAxis is hidden)
|
||||
ax2.Box = 'on'; % keep outline but no ticks on the right
|
||||
ax1.Box = 'on';
|
||||
|
||||
ax2.View = [90 -90];
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
|
||||
dsp_options.max_occurences = 1;
|
||||
% database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
||||
dsp_options.database_type = "mysql";
|
||||
@@ -18,8 +18,8 @@ cols = cbrewer2('BuPu',25);
|
||||
cols = [cols(end-10:2:end,:)];
|
||||
cols = cbrewer2('Set1',6);
|
||||
|
||||
fignum = 200;
|
||||
fig=figure(fignum);clf;
|
||||
% fignum = 200;
|
||||
% fig=figure(fignum);clf;
|
||||
|
||||
for dbmode = 0%length(rates)
|
||||
|
||||
@@ -32,11 +32,11 @@ for dbmode = 0%length(rates)
|
||||
|
||||
Pamsource = PAMsource(...
|
||||
"fsym",fsym,"M",4,"order",18,"useprbs",0,...
|
||||
"fs_out",fdac,...
|
||||
"fs_out",256e9,...
|
||||
"applyclipping",0,"clipfactor",1.2,...
|
||||
"applypulseform",pulsef,"pulseformer",Pform,...
|
||||
"randkey",20,...
|
||||
"db_precode",dbmode,"db_encode",0,...
|
||||
"duobinary_mode",dbmode,...
|
||||
"mrds_code",0,"mrds_blocklength",512);
|
||||
|
||||
[Digi_sig,Symbols,Bits] = Pamsource.process();
|
||||
@@ -44,7 +44,7 @@ for dbmode = 0%length(rates)
|
||||
Digi_sig = Digi_sig.normalize("mode","rms");
|
||||
|
||||
%%% 1) PLOT FULL RESPONSE SIGNAL
|
||||
Digi_sig.spectrum("displayname","Full Response","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0.2,0.2,0.2],"linestyle",'-','addDCoffset',0,'normalizeToDC',1);
|
||||
Digi_sig.spectrum("displayname","Full Response","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",clr.Paired.dgreen,"linestyle",'-','addDCoffset',0,'normalizeToDC',1);
|
||||
|
||||
|
||||
%%% 2) PLOT PREEMPH. TX SIGNAL
|
||||
@@ -56,13 +56,26 @@ for dbmode = 0%length(rates)
|
||||
precomp_fn = "lab_high_speed";
|
||||
Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
|
||||
Digi_sig_pre = Digi_sig_pre.resample("fs_out",fdac);
|
||||
Digi_sig_pre = Digi_sig_pre.resample("fs_out",256e9);
|
||||
|
||||
Digi_sig_pre= Digi_sig_pre.normalize("mode","rms");
|
||||
|
||||
Digi_sig_pre.spectrum("displayname","Tx w/ pre-emph.","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",clr.Paired.dred,"linestyle",'-.','addDCoffset',0,'normalizeToDC',1);
|
||||
elseif dbmode == 2
|
||||
|
||||
maxamp = -38;
|
||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
|
||||
|
||||
precomp_path = "W:\labdata\sioe_labor\precomp";
|
||||
precomp_fn = "lab_high_speed";
|
||||
Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
|
||||
Digi_sig_pre = Digi_sig_pre.resample("fs_out",256e9);
|
||||
|
||||
Digi_sig_pre= Digi_sig_pre.normalize("mode","rms");
|
||||
|
||||
Digi_sig_pre.spectrum("displayname","Strong Precomp","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0,0,0],"linestyle",'-.','addDCoffset',0,'normalizeToDC',1);
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
% 1 - PAM 4 with preemphasis
|
||||
@@ -70,7 +83,7 @@ for dbmode = 0%length(rates)
|
||||
M = 4;
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'bitrate','EQUALS', rates);%360,390
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 10);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310); %1327.4
|
||||
fp.where('Runs', 'db_mode','EQUALS', dbmode);
|
||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||
@@ -89,14 +102,18 @@ for dbmode = 0%length(rates)
|
||||
Scpe_sig = Scpe_cell{1};
|
||||
|
||||
%%% 3) PLOT DB Tgt. SIGNAL
|
||||
if 1
|
||||
if dbmode ~= 2
|
||||
DB_Symbols = Duobinary().encode(Symbols);
|
||||
DB_Symbols.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'DB-Response','addDCoffset',0,'color',clr.Set1.blue,'normalizeToNyquist',0,'linestyle','--');
|
||||
else
|
||||
DB_Symbols = Symbols;
|
||||
end
|
||||
DB_Symbols.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'DB response','addDCoffset',0,'color',clr.Paired.dblue,'normalizeToNyquist',0,'linestyle','-');
|
||||
|
||||
|
||||
%%% 4) Plot RX Signal
|
||||
Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
|
||||
Scpe_sig.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'Rx','addDCoffset',1,'color',[0,0,0],'normalizeToNyquist',0,'linestyle',':');
|
||||
Scpe_sig.eye(fsym,M,"fignum",47,"displayname",' Eye of AVG Signal');
|
||||
% Scpe_sig.eye(fsym,M,"fignum",47,"displayname",' Eye of AVG Signal');
|
||||
% xline(Symbols.fs/2.*1e-9,'Color',cols(r,:),'HandleVisibility','off');
|
||||
|
||||
average_signals = 1;
|
||||
@@ -113,8 +130,8 @@ for dbmode = 0%length(rates)
|
||||
Symbols.spectrum("fignum",20,"normalizeTo0dB",1,"displayname",'Full Response','addDCoffset',0,'color',clr.Set1.red,'normalizeToNyquist',0,'linestyle','--');
|
||||
DB_Symbols.spectrum("fignum",20,"normalizeTo0dB",1,"displayname",'DB-Response','addDCoffset',0,'color',clr.Set1.blue,'normalizeToNyquist',0,'linestyle','--');
|
||||
Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1,"addDCoffset",5);
|
||||
Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
|
||||
Scpe_sig_avg.eye(fsym,M,"fignum",48,"displayname",' Eye of AVG Signal');
|
||||
% Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
|
||||
% Scpe_sig_avg.eye(fsym,M,"fignum",48,"displayname",' Eye of AVG Signal');
|
||||
end
|
||||
|
||||
|
||||
@@ -128,9 +145,9 @@ for dbmode = 0%length(rates)
|
||||
xticks(-100:20:100);
|
||||
yticks(-20:10:10);
|
||||
|
||||
beautifyBERplot("logscale",0,"setmarkers",0)
|
||||
pos = [100.3333 991.6667 358.0000 192.6667];
|
||||
set(fig, 'Position', pos);
|
||||
beautifyBERplot("logscale",0,"setmarkers",0,"setcolors",0,"changemarkers",0)
|
||||
% pos = [100.3333 991.6667 358.0000 192.6667];
|
||||
% set(fig, 'Position', pos);
|
||||
|
||||
%%%%%%%%%%%%
|
||||
drawnow;
|
||||
@@ -187,8 +204,8 @@ for dbmode = 0%length(rates)
|
||||
yticks(-50:10:10);
|
||||
|
||||
beautifyBERplot("logscale",0,"setmarkers",0)
|
||||
pos = [100.3333 991.6667 358.0000 192.6667];
|
||||
set(fig, 'Position', pos);
|
||||
% pos = [100.3333 991.6667 358.0000 192.6667];
|
||||
% set(fig, 'Position', pos);
|
||||
|
||||
end
|
||||
|
||||
|
||||
@@ -1,160 +0,0 @@
|
||||
%% ============================================================
|
||||
% PARAMETERS
|
||||
% ============================================================
|
||||
database_type = 'mysql';
|
||||
db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
|
||||
|
||||
pam_level = 4; % FIXED for this figure
|
||||
baudrates = [300e9 330e9 360e9 390e9];
|
||||
fiberL = 10;
|
||||
|
||||
fields = [
|
||||
db.getTableFieldNames('power_state_info');
|
||||
db.getTableFieldNames('dashboard_ungrouped_alltime')
|
||||
];
|
||||
|
||||
%% ============================================================
|
||||
% DEFINE DSP SCHEMES
|
||||
% ============================================================
|
||||
curves = struct;
|
||||
|
||||
curves(1).name = 'VNLE';
|
||||
curves(1).eq = equalizer_structure.vnle;
|
||||
curves(1).color = clr.Paired.red;
|
||||
|
||||
curves(2).name = 'PF + MLSE';
|
||||
curves(2).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(2).color = clr.Paired.green;
|
||||
|
||||
curves(3).name = 'DB-target + MLSE';
|
||||
curves(3).eq = equalizer_structure.vnle_db_mlse;
|
||||
curves(3).color = clr.Paired.blue;
|
||||
|
||||
curves(4).name = 'ML-based MLSE';
|
||||
curves(4).eq = equalizer_structure.ml_mlse;
|
||||
curves(4).color = clr.Paired.purple;
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% ANALYSIS — results(b, k): b = baudrate index, k = DSP scheme index
|
||||
% ============================================================
|
||||
results = struct;
|
||||
|
||||
for b = 1:length(baudrates)
|
||||
|
||||
Rb = baudrates(b);
|
||||
|
||||
% --- query matching runs ---
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','pam_level','EQUALS', pam_level);
|
||||
fp.where('Runs','fiber_length','EQUALS', fiberL);
|
||||
fp.where('Runs','bitrate','EQUALS', Rb);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
|
||||
[dataTable, ~] = db.queryDB(fp, fields);
|
||||
|
||||
for k = 1:numel(curves)
|
||||
|
||||
%% ---- DECIDE PRE-EMPH & PRECoded RULES for PAM-4 ----
|
||||
pre_emph = decide_preemph(pam_level, curves(k).eq);
|
||||
use_precoded = decide_precoded(pam_level, curves(k).eq);
|
||||
|
||||
%% ---- SETUP ANALYSIS CONFIG ----
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'wavelength';
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.agg = 'min';
|
||||
cfg.outlier = 'none';
|
||||
% cfg.group_by = {'wavelength'};
|
||||
cfg.show_raw = false;
|
||||
|
||||
cfg.filters = struct( ...
|
||||
'pam_level', pam_level, ...
|
||||
'is_mpi', 0, ...
|
||||
'bitrate', Rb, ...
|
||||
'fiber_length', fiberL, ...
|
||||
'equalizer_structure', curves(k).eq, ...
|
||||
'pre_emph', pre_emph);
|
||||
|
||||
%% ---- RUN ANALYSIS ----
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
results(b,k).wavelength = A.group{1}.x;
|
||||
|
||||
if use_precoded
|
||||
results(b,k).ber = A.group{1}.y_precoded;
|
||||
else
|
||||
results(b,k).ber = A.group{1}.y;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% PLOT — 1×4 (one tile per baudrate)
|
||||
% ============================================================
|
||||
fig = figure(); clf;
|
||||
tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
|
||||
|
||||
lw = 1.8;
|
||||
ms = 6;
|
||||
|
||||
for b = 1:length(baudrates)
|
||||
nexttile; hold on;
|
||||
|
||||
for k = 1:numel(curves)
|
||||
plot(results(b,k).wavelength, results(b,k).ber, ...
|
||||
'-o', ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerFaceColor', curves(k).color, ...
|
||||
'MarkerSize', ms, ...
|
||||
'LineWidth', lw, ...
|
||||
'DisplayName', curves(k).name);
|
||||
end
|
||||
|
||||
set(gca,'YScale','log');
|
||||
grid on;
|
||||
xlabel('Wavelength [nm]');
|
||||
ylabel('BER');
|
||||
ylim([1e-4 0.1]);
|
||||
title(sprintf('PAM-%d @ %.0f GBd',pam_level, baudrates(b)/1e9));
|
||||
legend('Location','best');
|
||||
beautifyBERplot();
|
||||
end
|
||||
|
||||
% Optional figure size
|
||||
pos = 1e3.*[0.1 0.55 1.4 0.32];
|
||||
set(fig, 'Position', pos);
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% DECISION LOGIC (INLINE FUNCTIONS)
|
||||
% ============================================================
|
||||
|
||||
function pe = decide_preemph(M, eq)
|
||||
% PRE-EMPH RULES:
|
||||
switch M
|
||||
case 4
|
||||
if eq == equalizer_structure.vnle
|
||||
pe = 1; % PAM4: VNLE → pre-emph on
|
||||
else
|
||||
pe = 0; % PAM4: all others → off
|
||||
end
|
||||
case {6,8}
|
||||
pe = 1; % PAM6/8: all → pre-emph on
|
||||
otherwise
|
||||
pe = 0;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function flag = decide_precoded(M, eq)
|
||||
% PRE-CODE RULES:
|
||||
if eq == equalizer_structure.vnle_db_mlse
|
||||
flag = 1; % Always for DB-target
|
||||
elseif eq == equalizer_structure.ml_mlse && M == 4
|
||||
flag = 1; % PAM4: ML-based → precoded
|
||||
else
|
||||
flag = 0;
|
||||
end
|
||||
end
|
||||
@@ -107,14 +107,14 @@ if 1
|
||||
fprintf('Plotting: %s\n', precomp_filename);
|
||||
freqresp.plot();
|
||||
|
||||
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\spectrum_2.tikz';
|
||||
matlab2tikz(outfile, ...
|
||||
'width','\fwidth', ...
|
||||
'height','\fheight', ...
|
||||
'showInfo',false, ...
|
||||
'extraAxisOptions',{ ...
|
||||
'legend style={font=\footnotesize}', ...
|
||||
'legend columns=1' ...
|
||||
});
|
||||
% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\spectrum_2.tikz';
|
||||
% matlab2tikz(outfile, ...
|
||||
% 'width','\fwidth', ...
|
||||
% 'height','\fheight', ...
|
||||
% 'showInfo',false, ...
|
||||
% 'extraAxisOptions',{ ...
|
||||
% 'legend style={font=\footnotesize}', ...
|
||||
% 'legend columns=1' ...
|
||||
% });
|
||||
|
||||
end
|
||||
|
||||
@@ -8,6 +8,7 @@ fp.where('power_state_info', 'pam_level','EQUALS', 4);
|
||||
fp.where('power_state_info', 'db_mode','EQUALS', 0);
|
||||
% fp.where('power_state_info', 'fiber_length','EQUALS', 1);
|
||||
fp.where('power_state_info', 'is_mpi','EQUALS', 0);
|
||||
fp.where('rop_attenuation', 'is_mpi','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('power_state_info');
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
@@ -60,7 +61,7 @@ for fl = 1:numel(fiber_len)
|
||||
|
||||
%
|
||||
dname = sprintf('%s; %d km',y_variable, fiber_len(fl));
|
||||
h2 = plot(fl_filtered_.(x_variable), fl_filtered_.(['mean_',y_variable]), 'LineWidth', 1, 'MarkerSize', 4,'Marker','o','LineStyle','-','Color',cols(fl,:),'MarkerFaceColor',cols(fl,:),'DisplayName',dname);
|
||||
h2 = scatter(fl_filtered_.(x_variable), fl_filtered_.(['mean_',y_variable]), 36, 'Marker','o', 'MarkerEdgeColor',cols(fl,:), 'MarkerFaceColor',cols(fl,:), 'DisplayName',dname);
|
||||
|
||||
h2.DataTipTemplate.DataTipRows(end+1) = ...
|
||||
dataTipTextRow('run\_id', run_ids);
|
||||
@@ -94,3 +95,28 @@ yline([4.85e-3, 2e-2],'--','LineWidth',1,'HandleVisibility','off');
|
||||
posH = get(f, 'Position'); % [left, bottom, width, height]
|
||||
newPos = [posH(1), posH(2), 750, 300];
|
||||
set(f, 'Position', newPos);
|
||||
|
||||
%% Average laser power over all measurements for each wavelength
|
||||
valid_rows = ~ismissing(dataTable.wavelength) & ...
|
||||
~ismissing(dataTable.power_laser);
|
||||
laser_measurements = dataTable(valid_rows, :);
|
||||
|
||||
laser_power_by_wavelength = groupsummary( ...
|
||||
laser_measurements, ...
|
||||
'wavelength', ...
|
||||
'mean', ...
|
||||
'power_mzm');
|
||||
laser_power_by_wavelength = sortrows( ...
|
||||
laser_power_by_wavelength, 'wavelength', 'ascend');
|
||||
|
||||
f_all = figure(4);
|
||||
clf(f_all);
|
||||
scatter(laser_power_by_wavelength.wavelength, ...
|
||||
laser_power_by_wavelength.mean_power_mzm, ...
|
||||
36, 'o', 'filled');
|
||||
grid on;
|
||||
xlabel('Wavelength in nm', 'FontSize', 12);
|
||||
ylabel('Average laser power in dB', 'FontSize', 12);
|
||||
title('Average laser power over all measurements', ...
|
||||
'FontSize', 14, 'FontWeight', 'bold');
|
||||
set(gca, 'FontSize', 11);
|
||||
@@ -1,5 +1,5 @@
|
||||
|
||||
filename = "Z:\2024\sioe\High Speed Messungen Oktober\bias_5km\PAMX_5km_20241025_204334_wh.mat";
|
||||
filename = "W:\labdata\sioe_labor\bias_5km\PAMX_5km_20241025_204334_wh.mat";
|
||||
|
||||
a = load(filename);
|
||||
wh = a.obj;
|
||||
@@ -30,7 +30,7 @@ for l = 1:numel(lambda_vals)
|
||||
for m = 1:numel(M_vals)
|
||||
|
||||
ber_ffe = wh.getStoValue('ber_ffe',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
ber = wh.getStoValue('ber_ffe',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
ber = wh.getStoValue('ber_collect',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
exfo = wh.getStoValue('exfo',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
|
||||
lb = wh.getStoValue('exfo',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
|
||||
158
projects/Diss/400G_revisit/FIGURE_EQ_NOISE_VS_BAUDRATE.m
Normal file
158
projects/Diss/400G_revisit/FIGURE_EQ_NOISE_VS_BAUDRATE.m
Normal file
@@ -0,0 +1,158 @@
|
||||
% Minimal all-time database plot: PAM-4 baudrate sweep versus VNLE noise.
|
||||
% The dashed curves are the Burg postfilter responses estimated by
|
||||
% VNLE + postfilter + MLSE.
|
||||
|
||||
%% SettingsC:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Advanced_DSP_for_400G_IMDD_experiments\Auswertung_JLT\final\FIGURE_EQ_NOISE_VS_BAUDRATE.m
|
||||
M = 4;
|
||||
bitrate = [390e9]; % [] -> all matching baudrates in the DB
|
||||
|
||||
dsp_options.storage_path = "W:\labdata\sioe_labor\";
|
||||
dsp_options.max_occurences = 1; % standard routine: first synchronized signal
|
||||
|
||||
fiber_length = 10;
|
||||
wavelength = 1310;
|
||||
is_mpi = 0;
|
||||
db_mode_filter = int32(db_mode.no_db);
|
||||
rop_attenuation = 0;
|
||||
|
||||
vnle_order = [50 5 5];
|
||||
dfe_order = [0 0 0];
|
||||
training_length = 4096*2;
|
||||
postfilter_order = 1;
|
||||
|
||||
%% Query the all-time database
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor_highspeed", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'pam_level', SqlOperator.EQUALS, M);
|
||||
fp.where('Runs', 'fiber_length', SqlOperator.EQUALS, fiber_length);
|
||||
fp.where('Runs', 'wavelength', SqlOperator.EQUALS, wavelength);
|
||||
fp.where('Runs', 'is_mpi', SqlOperator.EQUALS, is_mpi);
|
||||
fp.where('Runs', 'db_mode', 'LESS_THAN', 2);
|
||||
fp.where('Runs', 'rop_attenuation', SqlOperator.EQUALS, rop_attenuation);
|
||||
% fp.where('Runs', 'symbolrate','EQUALS', 180e9);
|
||||
% if ~isempty(baudrate_GBd)
|
||||
% fp.where('Runs', 'symbolrate', SqlOperator.IN, baudrate_GBd*1e9);
|
||||
% end
|
||||
|
||||
fields = [db.getTableFieldNames('dashboard_ungrouped_alltime')];
|
||||
fields = unique(fields, 'stable');
|
||||
[runs, ~] = db.queryDB(fp, fields);
|
||||
|
||||
if ~isempty(bitrate)
|
||||
runs = runs(ismember(runs.bitrate, bitrate), :);
|
||||
end
|
||||
|
||||
if isempty(runs)
|
||||
error('FIGURE_EQ_NOISE_VS_BAUDRATE:NoData', ...
|
||||
'No PAM-%d runs matched the selected baudrate range.', M);
|
||||
end
|
||||
|
||||
run_ids = unique(runs.run_id, 'stable');
|
||||
|
||||
%% One plot: VNLE residual noise and Burg tap response
|
||||
fig = figure(220);
|
||||
clf(fig);
|
||||
hold on;
|
||||
max_fs = 0;
|
||||
|
||||
for runIndex = 1:numel(run_ids)
|
||||
runData = queryRunid(run_ids(runIndex), db);
|
||||
fsym = double(runData.symbolrate(1));
|
||||
bitrate = double(runData.bitrate(1));
|
||||
|
||||
dsp_options.max_occurences = 20;
|
||||
dsp_options.start_occurence = 2;
|
||||
[Tx_bits, Symbols, Scpe_cell, found_sync] = loadAndSyncRunSignals(runData, dsp_options);
|
||||
if ~found_sync || isempty(Scpe_cell)
|
||||
warning('Skipping run %g: no synchronized signal found.', runData.run_id(1));
|
||||
continue
|
||||
end
|
||||
|
||||
useavg = 0;
|
||||
Scpe_sig = averageScopeSignals(Scpe_cell, useavg, 1);
|
||||
Scpe_sig = preprocessSignal(Scpe_sig, Symbols, fsym);
|
||||
Scpe_sig = Scpe_sig.normalize("mode", "rms");
|
||||
|
||||
% VNLE: retain the difference between equalizer output and reference.
|
||||
eq_vnle = EQ( ...
|
||||
"Ne", vnle_order, ...
|
||||
"Nb", dfe_order, ...
|
||||
"training_length", training_length, ...
|
||||
"training_loops", 5, ...
|
||||
"dd_loops", 5, ...
|
||||
"K", 2, ...
|
||||
"DCmu", 0.001, ...
|
||||
"DDmu", [0.0004 0.0004 0.0004 0.0004], ...
|
||||
"DFEmu", 0.05, ...
|
||||
"FFEmu", 0, ...
|
||||
"plotfinal", 0, ...
|
||||
"ideal_dfe", 1);
|
||||
|
||||
% eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
|
||||
% "mu_dd",1e-1,"mu_tr",0.4,"order",50, ...
|
||||
% "sps",2,"decide",0,"optmize_mus",1,"dd_mode",1, ...
|
||||
% "adaption_technique","nlms","dc_tracking_mu",1.021e-05);
|
||||
|
||||
|
||||
[equalized_signal, eq_noise] = eq_vnle.process(Scpe_sig, Symbols);
|
||||
|
||||
|
||||
postfilter_order = 3;
|
||||
pf = Postfilter("ncoeff", postfilter_order, "useBurg", 1);
|
||||
[mlse_sig_sd,whitened_noise] = pf.process(equalized_signal, eq_noise);
|
||||
|
||||
% mlse = MLSE( ...
|
||||
% "DIR", [0 0], ...
|
||||
% "duobinary_output", 0, ...
|
||||
% "M", M, ...
|
||||
% "trellis_states", PAMmapper(M, 0).levels);
|
||||
%
|
||||
% [results, ~] = vnle_postfilter_mlse( ...
|
||||
% eq_vnle, pf, mlse, M, Scpe_sig, Symbols, Tx_bits, ...
|
||||
% "precode_mode", db_mode.no_db, ...
|
||||
% "showAnalysis", 0, ...
|
||||
% "postFFE", [], ...
|
||||
% "eth_style_symbol_mapping", 0);
|
||||
|
||||
eq_noise = eq_noise - mean(eq_noise.signal);
|
||||
fig = figure(220+runIndex);
|
||||
showEQNoisePSD(eq_noise, ...
|
||||
"fignum", fig.Number, ...
|
||||
"displayname", sprintf('%.0f Gbps: VNLE Noise', bitrate*1e-9), ...
|
||||
"postfilter_taps", pf.coefficients, ...
|
||||
"colormode", "qualitative");
|
||||
whitened_noise = whitened_noise - mean(whitened_noise.signal);
|
||||
whitened_noise.spectrum("displayname", 'Whitened Noise', "fignum", fig.Number, "normalizeTo0dB", 0,"fft_length",4096,"normalizeToDC",0);
|
||||
|
||||
max_fs = max(max_fs, eq_noise.fs);
|
||||
end
|
||||
|
||||
xlabel('Frequency in GHz');
|
||||
ylabel('normalized to 0 dB');
|
||||
title('Noise of soft decision signal (not MLSE)');
|
||||
grid on;
|
||||
grid minor;
|
||||
legend('show', 'Interpreter', 'none', 'Location', 'best');
|
||||
xlim([-max_fs/2 max_fs/2]*1e-9);
|
||||
ylim([-20 0]);
|
||||
|
||||
function signalOut = averageScopeSignals(scopeCell, averageSignals, gain)
|
||||
signalOut = scopeCell{1};
|
||||
if ~averageSignals
|
||||
return
|
||||
end
|
||||
|
||||
commonLength = min(cellfun(@(s) numel(s.signal), scopeCell));
|
||||
scopeMean = zeros(commonLength, 1);
|
||||
for idx = 1:numel(scopeCell)
|
||||
scopeMean = scopeMean + scopeCell{idx}.signal(1:commonLength);
|
||||
end
|
||||
scopeMean = scopeMean ./ numel(scopeCell);
|
||||
signalOut.signal = gain .* scopeMean;
|
||||
end
|
||||
@@ -8,7 +8,7 @@ clear; clc;
|
||||
|
||||
%% 1) Query data
|
||||
|
||||
selectedPamLevel = 8;
|
||||
selectedPamLevels = [4, 6, 8];
|
||||
selectedFiberLengthKm = 10;
|
||||
selectedWavelengthNm = 1310;
|
||||
selectedRopAttenuation = 0; % set [] to use all ROP attenuation values
|
||||
@@ -33,7 +33,6 @@ db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', selectedFiberLengthKm);
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', selectedPamLevel);
|
||||
fp.where('Runs', 'wavelength', 'EQUALS', selectedWavelengthNm);
|
||||
if ~isempty(selectedRopAttenuation)
|
||||
fp.where('Runs', 'rop_attenuation', 'EQUALS', selectedRopAttenuation);
|
||||
@@ -66,6 +65,8 @@ for fieldIdx = 1:numel(numericFields)
|
||||
end
|
||||
end
|
||||
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
|
||||
if ~ismember("precomp_amp", string(data.Properties.VariableNames))
|
||||
warning("plot_best_algos:NoPrecompAmp", ...
|
||||
"Runs.precomp_amp was not returned. Falling back to pre_emphasis = (db_mode == 0).");
|
||||
@@ -85,6 +86,9 @@ if ismember("equalizer_structure", string(duobinaryRows.Properties.VariableNames
|
||||
equalizerMask(duobinaryRows.equalizer_structure, ...
|
||||
equalizer_structure.db_encoded), :);
|
||||
end
|
||||
|
||||
duobinaryRows = duobinaryRows(datetime(duobinaryRows.date_of_processing)>datetime("2026-01-01 00:00:00"),:);
|
||||
|
||||
duobinaryPlotData = buildDuobinarySignalingRows(duobinaryRows);
|
||||
duobinaryPlotData = duobinaryPlotData(isfinite(duobinaryPlotData.BER_plot) & ...
|
||||
duobinaryPlotData.BER_plot > 0 & ...
|
||||
@@ -117,12 +121,19 @@ if isempty(availableStyles)
|
||||
return
|
||||
end
|
||||
|
||||
fig = figure(); clf;
|
||||
ax = axes(fig); hold(ax, "on");
|
||||
fig = figure(432); clf;
|
||||
t = tiledlayout(fig, 1, numel(selectedPamLevels), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
for styleIdx = 1:height(availableStyles)
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
selectedPamLevel = selectedPamLevels(pamIdx);
|
||||
ax = nexttile(t); hold(ax, "on");
|
||||
pamMask = bestPlotData.pam_level == selectedPamLevel;
|
||||
|
||||
for styleIdx = 1:height(availableStyles)
|
||||
style = availableStyles(styleIdx, :);
|
||||
rowMask = bestPlotData.algorithm_key == style.algorithm_key;
|
||||
rowMask = pamMask & bestPlotData.algorithm_key == style.algorithm_key;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
@@ -151,36 +162,34 @@ for styleIdx = 1:height(availableStyles)
|
||||
"MarkerEdgeColor", style.color, ...
|
||||
"DisplayName", style.name);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
yline(ax, [2.2e-4, 4.85e-3, 2e-2], ...
|
||||
yline(ax, [2.2e-4, 4.85e-3, 2e-2], ...
|
||||
"LineWidth", 1, ...
|
||||
"LineStyle", "--", ...
|
||||
"Color", [0.25 0.25 0.25], ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
title(ax, sprintf("PAM-%d, %.0f km, %.0f nm", ...
|
||||
selectedPamLevel, selectedFiberLengthKm, selectedWavelengthNm));
|
||||
xlabel(ax, "Gross rate [Gb/s]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [1e-5, maxBerForPlot]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
% title(ax, sprintf("PAM-%d, %.0f km, %.0f nm", ...
|
||||
% selectedPamLevel, selectedFiberLengthKm, selectedWavelengthNm));
|
||||
xlabel(ax, "Gross rate [Gb/s]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [8e-5, 0.1]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
|
||||
xTicks = unique(bestPlotData.grossrate_Gbps(isfinite(bestPlotData.grossrate_Gbps)));
|
||||
if ~isempty(xTicks)
|
||||
xticks(ax, xTicks);
|
||||
xlim(ax, [min(xTicks), max(xTicks)]);
|
||||
end
|
||||
xticks(ax, 300:30:480);
|
||||
xlim(ax, [300, 480]);
|
||||
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
if exist("beautifyBERplot", "file")
|
||||
legend(ax, "Location", "northeast", "Interpreter", "none");
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, ...
|
||||
"setmarkers", false, "changemarkers", false);
|
||||
end
|
||||
end
|
||||
|
||||
set(fig, "Position", 1e3 .* [0.1000 0.5500 0.7200 0.4200]);
|
||||
set(fig, "Position", 1e3 .* [0.1070 0.5497 1.0585 0.2282]);
|
||||
|
||||
%% Local helpers
|
||||
|
||||
@@ -220,7 +229,7 @@ styles = table( ...
|
||||
"VNLE + PF + MLSE"; ...
|
||||
"VNLE DBt. + MLSE"; ...
|
||||
"ML pre-EQ + Viterbi"; ...
|
||||
"Duobinary signaling"], ...
|
||||
"DBS + VNLE + MLSE"], ...
|
||||
["o"; "square"; "diamond"; "^"; "v"], ...
|
||||
["-"; "-"; "-"; "-"; "-"], ...
|
||||
["w"; "w"; "w"; "w"; "w"], ...
|
||||
@@ -322,7 +331,7 @@ value = double(enumEntry);
|
||||
end
|
||||
|
||||
function bestData = bestBerByAlgorithmAndGrossRate(data)
|
||||
groupVars = ["algorithm_key", "grossrate_Gbps"];
|
||||
groupVars = ["pam_level", "algorithm_key", "grossrate_Gbps"];
|
||||
groupId = findgroups(data(:, groupVars));
|
||||
keepIdx = NaN(max(groupId), 1);
|
||||
|
||||
|
||||
@@ -8,7 +8,7 @@ clear; clc;
|
||||
%% 1) Gather data
|
||||
|
||||
selectedPamLevel = 4;
|
||||
selectedFiberLengthKm = 10;
|
||||
selectedFiberLengthKm = 2;
|
||||
selectedWavelength =1310; % set [] to use all wavelengths
|
||||
selectedRopAttenuation = []; % set [] to use all ROP attenuation values
|
||||
selectedIsMpi = []; % set [] to use all entries
|
||||
@@ -98,7 +98,7 @@ if isempty(availableEqStyles)
|
||||
return
|
||||
end
|
||||
|
||||
fig = figure(401); clf;
|
||||
fig = figure(); clf;
|
||||
tiledlayout(1, height(availableEqStyles), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
@@ -14,7 +14,9 @@ selectedWavelengthNm = 1310;
|
||||
selectedRopAttenuation = 0;
|
||||
selectedIsMpi = 0;
|
||||
selectedDbMode = db_mode.db_encoded;
|
||||
selectedEqualizerStructure = equalizer_structure.db_encoded;
|
||||
selectedEqualizerStructures = [ ...
|
||||
equalizer_structure.db_encoded, ...
|
||||
equalizer_structure.ml_mlse];
|
||||
|
||||
maxBerForPlot = 0.5;
|
||||
showRawEntries = false;
|
||||
@@ -60,8 +62,13 @@ end
|
||||
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
if ismember("equalizer_structure", string(data.Properties.VariableNames)) && ...
|
||||
~isempty(selectedEqualizerStructure)
|
||||
data = data(equalizerMask(data.equalizer_structure, selectedEqualizerStructure), :);
|
||||
~isempty(selectedEqualizerStructures)
|
||||
eqMask = false(height(data), 1);
|
||||
for eqIdx = 1:numel(selectedEqualizerStructures)
|
||||
eqMask = eqMask | equalizerMask(data.equalizer_structure, ...
|
||||
selectedEqualizerStructures(eqIdx));
|
||||
end
|
||||
data = data(eqMask, :);
|
||||
end
|
||||
|
||||
plotData = buildDetectionMetricRows(data);
|
||||
@@ -150,6 +157,9 @@ for pamIdx = 1:numel(availablePamLevels)
|
||||
xlim(ax, [min(xTicks), max(xTicks)]);
|
||||
end
|
||||
|
||||
xticks(ax, 300:30:480);
|
||||
xlim(ax, [300, 480]);
|
||||
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, ...
|
||||
@@ -182,31 +192,52 @@ values = double(values);
|
||||
end
|
||||
|
||||
function plotData = buildDetectionMetricRows(data)
|
||||
baseRows = data(isfinite(data.BER), :);
|
||||
dbEncodedRows = data(equalizerMask(data.equalizer_structure, ...
|
||||
equalizer_structure.db_encoded), :);
|
||||
baseRows = dbEncodedRows(isfinite(dbEncodedRows.BER), :);
|
||||
baseRows.detection_type = repmat("VNLE + MLSE", height(baseRows), 1);
|
||||
baseRows.BER_plot = baseRows.BER;
|
||||
|
||||
if ismember("BER_precoded", string(data.Properties.VariableNames))
|
||||
memorylessRows = data(isfinite(data.BER_precoded), :);
|
||||
memorylessRows = dbEncodedRows(isfinite(dbEncodedRows.BER_precoded), :);
|
||||
memorylessRows.detection_type = repmat("VNLE + memoryless", ...
|
||||
height(memorylessRows), 1);
|
||||
memorylessRows.BER_plot = memorylessRows.BER_precoded;
|
||||
plotData = [baseRows; memorylessRows];
|
||||
else
|
||||
warning("plot_duobinary_detection:NoPrecodedBer", ...
|
||||
"BER_precoded was not returned. Plotting only VNLE + MLSE rows.");
|
||||
plotData = baseRows;
|
||||
"BER_precoded was not returned. Plotting only BER rows.");
|
||||
memorylessRows = dbEncodedRows([], :);
|
||||
end
|
||||
|
||||
mlMlseRows = data(equalizerMask(data.equalizer_structure, ...
|
||||
equalizer_structure.ml_mlse), :);
|
||||
if ismember("BER_precoded", string(data.Properties.VariableNames))
|
||||
mlMlsePrecodedRows = mlMlseRows(isfinite(mlMlseRows.BER_precoded), :);
|
||||
mlMlsePrecodedRows.detection_type = repmat( ...
|
||||
"ML pre-EQ + Viterbi", ...
|
||||
height(mlMlsePrecodedRows), 1);
|
||||
mlMlsePrecodedRows.BER_plot = mlMlsePrecodedRows.BER_precoded;
|
||||
else
|
||||
mlMlsePrecodedRows = mlMlseRows([], :);
|
||||
end
|
||||
|
||||
plotData = [baseRows; memorylessRows; mlMlsePrecodedRows];
|
||||
end
|
||||
|
||||
function styles = defaultDetectionStyles()
|
||||
styles = table( ...
|
||||
["VNLE + MLSE"; "VNLE + memoryless"], ...
|
||||
["VNLE + MLSE"; "VNLE + memoryless"], ...
|
||||
["o"; "square"], ...
|
||||
["-"; "--"], ...
|
||||
["w"; "none"], ...
|
||||
[clr.Paired.blue; clr.Paired.orange], ...
|
||||
["VNLE + MLSE"; ...
|
||||
"VNLE + memoryless"; ...
|
||||
"ML pre-EQ + Viterbi"], ...
|
||||
["VNLE + MLSE"; ...
|
||||
"VNLE + memoryless"; ...
|
||||
"ML pre-EQ + Viterbi"], ...
|
||||
["o"; "square"; "^"], ...
|
||||
["-"; "--"; "-"], ...
|
||||
["w"; "none"; "w"], ...
|
||||
[clr.Paired.blue; ...
|
||||
clr.Paired.orange; ...
|
||||
clr.Paired.purple], ...
|
||||
'VariableNames', ["detection_type", "name", "marker", ...
|
||||
"lineStyle", "markerFaceColor", "color"]);
|
||||
end
|
||||
|
||||
384
projects/Diss/400G_revisit/PLOT_EYES_400G_REVISIT.m
Normal file
384
projects/Diss/400G_revisit/PLOT_EYES_400G_REVISIT.m
Normal file
@@ -0,0 +1,384 @@
|
||||
%% 400G revisit: eye comparison for PAM-4/6/8
|
||||
% The native Signal.eye() method is used for the eye display. Optional TikZ
|
||||
% export is configured below and uses mat2tikz_improved().
|
||||
%
|
||||
% db_mode_setting:
|
||||
% 0 - no duobinary processing; use the copied FFE configuration
|
||||
% 1 - DB-targeted VNLE; the VNLE target is Duobinary().encode(Symbols)
|
||||
% 2 - DB-encoded data; equalize the stored DB-encoded Symbols with a VNLE
|
||||
|
||||
clc;
|
||||
|
||||
%% Configuration
|
||||
M_values = [4];
|
||||
selectedBitrate = 360e9;
|
||||
selectedFiberLengthKm = 10;
|
||||
selectedWavelengthNm = 1310;
|
||||
selectedRopAttenuation = 0;
|
||||
selectedIsMpi = 0;
|
||||
db_mode_setting = 2; % Set to 0, 1, or 2.
|
||||
|
||||
average_signals = 1;
|
||||
show_histograms = true;
|
||||
export_tikz = 0;
|
||||
max_occurences = 10;
|
||||
histogram_figure_base = 600;
|
||||
eye_figure_base = 500;
|
||||
psd_figure_base = 700;
|
||||
|
||||
% PSD colors follow the established DSP algorithm styles. The target is
|
||||
% black and the received waveform is grey.
|
||||
dsp_styles = defaultAlgorithmStyles();
|
||||
psd_color_target = [0, 0, 0];
|
||||
psd_color_received = [0.3500, 0.3500, 0.3500];
|
||||
psd_color_equalized = dsp_styles.color(dsp_styles.algorithm_key == "vnle", :);
|
||||
|
||||
tikz_root = 'C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\04_Experimental_Evaluation\tikz\400g';
|
||||
tikz_eye_folder = fullfile(tikz_root, 'eyes');
|
||||
tikz_histogram_folder = fullfile(tikz_root, 'histograms');
|
||||
tikz_psd_folder = fullfile(tikz_root, 'psd');
|
||||
|
||||
% The averaging/gain/normalization follows FIGURE_EYES.m.
|
||||
average_gain = 1.25;
|
||||
|
||||
% Common equalizer parameters copied from FIGURE_EYES.m.
|
||||
len_tr = 4096*2;
|
||||
training_loops = 5;
|
||||
dd_loops = 5;
|
||||
eq_K = 2;
|
||||
mu_dc = 0.005;
|
||||
mu_ffe = [0.0001, 0.0008, 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
|
||||
if ~ismember(db_mode_setting, [0, 1, 2])
|
||||
error('PLOT_EYES_400G_REVISIT:InvalidDbMode', ...
|
||||
'db_mode_setting must be 0, 1, or 2.');
|
||||
end
|
||||
|
||||
if export_tikz
|
||||
if ~exist(tikz_eye_folder, 'dir')
|
||||
mkdir(tikz_eye_folder);
|
||||
end
|
||||
if ~exist(tikz_histogram_folder, 'dir')
|
||||
mkdir(tikz_histogram_folder);
|
||||
end
|
||||
if ~exist(tikz_psd_folder, 'dir')
|
||||
mkdir(tikz_psd_folder);
|
||||
end
|
||||
end
|
||||
|
||||
%% Database setup
|
||||
dsp_options = struct();
|
||||
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
|
||||
dsp_options.start_occurence = 1;
|
||||
dsp_options.max_occurences = max_occurences;
|
||||
|
||||
database = DBHandler( ...
|
||||
"dataBase", "labor_highspeed", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
|
||||
%% DSP phase: query, synchronize, equalize, and collect all signals
|
||||
selectedRuns = [];
|
||||
eyeSignals = cell(size(M_values));
|
||||
rawSignals = cell(size(M_values));
|
||||
eyeReferences = cell(size(M_values));
|
||||
symbolRates = NaN(size(M_values));
|
||||
|
||||
for mIdx = 1:numel(M_values)
|
||||
M = M_values(mIdx);
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', selectedFiberLengthKm);
|
||||
fp.where('Runs', 'wavelength', 'EQUALS', selectedWavelengthNm);
|
||||
fp.where('Runs', 'rop_attenuation', 'EQUALS', selectedRopAttenuation);
|
||||
fp.where('Runs', 'is_mpi', 'EQUALS', selectedIsMpi);
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', M);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', db_mode_setting);
|
||||
if ~isempty(selectedBitrate)
|
||||
fp.where('Runs', 'bitrate', 'EQUALS', selectedBitrate);
|
||||
end
|
||||
|
||||
[dataTable, ~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
|
||||
if isempty(dataTable)
|
||||
error('PLOT_EYES_400G_REVISIT:MissingRun', ...
|
||||
'No run found for PAM-%d with the selected filters.', M);
|
||||
end
|
||||
|
||||
dataTable = sortrows(dataTable, {'bitrate', 'run_id'});
|
||||
dataTable = dataTable(1, :);
|
||||
if isempty(selectedRuns)
|
||||
selectedRuns = dataTable;
|
||||
else
|
||||
selectedRuns = [selectedRuns; dataTable]; %#ok<AGROW>
|
||||
end
|
||||
|
||||
fsym = double(dataTable.symbolrate(1));
|
||||
symbolRates(mIdx) = fsym;
|
||||
[~, Symbols, Scpe_cell, found_sync] = ...
|
||||
loadAndSyncRunSignals(dataTable, dsp_options);
|
||||
|
||||
if ~found_sync || isempty(Scpe_cell)
|
||||
error('PLOT_EYES_400G_REVISIT:SynchronizationFailed', ...
|
||||
'Could not synchronize the selected PAM-%d run.', M);
|
||||
end
|
||||
|
||||
Scpe_sig = averageScopeSignals(Scpe_cell, average_signals, average_gain);
|
||||
Scpe_sig = preprocessSignal(Scpe_sig, Symbols, fsym);
|
||||
Scpe_sig = Scpe_sig.normalize("mode", "rms");
|
||||
|
||||
switch db_mode_setting
|
||||
case 0
|
||||
% Simply FFE, copied from FIGURE_EYES.m.
|
||||
eq_ = EQ("Ne", [50, 0, 0], ...
|
||||
"Nb", [0, 0, 0], ...
|
||||
"training_length", len_tr, ...
|
||||
"training_loops", training_loops, ...
|
||||
"dd_loops", dd_loops, ...
|
||||
"K", eq_K, ...
|
||||
"DCmu", mu_dc, ...
|
||||
"DDmu", [mu_ffe, mu_dfe], ...
|
||||
"DFEmu", 0.005, ...
|
||||
"FFEmu", 0, ...
|
||||
"plotfinal", 0, ...
|
||||
"ideal_dfe", false);
|
||||
referenceForEye = Symbols;
|
||||
|
||||
case 1
|
||||
% DB-targeted VNLE: train against the DB waveform generated
|
||||
% from the stored (precoded but not DB-encoded) symbols.
|
||||
eq_ = makeVnle(len_tr, training_loops, dd_loops, eq_K, ...
|
||||
mu_dc, mu_ffe, mu_dfe);
|
||||
referenceForEye = Duobinary().encode(Symbols, "M", M);
|
||||
|
||||
case 2
|
||||
% DB-encoded data: the stored Symbols already contain the DB
|
||||
% encoded reference waveform used by the VNLE.
|
||||
eq_ = makeVnle(len_tr, training_loops, dd_loops, eq_K, ...
|
||||
mu_dc, mu_ffe, mu_dfe);
|
||||
referenceForEye = Symbols;
|
||||
end
|
||||
|
||||
[equalized_signal, ~] = eq_.process(Scpe_sig, referenceForEye);
|
||||
eyeSignals{mIdx} = equalized_signal;
|
||||
rawSignals{mIdx} = Scpe_sig;
|
||||
eyeReferences{mIdx} = referenceForEye;
|
||||
|
||||
fprintf('PAM-%d: run %d, %.3f GBd, db_mode %d\n', ...
|
||||
M, dataTable.run_id(1), fsym*1e-9, db_mode_setting);
|
||||
end
|
||||
|
||||
|
||||
%% Plotting and export phase
|
||||
show_histograms = 1;
|
||||
showEye = 1;
|
||||
showPSD = 1;
|
||||
|
||||
for mIdx = 1:numel(M_values)
|
||||
M = M_values(mIdx);
|
||||
fsym = symbolRates(mIdx);
|
||||
rawSignal = rawSignals{mIdx};
|
||||
equalized_signal = eyeSignals{mIdx};
|
||||
referenceForEye = eyeReferences{mIdx};
|
||||
|
||||
if showPSD
|
||||
psdFigure = psd_figure_base + mIdx + db_mode_setting;
|
||||
figure(psdFigure); clf;
|
||||
|
||||
switch db_mode_setting
|
||||
case 0
|
||||
referencePsdName = sprintf('PAM-%d reference', M);
|
||||
equalizedPsdName = 'FFE output';
|
||||
psd_color_equalized = dsp_styles.color( ...
|
||||
dsp_styles.algorithm_key == "vnle", :);
|
||||
case 1
|
||||
referencePsdName = 'DB-target reference';
|
||||
equalizedPsdName = 'DB-targeted VNLE output';
|
||||
psd_color_equalized = dsp_styles.color( ...
|
||||
dsp_styles.algorithm_key == "vnle_db_mlse", :);
|
||||
case 2
|
||||
referencePsdName = 'DB-encoded reference';
|
||||
equalizedPsdName = 'DB-encoded VNLE output';
|
||||
psd_color_equalized = dsp_styles.color( ...
|
||||
dsp_styles.algorithm_key == "db_encoded", :);
|
||||
end
|
||||
|
||||
referenceForEye.spectrum( ...
|
||||
"fignum", psdFigure, ...
|
||||
"show_onesided", 1, ...
|
||||
"fft_length", 4096, ...
|
||||
"displayname", referencePsdName, ...
|
||||
"color", psd_color_target);
|
||||
rawSignal.spectrum( ...
|
||||
"fignum", psdFigure, ...
|
||||
"show_onesided", 1, ...
|
||||
"fft_length", 4096, ...
|
||||
"displayname", 'Received signal', ...
|
||||
"color", psd_color_received);
|
||||
equalized_signal.spectrum( ...
|
||||
"fignum", psdFigure, ...
|
||||
"show_onesided", 1, ...
|
||||
"fft_length", 4096, ...
|
||||
"displayname", equalizedPsdName, ...
|
||||
"color", psd_color_equalized);
|
||||
|
||||
eq_noise = equalized_signal - referenceForEye;
|
||||
showEQNoisePSD(eq_noise, ...
|
||||
"fignum", 800, ...
|
||||
"displayname", sprintf('%.0f Gbps: VNLE Noise', selectedBitrate), ...
|
||||
"colormode", "diverging");
|
||||
|
||||
% PSDs are line-only, irrespective of the marker styles used by
|
||||
% algorithm comparison plots.
|
||||
set(findall(gca, 'Type', 'Line'), 'Marker', 'none');
|
||||
|
||||
figure(psdFigure);
|
||||
title(sprintf('PSD comparison: %.0f GBd PAM-%d', fsym*1e-9, M), ...
|
||||
'Interpreter', 'none');
|
||||
legend('Location', 'best', 'Interpreter', 'none');
|
||||
grid on;
|
||||
box on;
|
||||
|
||||
if export_tikz
|
||||
baudrateLabel = sprintf('%.0fGBd', fsym*1e-9);
|
||||
psdFilename = fullfile(tikz_psd_folder, ...
|
||||
sprintf('psd_pam_%d_%s_db_mode_%d.tikz', ...
|
||||
M, baudrateLabel, db_mode_setting));
|
||||
mat2tikz_improved(psdFilename);
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
if showEye
|
||||
% Use the native Signal eye implementation for the displayed eye.
|
||||
eyeFigure = eye_figure_base + mIdx+ db_mode_setting;
|
||||
equalized_signal.eye(fsym, M, ...
|
||||
"fignum", eyeFigure, ...
|
||||
"displayname", sprintf('PAM-%d, db_mode = %d', M, db_mode_setting));
|
||||
figure(eyeFigure);
|
||||
set(gcf, "Position", [100 + 430*(mIdx - 1), 100, 400, 360]);
|
||||
stripEyeAxes(gca);
|
||||
|
||||
|
||||
if export_tikz
|
||||
baudrateLabel = sprintf('%.0fGBd', fsym*1e-9);
|
||||
eyeFilename = fullfile(tikz_eye_folder, ...
|
||||
sprintf('eye_pam_%d_%s_db_mode_%d.tikz', ...
|
||||
M, baudrateLabel, db_mode_setting));
|
||||
mat2tikz_improved(eyeFilename);
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
if show_histograms || export_tikz
|
||||
histogramFigure = histogram_figure_base + M + db_mode_setting;
|
||||
plotHistogram(equalized_signal, referenceForEye, ...
|
||||
M, histogramFigure, db_mode_setting, histogramLegendLabels(M));
|
||||
|
||||
if export_tikz
|
||||
figure(histogramFigure);
|
||||
histogramFilename = fullfile(tikz_histogram_folder, ...
|
||||
sprintf('histogram_pam_%d_%s_db_mode_%d.tikz', ...
|
||||
M, baudrateLabel, db_mode_setting));
|
||||
mat2tikz_improved(histogramFilename,"cleanfigure",1);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
%% Local functions
|
||||
function signalOut = averageScopeSignals(scopeCell, averageSignals, gain)
|
||||
signalOut = scopeCell{1};
|
||||
if ~averageSignals
|
||||
return
|
||||
end
|
||||
|
||||
commonLength = min(cellfun(@(s) numel(s.signal), scopeCell));
|
||||
scopeMean = zeros(commonLength, 1);
|
||||
for idx = 1:numel(scopeCell)
|
||||
scopeMean = scopeMean + scopeCell{idx}.signal(1:commonLength);
|
||||
end
|
||||
scopeMean = scopeMean ./ numel(scopeCell);
|
||||
signalOut.signal = gain .* scopeMean;
|
||||
end
|
||||
|
||||
function eq_ = makeVnle(lenTr, trainingLoops, ddLoops, eqK, muDc, muFfe, muDfe)
|
||||
eq_ = EQ("Ne", [50, 5, 5], ...
|
||||
"Nb", [0, 0, 0], ...
|
||||
"training_length", lenTr, ...
|
||||
"training_loops", trainingLoops, ...
|
||||
"dd_loops", ddLoops, ...
|
||||
"K", eqK, ...
|
||||
"DCmu", muDc, ...
|
||||
"DDmu", [muFfe, muDfe], ...
|
||||
"DFEmu", 0.005, ...
|
||||
"FFEmu", 0, ...
|
||||
"plotfinal", 0, ...
|
||||
"ideal_dfe", true);
|
||||
end
|
||||
|
||||
function plotHistogram(equalizedSignal, referenceForEye, M, figureNumber, ...
|
||||
dbModeSetting, legendLabels)
|
||||
figure(figureNumber); clf;
|
||||
if dbModeSetting ~= 0
|
||||
uncodedReference = Duobinary().decode(referenceForEye, "M", M);
|
||||
showLevelHistogram(equalizedSignal, referenceForEye, ...
|
||||
"fignum", figureNumber, ...
|
||||
"ref_symbol_uncoded", uncodedReference, ...
|
||||
"legendLabels", legendLabels);
|
||||
else
|
||||
showLevelHistogram(equalizedSignal, referenceForEye, ...
|
||||
"fignum", figureNumber, ...
|
||||
"legendLabels", legendLabels);
|
||||
end
|
||||
title(sprintf('PAM-%d histogram, db\_mode = %d', M, dbModeSetting));
|
||||
end
|
||||
|
||||
function labels = histogramLegendLabels(M)
|
||||
labels = arrayfun(@(idx) sprintf('$p(z|d=d_{%d})$', idx), ...
|
||||
1:M, 'UniformOutput', false);
|
||||
end
|
||||
|
||||
function stripEyeAxes(ax)
|
||||
title(ax, '');
|
||||
xlabel(ax, '');
|
||||
ylabel(ax, '');
|
||||
set(ax, ...
|
||||
'XTick', [], ...
|
||||
'YTick', [], ...
|
||||
'XTickLabel', [], ...
|
||||
'YTickLabel', [], ...
|
||||
'XMinorTick', 'off', ...
|
||||
'YMinorTick', 'off');
|
||||
grid(ax, 'off');
|
||||
end
|
||||
|
||||
function styles = defaultAlgorithmStyles()
|
||||
styles = table( ...
|
||||
["vnle"; ...
|
||||
"vnle_pf_mlse"; ...
|
||||
"vnle_db_mlse"; ...
|
||||
"ml_mlse"; ...
|
||||
"db_encoded"], ...
|
||||
[equalizer_structure.vnle; ...
|
||||
equalizer_structure.vnle_pf_mlse; ...
|
||||
equalizer_structure.vnle_db_mlse; ...
|
||||
equalizer_structure.ml_mlse; ...
|
||||
equalizer_structure.db_encoded], ...
|
||||
["VNLE"; ...
|
||||
"VNLE + PF + MLSE"; ...
|
||||
"VNLE DBt. + MLSE"; ...
|
||||
"ML pre-EQ + Viterbi"; ...
|
||||
"DBS + VNLE + MLSE"], ...
|
||||
["o"; "square"; "diamond"; "^"; "v"], ...
|
||||
["-"; "-"; "-"; "-"; "-"], ...
|
||||
["w"; "w"; "w"; "w"; "w"], ...
|
||||
[clr.Paired.red; ...
|
||||
clr.Paired.green; ...
|
||||
clr.Paired.blue; ...
|
||||
clr.Paired.purple; ...
|
||||
clr.Paired.orange], ...
|
||||
'VariableNames', ["algorithm_key", "eq", "name", "marker", ...
|
||||
"lineStyle", "markerFaceColor", "color"]);
|
||||
end
|
||||
@@ -0,0 +1,168 @@
|
||||
%% Memoryless DB-target BER versus baudrate for PAM4/6/8
|
||||
clear; clc;
|
||||
|
||||
warehouseFile = "C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\400G_revisit\results_duobinary_eq_memoryless_2km_pam468.mat";
|
||||
pamLevels = [4, 6, 8];
|
||||
|
||||
%% Load warehouse and matching run metadata
|
||||
S = load(warehouseFile, "wh");
|
||||
wh = S.wh;
|
||||
|
||||
db = DBHandler("dataBase", "labor_highspeed", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength', 'EQUALS', 1310);
|
||||
fp.where('Runs', 'rop_attenuation', 'EQUALS', 0);
|
||||
fp.where('Runs', 'is_mpi', 'EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', 0);
|
||||
|
||||
[runTable, ~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||
warehouseRunIds = warehouseIds(wh);
|
||||
runTable = runTable(ismember(double(runTable.run_id), warehouseRunIds), :);
|
||||
|
||||
%% Extract BER precoded from dbtgt_package
|
||||
warehouseRows = zeros(0, 3); % PAM level, baudrate [GBd], BER precoded
|
||||
for k = 1:numel(wh.sto.dbtgt_package)
|
||||
[phys, realizationResults] = wh.getPhysAndValueByLinIndex( ...
|
||||
"dbtgt_package", k);
|
||||
if ~isfield(phys, "run_id")
|
||||
continue
|
||||
end
|
||||
|
||||
runRow = find(double(runTable.run_id) == double(phys.run_id), 1);
|
||||
if isempty(runRow)
|
||||
continue
|
||||
end
|
||||
|
||||
for realization = 1:numel(realizationResults)
|
||||
package = realizationResults{realization};
|
||||
berPrecoded = readMetric(package, "BER_precoded");
|
||||
if isfinite(berPrecoded) && berPrecoded > 0
|
||||
warehouseRows(end+1, :) = [ ...
|
||||
double(runTable.pam_level(runRow)), ...
|
||||
double(runTable.grossrate(runRow)) * 1e-9, ...
|
||||
berPrecoded]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
warehouseCurve = minByBaudrate(warehouseRows, "BER_precoded");
|
||||
|
||||
%% Load database BER and BER precoded for VNLE + DB target/MLSE
|
||||
selectedFields = db.getTableFieldNames('dashboard_ungrouped_alltime');
|
||||
[databaseRows, ~] = db.queryDB(fp, selectedFields);
|
||||
|
||||
databaseRows = databaseRows(ismember(double(databaseRows.pam_level), pamLevels), :);
|
||||
databaseRows = databaseRows(equalizerMask( ...
|
||||
databaseRows.equalizer_structure, equalizer_structure.vnle_db_mlse), :);
|
||||
|
||||
berRows = databaseRows(isfinite(double(databaseRows.BER)) & ...
|
||||
double(databaseRows.BER) > 0, :);
|
||||
berPrecodedRows = databaseRows(isfinite(double(databaseRows.BER_precoded)) & ...
|
||||
double(databaseRows.BER_precoded) > 0, :);
|
||||
|
||||
databaseBerCurve = minByBaudrate( ...
|
||||
[double(berRows.pam_level), double(berRows.grossrate) * 1e-9, ...
|
||||
double(berRows.BER)], "BER");
|
||||
databaseBerPrecodedCurve = minByBaudrate( ...
|
||||
[double(berPrecodedRows.pam_level), ...
|
||||
double(berPrecodedRows.grossrate) * 1e-9, ...
|
||||
double(berPrecodedRows.BER_precoded)], "BER_precoded");
|
||||
|
||||
%% Plot: three curves per PAM format
|
||||
figure(470); clf;
|
||||
tiledlayout(1, 3, "TileSpacing", "compact", "Padding", "compact");
|
||||
|
||||
for pamLevel = pamLevels
|
||||
ax = nexttile; hold(ax, "on");
|
||||
|
||||
plotCurve(ax, warehouseCurve, pamLevel, "BER_precoded", ...
|
||||
"o-", "precode + memoryless");
|
||||
plotCurve(ax, databaseBerCurve, pamLevel, "BER", ...
|
||||
"s-", "DBt. + MLSE");
|
||||
plotCurve(ax, databaseBerPrecodedCurve, pamLevel, "BER_precoded", ...
|
||||
"s--", "precode + DBt. + MLSE");
|
||||
|
||||
ylim([1e-4, 1e-1]);
|
||||
title(ax, sprintf("PAM-%d", pamLevel));
|
||||
xlabel(ax, "Baudrate [GBd]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
%% Local helpers
|
||||
function runIds = warehouseIds(wh)
|
||||
runIds = zeros(0, 1);
|
||||
for k = 1:numel(wh.sto.dbtgt_package)
|
||||
[phys, ~] = wh.getPhysAndValueByLinIndex("dbtgt_package", k);
|
||||
if isfield(phys, "run_id")
|
||||
runIds(end+1, 1) = double(phys.run_id); %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
runIds = unique(runIds);
|
||||
end
|
||||
|
||||
function value = readMetric(package, metricName)
|
||||
value = NaN;
|
||||
if isempty(package) || ~isstruct(package) || ~isfield(package, "metrics")
|
||||
return
|
||||
end
|
||||
|
||||
metrics = package.metrics;
|
||||
if isstruct(metrics) && isfield(metrics, metricName)
|
||||
value = double(metrics.(metricName));
|
||||
elseif isobject(metrics) && isprop(metrics, metricName)
|
||||
value = double(metrics.(metricName));
|
||||
end
|
||||
end
|
||||
|
||||
function curve = minByBaudrate(rows, metricName)
|
||||
if isempty(rows)
|
||||
curve = table(zeros(0, 1), zeros(0, 1), ...
|
||||
'VariableNames', ["pam_level", "baudrate_GBd"]);
|
||||
curve.(metricName) = zeros(0, 1);
|
||||
return
|
||||
end
|
||||
|
||||
curve = table(rows(:, 1), rows(:, 2), rows(:, 3), ...
|
||||
'VariableNames', ["pam_level", "baudrate_GBd", metricName]);
|
||||
curve = groupsummary(curve, ["pam_level", "baudrate_GBd"], ...
|
||||
"min", metricName);
|
||||
curve.Properties.VariableNames(end) = metricName;
|
||||
end
|
||||
|
||||
function plotCurve(ax, curve, pamLevel, metricName, style, label)
|
||||
if isempty(curve) || ~ismember(metricName, string(curve.Properties.VariableNames))
|
||||
return
|
||||
end
|
||||
|
||||
rows = curve(curve.pam_level == pamLevel, :);
|
||||
if isempty(rows)
|
||||
return
|
||||
end
|
||||
|
||||
rows = sortrows(rows, "baudrate_GBd");
|
||||
plot(ax, rows.baudrate_GBd, rows.(metricName), style, ...
|
||||
"LineWidth", 1.3, "MarkerSize", 5, "DisplayName", label);
|
||||
end
|
||||
|
||||
function mask = equalizerMask(values, target)
|
||||
if isa(values, "equalizer_structure")
|
||||
mask = values == target;
|
||||
elseif isnumeric(values)
|
||||
mask = double(values) == double(target);
|
||||
else
|
||||
valuesString = string(values);
|
||||
mask = valuesString == string(target) | ...
|
||||
str2double(valuesString) == double(target);
|
||||
end
|
||||
mask = mask(:);
|
||||
end
|
||||
307
projects/Diss/400G_revisit/PLOT_MLSE_N_TAP_BER_BY_DB_MODE.m
Normal file
307
projects/Diss/400G_revisit/PLOT_MLSE_N_TAP_BER_BY_DB_MODE.m
Normal file
@@ -0,0 +1,307 @@
|
||||
%% Minimal BERp plot for MLSE postfilter orders and duobinary modes
|
||||
clear; clc;
|
||||
|
||||
%% Configuration
|
||||
warehouseFile = "C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\400G_revisit\mlse_n_tap_pam4.mat";
|
||||
pfValues = [1, 2, 3];
|
||||
|
||||
%% Load warehouse and query run metadata
|
||||
loadedData = load(warehouseFile, "wh");
|
||||
wh = loadedData.wh;
|
||||
wh.showInfo;
|
||||
|
||||
db = DBHandler("dataBase", "labor_highspeed", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
|
||||
warehouseRunIds = getWarehouseRunIds(wh, "mlse_package");
|
||||
runTable = queryRunsById(db, warehouseRunIds);
|
||||
if isempty(runTable)
|
||||
error("plot_mlse_n_tap:NoRunOverlap", ...
|
||||
"The DB returned no metadata for the warehouse run_ids %s.", ...
|
||||
mat2str(warehouseRunIds));
|
||||
end
|
||||
|
||||
%% Extract minimum BERp for every run_id and pf_ncoeffs
|
||||
plotData = warehouseToTable(wh, runTable, pfValues);
|
||||
hasBerp = isfinite(plotData.BERp) & plotData.BERp > 0;
|
||||
hasBer = isfinite(plotData.BER) & plotData.BER > 0;
|
||||
if ~any(hasBerp | hasBer)
|
||||
error("plot_mlse_n_tap:NoBerp", ...
|
||||
"No positive BER or BER_precoded values were found in mlse_package.");
|
||||
end
|
||||
|
||||
berpData = plotData(hasBerp, :);
|
||||
berpData = groupsummary(berpData, ...
|
||||
["db_mode", "pf_ncoeffs", "bitrate_Gbps"], "min", "BERp");
|
||||
berData = plotData(hasBer, :);
|
||||
berData = groupsummary(berData, ...
|
||||
["db_mode", "pf_ncoeffs", "bitrate_Gbps"], "min", "BER");
|
||||
|
||||
vnleData = vnleBerTable(wh, runTable);
|
||||
vnleBerData = vnleData(isfinite(vnleData.BER) & vnleData.BER > 0, :);
|
||||
if ~isempty(vnleBerData)
|
||||
vnleBerData = groupsummary(vnleBerData, ...
|
||||
["db_mode", "bitrate_Gbps"], "min", "BER");
|
||||
end
|
||||
vnleBerpData = vnleData(isfinite(vnleData.BERp) & vnleData.BERp > 0, :);
|
||||
if ~isempty(vnleBerpData)
|
||||
vnleBerpData = groupsummary(vnleBerpData, ...
|
||||
["db_mode", "bitrate_Gbps"], "min", "BERp");
|
||||
end
|
||||
|
||||
%% Plot db_mode = 0 and db_mode = 1 in separate panels
|
||||
fig = figure(461); clf;
|
||||
tiledlayout(1, 2, "TileSpacing", "compact", "Padding", "compact");
|
||||
markers = ["o", "square", "diamond"];
|
||||
|
||||
for dbMode = [1, 0]
|
||||
ax = nexttile; hold(ax, "on");
|
||||
colors = modeColors(dbMode);
|
||||
|
||||
for pfIdx = 1:numel(pfValues)
|
||||
pf = pfValues(pfIdx);
|
||||
berpMask = berpData.db_mode == dbMode & ...
|
||||
berpData.pf_ncoeffs == pf;
|
||||
if any(berpMask)
|
||||
curveData = sortrows(berpData(berpMask, :), "bitrate_Gbps");
|
||||
plot(ax, curveData.bitrate_Gbps, curveData.min_BERp, ...
|
||||
"LineStyle", "--", ...
|
||||
"LineWidth", 1.3, ...
|
||||
"Marker", markers(pfIdx), ...
|
||||
"MarkerSize", 5, ...
|
||||
"Color", colors(pfIdx, :), ...
|
||||
"DisplayName", sprintf("L = %d, precoded",pf));
|
||||
end
|
||||
|
||||
berMask = berData.db_mode == dbMode & ...
|
||||
berData.pf_ncoeffs == pf;
|
||||
if any(berMask)
|
||||
curveData = sortrows(berData(berMask, :), "bitrate_Gbps");
|
||||
plot(ax, curveData.bitrate_Gbps, curveData.min_BER, ...
|
||||
"LineStyle", "-", ...
|
||||
"LineWidth", 1.3, ...
|
||||
"Marker", markers(pfIdx), ...
|
||||
"MarkerSize", 5, ...
|
||||
"Color", colors(pfIdx, :), ...
|
||||
"DisplayName", sprintf("L = %d",pf));
|
||||
end
|
||||
end
|
||||
|
||||
vnleBerMask = vnleBerData.db_mode == dbMode;
|
||||
if any(vnleBerMask)
|
||||
curveData = sortrows(vnleBerData(vnleBerMask, :), "bitrate_Gbps");
|
||||
plot(ax, curveData.bitrate_Gbps, curveData.min_BER, ...
|
||||
"LineStyle", "-", ...
|
||||
"LineWidth", 1.5, ...
|
||||
"Color", clr.Set1.gray, ...
|
||||
"DisplayName", sprintf("VNLE"));
|
||||
end
|
||||
|
||||
vnleBerpMask = vnleBerpData.db_mode == dbMode;
|
||||
if any(vnleBerpMask)
|
||||
curveData = sortrows(vnleBerpData(vnleBerpMask, :), "bitrate_Gbps");
|
||||
plot(ax, curveData.bitrate_Gbps, curveData.min_BERp, ...
|
||||
"LineStyle", "--", ...
|
||||
"LineWidth", 1.5, ...
|
||||
"Color", clr.Set1.gray, ...
|
||||
"DisplayName", sprintf("VNLE precoded"));
|
||||
end
|
||||
|
||||
yline(ax, [2.2e-4, 4.85e-3, 2e-2], ...
|
||||
"LineStyle", "--", ...
|
||||
"LineWidth", 0.8, ...
|
||||
"Color", [0.25, 0.25, 0.25], ...
|
||||
"HandleVisibility", "off");
|
||||
title(ax, sprintf("db\\_mode = %d", dbMode));
|
||||
xlabel(ax, "Gross rate [Gb/s]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [3e-4, 0.1]);
|
||||
xlim([360, 410])
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
beautifyBERplot("setcolors",false,"setmarkers",false);
|
||||
end
|
||||
|
||||
set(fig, "Position", [100, 500, 1050, 380]);
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function colors = modeColors(dbMode)
|
||||
switch dbMode
|
||||
case 0
|
||||
colors = [clr.Paired.dred; clr.Paired.dblue; clr.Paired.dgreen];
|
||||
case 1
|
||||
colors = [clr.Paired.dred; clr.Paired.dblue; clr.Paired.dgreen];
|
||||
otherwise
|
||||
error("plot_mlse_n_tap:UnknownDbMode", ...
|
||||
"Unsupported db_mode: %d.", dbMode);
|
||||
end
|
||||
end
|
||||
|
||||
function runIds = getWarehouseRunIds(wh, storageName)
|
||||
runIds = zeros(0, 1);
|
||||
if ~isfield(wh.sto, storageName)
|
||||
return
|
||||
end
|
||||
|
||||
storageValues = wh.sto.(storageName);
|
||||
for linIdx = 1:numel(storageValues)
|
||||
[phys, ~] = wh.getPhysAndValueByLinIndex(storageName, linIdx);
|
||||
if isfield(phys, "run_id")
|
||||
runIds(end+1, 1) = double(phys.run_id); %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
runIds = unique(runIds);
|
||||
end
|
||||
|
||||
function runTable = queryRunsById(db, runIds)
|
||||
runTable = table();
|
||||
fields = db.getTableFieldNames('Runs');
|
||||
|
||||
for runIdx = 1:numel(runIds)
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'run_id', 'EQUALS', runIds(runIdx));
|
||||
[oneRun, ~] = db.queryDB(fp, fields);
|
||||
if isempty(oneRun)
|
||||
warning("plot_mlse_n_tap:MissingRunMetadata", ...
|
||||
"No DB metadata found for warehouse run_id %d.", runIds(runIdx));
|
||||
continue
|
||||
end
|
||||
|
||||
if isempty(runTable)
|
||||
runTable = oneRun;
|
||||
else
|
||||
runTable = [runTable; oneRun]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
|
||||
if ~isempty(runTable) && ismember("db_mode", string(runTable.Properties.VariableNames))
|
||||
runTable = runTable(double(runTable.db_mode) < 2, :);
|
||||
end
|
||||
end
|
||||
|
||||
function plotData = warehouseToTable(wh, runTable, pfValues)
|
||||
runIdCol = zeros(0, 1);
|
||||
dbModeCol = zeros(0, 1);
|
||||
pfCol = zeros(0, 1);
|
||||
bitrateCol = zeros(0, 1);
|
||||
berCol = zeros(0, 1);
|
||||
berpCol = zeros(0, 1);
|
||||
|
||||
storageValues = wh.sto.mlse_package;
|
||||
for linIdx = 1:numel(storageValues)
|
||||
[phys, storedValue] = wh.getPhysAndValueByLinIndex("mlse_package", linIdx);
|
||||
if ~isfield(phys, "run_id") || ~isfield(phys, "pf_ncoeffs")
|
||||
continue
|
||||
end
|
||||
|
||||
pf = double(phys.pf_ncoeffs);
|
||||
if ~ismember(pf, pfValues)
|
||||
continue
|
||||
end
|
||||
|
||||
runId = double(phys.run_id);
|
||||
rowIdx = find(double(runTable.run_id) == runId, 1, "first");
|
||||
if isempty(rowIdx)
|
||||
continue
|
||||
end
|
||||
|
||||
ber = minMetricValue(storedValue, "BER");
|
||||
berp = minMetricValue(storedValue, "BER_precoded");
|
||||
if (~isfinite(ber) || ber <= 0) && ...
|
||||
(~isfinite(berp) || berp <= 0)
|
||||
continue
|
||||
end
|
||||
|
||||
runIdCol(end+1, 1) = runId; %#ok<AGROW>
|
||||
dbModeCol(end+1, 1) = double(runTable.db_mode(rowIdx)); %#ok<AGROW>
|
||||
pfCol(end+1, 1) = pf; %#ok<AGROW>
|
||||
bitrateCol(end+1, 1) = double(runTable.bitrate(rowIdx)) .* 1e-9; %#ok<AGROW>
|
||||
berCol(end+1, 1) = ber; %#ok<AGROW>
|
||||
berpCol(end+1, 1) = berp; %#ok<AGROW>
|
||||
end
|
||||
|
||||
plotData = table(runIdCol, dbModeCol, pfCol, bitrateCol, berCol, berpCol, ...
|
||||
'VariableNames', ["run_id", "db_mode", "pf_ncoeffs", ...
|
||||
"bitrate_Gbps", "BER", "BERp"]);
|
||||
end
|
||||
|
||||
function plotData = vnleBerTable(wh, runTable)
|
||||
plotData = table(zeros(0, 1), zeros(0, 1), zeros(0, 1), zeros(0, 1), zeros(0, 1), ...
|
||||
'VariableNames', ["run_id", "db_mode", "bitrate_Gbps", "BER", "BERp"]);
|
||||
if ~isfield(wh.sto, "vnle_package")
|
||||
return
|
||||
end
|
||||
|
||||
runIdCol = zeros(0, 1);
|
||||
dbModeCol = zeros(0, 1);
|
||||
bitrateCol = zeros(0, 1);
|
||||
berCol = zeros(0, 1);
|
||||
berpCol = zeros(0, 1);
|
||||
storageValues = wh.sto.vnle_package;
|
||||
|
||||
for linIdx = 1:numel(storageValues)
|
||||
[phys, storedValue] = wh.getPhysAndValueByLinIndex("vnle_package", linIdx);
|
||||
if ~isfield(phys, "run_id")
|
||||
continue
|
||||
end
|
||||
|
||||
runId = double(phys.run_id);
|
||||
rowIdx = find(double(runTable.run_id) == runId, 1, "first");
|
||||
if isempty(rowIdx)
|
||||
continue
|
||||
end
|
||||
|
||||
ber = minMetricValue(storedValue, "BER");
|
||||
berp = minMetricValue(storedValue, "BER_precoded");
|
||||
if (~isfinite(ber) || ber <= 0) && ...
|
||||
(~isfinite(berp) || berp <= 0)
|
||||
continue
|
||||
end
|
||||
|
||||
runIdCol(end+1, 1) = runId; %#ok<AGROW>
|
||||
dbModeCol(end+1, 1) = double(runTable.db_mode(rowIdx)); %#ok<AGROW>
|
||||
bitrateCol(end+1, 1) = double(runTable.bitrate(rowIdx)) .* 1e-9; %#ok<AGROW>
|
||||
berCol(end+1, 1) = ber; %#ok<AGROW>
|
||||
berpCol(end+1, 1) = berp; %#ok<AGROW>
|
||||
end
|
||||
|
||||
plotData = table(runIdCol, dbModeCol, bitrateCol, berCol, berpCol, ...
|
||||
'VariableNames', ["run_id", "db_mode", "bitrate_Gbps", "BER", "BERp"]);
|
||||
end
|
||||
|
||||
function minValue = minMetricValue(value, metricName)
|
||||
minValue = NaN;
|
||||
if isempty(value)
|
||||
return
|
||||
end
|
||||
if ~iscell(value)
|
||||
value = {value};
|
||||
end
|
||||
|
||||
metricValues = NaN(1, numel(value));
|
||||
for idx = 1:numel(value)
|
||||
package = value{idx};
|
||||
if ~isstruct(package) || ~isfield(package, "metrics")
|
||||
continue
|
||||
end
|
||||
|
||||
metrics = package.metrics;
|
||||
fieldName = char(metricName);
|
||||
if isstruct(metrics) && isfield(metrics, fieldName)
|
||||
metricValues(idx) = metrics.(fieldName);
|
||||
elseif isobject(metrics) && isprop(metrics, fieldName)
|
||||
metricValues(idx) = metrics.(fieldName);
|
||||
end
|
||||
end
|
||||
|
||||
metricValues = metricValues(isfinite(metricValues) & metricValues > 0);
|
||||
if ~isempty(metricValues)
|
||||
minValue = min(metricValues);
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,407 @@
|
||||
%% Best NGMI, GMI, AIR and FEC rates over symbol rate
|
||||
% The normal algorithms are reduced to the best result per algorithm,
|
||||
% PAM format and symbol rate. Duobinary signaling is added as a separate
|
||||
% algorithm and is restricted to the post-2026 processing results.
|
||||
%
|
||||
% The figure has one column per PAM format and one row per metric:
|
||||
% BER, NGMI, GMI, AIR, NGMI-based SD+HD NDR, and the best BER-based NDR.
|
||||
% NDR values are calculated from the measured BER/NGMI using
|
||||
% TransmissionPerformance.
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Query data
|
||||
|
||||
selectedPamLevels = [4, 6, 8];
|
||||
selectedFiberLengthKm = 10;
|
||||
selectedWavelengthNm = 1310;
|
||||
selectedRopAttenuation = 0;
|
||||
selectedIsMpi = 0;
|
||||
|
||||
normalDbModes = [double(db_mode.no_db), double(db_mode.db_precoded)];
|
||||
duobinaryDbMode = double(db_mode.db_encoded);
|
||||
|
||||
showRawEntries = false;
|
||||
maxBerForPlot = 0.5;
|
||||
|
||||
algoStyles = defaultAlgorithmStyles();
|
||||
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor_highspeed", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', selectedFiberLengthKm);
|
||||
fp.where('Runs', 'wavelength', 'EQUALS', selectedWavelengthNm);
|
||||
fp.where('Runs', 'rop_attenuation', 'EQUALS', selectedRopAttenuation);
|
||||
fp.where('Runs', 'is_mpi', 'EQUALS', selectedIsMpi);
|
||||
|
||||
selectedFields = db.getTableFieldNames('dashboard_ungrouped_alltime');
|
||||
selectedFields = appendMissingFields(selectedFields, {'Runs.precomp_amp'});
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d rows.\n", height(rawData));
|
||||
|
||||
%% 2) Clean data and keep the requested algorithms
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["result_id", "run_id", "eq_id", "bitrate", "grossrate", ...
|
||||
"symbolrate", "pam_level", "wavelength", "fiber_length", "db_mode", ...
|
||||
"rop_attenuation", "precomp_amp", "is_mpi", "numBits", "numBitErr", ...
|
||||
"BER", "numBitErr_precoded", "BER_precoded", "GMI", "AIR", "NGMI"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
|
||||
normalRows = data(ismember(data.db_mode, normalDbModes), :);
|
||||
normalPlotData = buildNormalMetricRows(normalRows);
|
||||
|
||||
duobinaryRows = data(data.db_mode == duobinaryDbMode, :);
|
||||
if ismember("equalizer_structure", string(duobinaryRows.Properties.VariableNames))
|
||||
duobinaryRows = duobinaryRows( ...
|
||||
equalizerMask(duobinaryRows.equalizer_structure, ...
|
||||
equalizer_structure.db_encoded), :);
|
||||
end
|
||||
|
||||
% Duobinary results before this date are not comparable to the current set.
|
||||
if ismember("date_of_processing", string(duobinaryRows.Properties.VariableNames))
|
||||
duobinaryRows.date_of_processing = datetime(string(duobinaryRows.date_of_processing));
|
||||
duobinaryRows = duobinaryRows(datetime(duobinaryRows.date_of_processing)>datetime("2026-01-01 00:00:00"),:);
|
||||
else
|
||||
warning("plot_best_metrics:NoProcessingDate", ...
|
||||
"date_of_processing was not returned; no duobinary date filtering was applied.");
|
||||
end
|
||||
|
||||
duobinaryPlotData = buildDuobinaryMetricRows(duobinaryRows);
|
||||
plotData = [normalPlotData; duobinaryPlotData];
|
||||
if isempty(plotData)
|
||||
warning("plot_best_metrics:NoRows", ...
|
||||
"No rows remain after the database and PAM-format filters.");
|
||||
return
|
||||
end
|
||||
|
||||
plotData = addDerivedMetrics(plotData);
|
||||
plotData = plotData(plotData.symbolrate_GBd > 0 & ...
|
||||
isfinite(plotData.symbolrate_GBd), :);
|
||||
|
||||
fprintf("Remaining metric rows: %d\n", height(plotData));
|
||||
disp(groupcounts(plotData, ["algorithm_key", "pam_level", "precode"]));
|
||||
|
||||
%% 3) Calculate BER-/NGMI-dependent net rates
|
||||
|
||||
tp = TransmissionPerformance;
|
||||
grossRate = double(plotData.grossrate);
|
||||
ngmi = double(plotData.NGMI);
|
||||
ber = double(plotData.BER_plot);
|
||||
|
||||
ngmi(~isfinite(ngmi) | ngmi < 0 | ngmi > 1.05) = NaN;
|
||||
ber(~isfinite(ber) | ber <= 0 | ber > maxBerForPlot) = NaN;
|
||||
|
||||
ndr = tp.calculateNetRate(grossRate, "NGMI", ngmi, "BER", ber);
|
||||
plotData.NDR_SDHD = columnVector(ndr.SDHD.NetRate) .* 1e-9;
|
||||
|
||||
berBasedRates = [ ...
|
||||
columnVector(ndr.STAIR.NetRate); ...
|
||||
columnVector(ndr.HD.NetRate); ...
|
||||
columnVector(ndr.KP4.NetRate); ...
|
||||
columnVector(ndr.KP4_hamming.NetRate); ...
|
||||
columnVector(ndr.O_FEC.NetRate)];
|
||||
plotData.NDR_BER_BEST = max(reshape(berBasedRates, height(plotData), []), [], 2, "omitnan") .* 1e-9;
|
||||
|
||||
%% 4) Keep the best row for every plotted metric and group
|
||||
|
||||
metricDefinitions = struct( ...
|
||||
"field", {"BER_plot", "NGMI", "GMI", "AIR_Gbps", "NDR_SDHD", "NDR_BER_BEST"}, ...
|
||||
"label", {"BER", "NGMI", "GMI [bit/sym]", "AIR [Gb/s]", ...
|
||||
"SD+HD NDR [Gb/s]", "Best BER-FEC NDR [Gb/s]"}, ...
|
||||
"scale", {"log", "linear", "linear", "linear", "linear", "linear"});
|
||||
|
||||
bestMetricData = cell(numel(metricDefinitions), 1);
|
||||
for metricIdx = 1:numel(metricDefinitions)
|
||||
bestMetricData{metricIdx} = bestMetricRows(plotData, ...
|
||||
metricDefinitions(metricIdx).field);
|
||||
end
|
||||
|
||||
availableStyles = algoStyles(hasAlgorithmRows(plotData, algoStyles), :);
|
||||
if isempty(availableStyles)
|
||||
warning("plot_best_metrics:NoSelectedAlgorithms", ...
|
||||
"None of the configured algorithm styles match the queried rows.");
|
||||
return
|
||||
end
|
||||
|
||||
%% 5) Plot one figure: metric rows x PAM columns
|
||||
|
||||
fig = figure(433); clf;
|
||||
t = tiledlayout(fig, numel(metricDefinitions), numel(selectedPamLevels), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
for metricIdx = 1:numel(metricDefinitions)
|
||||
metric = metricDefinitions(metricIdx);
|
||||
metricData = bestMetricData{metricIdx};
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
selectedPamLevel = selectedPamLevels(pamIdx);
|
||||
ax = nexttile(t); hold(ax, "on");
|
||||
pamMask = metricData.pam_level == selectedPamLevel;
|
||||
|
||||
for styleIdx = 1:height(availableStyles)
|
||||
style = availableStyles(styleIdx, :);
|
||||
rowMask = pamMask & metricData.algorithm_key == style.algorithm_key;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
algoData = sortrows(metricData(rowMask, :), "symbolrate_GBd");
|
||||
y = algoData.(metric.field);
|
||||
valid = isfinite(y);
|
||||
if ~any(valid)
|
||||
continue
|
||||
end
|
||||
|
||||
if showRawEntries
|
||||
scatter(ax, algoData.symbolrate_GBd(valid), y(valid), ...
|
||||
9, ...
|
||||
"Marker", ".", ...
|
||||
"MarkerEdgeColor", style.color, ...
|
||||
"MarkerFaceColor", style.color, ...
|
||||
"MarkerEdgeAlpha", 0.25, ...
|
||||
"MarkerFaceAlpha", 0.25, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
|
||||
plot(ax, algoData.symbolrate_GBd(valid), y(valid), ...
|
||||
"LineStyle", style.lineStyle, ...
|
||||
"Marker", style.marker, ...
|
||||
"MarkerSize", 4, ...
|
||||
"LineWidth", 1.35, ...
|
||||
"Color", style.color, ...
|
||||
"MarkerFaceColor", style.markerFaceColor, ...
|
||||
"MarkerEdgeColor", style.color, ...
|
||||
"DisplayName", style.name);
|
||||
end
|
||||
|
||||
formatMetricAxis(ax, metric, selectedPamLevel);
|
||||
|
||||
if metricIdx == 1 && pamIdx == 1
|
||||
legend(ax, "Location", "southwest", "Interpreter", "none");
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
title(t, sprintf("Best information metrics, %.0f km, %.0f nm", ...
|
||||
selectedFiberLengthKm, selectedWavelengthNm));
|
||||
set(fig, "Position", 1e3 .* [0.08 0.04 1.18 0.86]);
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function fields = appendMissingFields(fields, extraFields)
|
||||
fields = cellstr(fields);
|
||||
extraFields = cellstr(extraFields);
|
||||
for idx = 1:numel(extraFields)
|
||||
if ~any(strcmp(fields, extraFields{idx}))
|
||||
fields{end+1, 1} = extraFields{idx}; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function values = columnVector(values)
|
||||
values = double(values(:));
|
||||
end
|
||||
|
||||
function styles = defaultAlgorithmStyles()
|
||||
styles = table( ...
|
||||
["vnle"; "vnle_pf_mlse"; "vnle_db_mlse"; "ml_mlse"; "db_encoded"], ...
|
||||
["VNLE"; "VNLE + PF + MLSE"; "VNLE DBt. + MLSE"; ...
|
||||
"ML pre-EQ + Viterbi"; "DBS + VNLE + MLSE"], ...
|
||||
["o"; "square"; "diamond"; "^"; "v"], ...
|
||||
["-"; "-"; "-"; "-"; "-"], ...
|
||||
["w"; "w"; "w"; "w"; "w"], ...
|
||||
[clr.Paired.red; clr.Paired.green; clr.Paired.blue; ...
|
||||
clr.Paired.purple; clr.Paired.orange], ...
|
||||
'VariableNames', ["algorithm_key", "name", "marker", ...
|
||||
"lineStyle", "markerFaceColor", "color"]);
|
||||
end
|
||||
|
||||
function plotData = buildNormalMetricRows(data)
|
||||
baseRows = data(isfinite(data.BER), :);
|
||||
baseRows.precode = false(height(baseRows), 1);
|
||||
baseRows.BER_plot = baseRows.BER;
|
||||
baseRows.algorithm_key = algorithmKeyFromEqualizer(baseRows.equalizer_structure);
|
||||
baseRows = baseRows(baseRows.algorithm_key ~= "", :);
|
||||
|
||||
if ismember("BER_precoded", string(data.Properties.VariableNames))
|
||||
precodedRows = data(isfinite(data.BER_precoded), :);
|
||||
precodedRows.precode = true(height(precodedRows), 1);
|
||||
precodedRows.BER_plot = precodedRows.BER_precoded;
|
||||
precodedRows.algorithm_key = algorithmKeyFromEqualizer( ...
|
||||
precodedRows.equalizer_structure);
|
||||
precodedRows = precodedRows(precodedRows.algorithm_key ~= "", :);
|
||||
plotData = [baseRows; precodedRows];
|
||||
else
|
||||
warning("plot_best_metrics:NoPrecodedBer", ...
|
||||
"BER_precoded was not returned; plotting only BER rows.");
|
||||
plotData = baseRows;
|
||||
end
|
||||
end
|
||||
|
||||
function plotData = buildDuobinaryMetricRows(data)
|
||||
plotData = data(isfinite(data.BER), :);
|
||||
plotData.precode = false(height(plotData), 1);
|
||||
plotData.BER_plot = plotData.BER;
|
||||
plotData.algorithm_key = repmat("db_encoded", height(plotData), 1);
|
||||
end
|
||||
|
||||
function plotData = addDerivedMetrics(plotData)
|
||||
plotData.symbolrate_GBd = double(plotData.symbolrate) .* 1e-9;
|
||||
plotData.grossrate_Gbps = double(plotData.grossrate) .* 1e-9;
|
||||
|
||||
% AIR is stored in bit/s. Reconstruct it from GMI when the stored value is
|
||||
% missing or outside the physically meaningful range.
|
||||
plotData.AIR_Gbps = numericOrNaN(plotData, "AIR") .* 1e-9;
|
||||
gmi = numericOrNaN(plotData, "GMI");
|
||||
symbolrate = double(plotData.symbolrate);
|
||||
grossrate = double(plotData.grossrate);
|
||||
fallbackAir = gmi .* symbolrate .* 1e-9;
|
||||
useFallback = ~isfinite(plotData.AIR_Gbps) | plotData.AIR_Gbps < 0 | ...
|
||||
(isfinite(grossrate) & plotData.AIR_Gbps > grossrate .* 1.05e-9);
|
||||
plotData.AIR_Gbps(useFallback) = fallbackAir(useFallback);
|
||||
|
||||
plotData.GMI = gmi;
|
||||
plotData.NGMI = numericOrNaN(plotData, "NGMI");
|
||||
end
|
||||
|
||||
function values = numericOrNaN(data, fieldName)
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
values = numericColumn(data.(char(fieldName)));
|
||||
else
|
||||
values = NaN(height(data), 1);
|
||||
end
|
||||
values = values(:);
|
||||
end
|
||||
|
||||
function algorithmKey = algorithmKeyFromEqualizer(equalizerColumn)
|
||||
if isnumeric(equalizerColumn) || islogical(equalizerColumn)
|
||||
eqNumeric = double(equalizerColumn);
|
||||
algorithmKey = strings(size(eqNumeric));
|
||||
knownKeys = ["vnle", "vnle_pf_mlse", "vnle_db_mlse", "ml_mlse"];
|
||||
knownValues = [double(equalizer_structure.vnle), ...
|
||||
double(equalizer_structure.vnle_pf_mlse), ...
|
||||
double(equalizer_structure.vnle_db_mlse), ...
|
||||
double(equalizer_structure.ml_mlse)];
|
||||
for idx = 1:numel(knownKeys)
|
||||
algorithmKey(eqNumeric == knownValues(idx)) = knownKeys(idx);
|
||||
end
|
||||
return
|
||||
end
|
||||
|
||||
algorithmKey = lower(string(equalizerColumn));
|
||||
algorithmKey(~ismember(algorithmKey, ...
|
||||
["vnle", "vnle_pf_mlse", "vnle_db_mlse", "ml_mlse"])) = "";
|
||||
end
|
||||
|
||||
function mask = equalizerMask(equalizerColumn, eqValue)
|
||||
if isnumeric(equalizerColumn) || islogical(equalizerColumn)
|
||||
mask = double(equalizerColumn) == double(eqValue);
|
||||
else
|
||||
mask = lower(string(equalizerColumn)) == lower(string(eqValue));
|
||||
end
|
||||
end
|
||||
|
||||
function bestData = bestMetricRows(data, metricField)
|
||||
valid = isfinite(data.(metricField));
|
||||
if strcmp(metricField, "BER_plot")
|
||||
valid = valid & data.(metricField) > 0;
|
||||
elseif strcmp(metricField, "NGMI")
|
||||
valid = valid & data.(metricField) >= 0 & data.(metricField) <= 1.05;
|
||||
else
|
||||
valid = valid & data.(metricField) >= 0;
|
||||
end
|
||||
|
||||
candidateData = data(valid, :);
|
||||
if isempty(candidateData)
|
||||
bestData = candidateData;
|
||||
return
|
||||
end
|
||||
|
||||
groupVars = ["pam_level", "algorithm_key", "symbolrate_GBd"];
|
||||
groupId = findgroups(candidateData(:, groupVars));
|
||||
keepIdx = zeros(max(groupId), 1);
|
||||
for curGroup = 1:max(groupId)
|
||||
rowIdx = find(groupId == curGroup);
|
||||
values = candidateData.(metricField)(rowIdx);
|
||||
if strcmp(metricField, "BER_plot")
|
||||
[~, localIdx] = min(values);
|
||||
else
|
||||
[~, localIdx] = max(values);
|
||||
end
|
||||
keepIdx(curGroup) = rowIdx(localIdx(1));
|
||||
end
|
||||
bestData = sortrows(candidateData(keepIdx, :), groupVars);
|
||||
end
|
||||
|
||||
function keep = hasAlgorithmRows(data, algoStyles)
|
||||
keep = false(height(algoStyles), 1);
|
||||
for idx = 1:height(algoStyles)
|
||||
keep(idx) = any(data.algorithm_key == algoStyles.algorithm_key(idx));
|
||||
end
|
||||
end
|
||||
|
||||
function formatMetricAxis(ax, metric, pamLevel)
|
||||
set(ax, "FontSize", 8, "TickLabelInterpreter", "none");
|
||||
xlabel(ax, "Symbol rate [GBd]");
|
||||
ylabel(ax, metric.label);
|
||||
xlim(ax, [95 245]);
|
||||
xticks(ax, 100:20:240);
|
||||
grid(ax, "on");
|
||||
grid(ax, "minor");
|
||||
box(ax, "on");
|
||||
|
||||
switch metric.field
|
||||
case "BER_plot"
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [1e-5 0.2]);
|
||||
yline(ax, [2.2e-4 4.85e-3 2e-2], ...
|
||||
"LineWidth", 0.8, "LineStyle", ":", ...
|
||||
"Color", [0.25 0.25 0.25], "HandleVisibility", "off");
|
||||
case "NGMI"
|
||||
ylim(ax, [0.45 1.02]);
|
||||
case "GMI"
|
||||
set(ax, "YScale", "linear");
|
||||
ylim(ax, [0 max(3.2, log2(pamLevel) + 0.15)]);
|
||||
otherwise
|
||||
set(ax, "YScale", "linear");
|
||||
ylim(ax, [0 500]);
|
||||
yline(ax, 400, "LineWidth", 0.8, "LineStyle", "--", ...
|
||||
"Color", [0.25 0.25 0.25], "HandleVisibility", "off");
|
||||
end
|
||||
|
||||
if pamLevel == 4
|
||||
title(ax, "PAM-4");
|
||||
elseif pamLevel == 6
|
||||
title(ax, "PAM-6");
|
||||
elseif pamLevel == 8
|
||||
title(ax, "PAM-8");
|
||||
else
|
||||
title(ax, sprintf("PAM-%d", pamLevel));
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,471 @@
|
||||
%% Analysis plots for the reprocessed dsp_400g_recipe warehouse
|
||||
% Source warehouse is produced by RUN_REPROCESS_BAUDRATE_FILES_DSP400G.m.
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Configuration and warehouse loading
|
||||
|
||||
warehouseFile = "D:\baudrate_sweep_b2b\PAMX_b2b_baudrate20241024_210648_dsp400g_reprocessed_wh.mat";
|
||||
|
||||
selectedRopAttenForBaudrate = 0;
|
||||
selectedEqForRopCurves = "mlse"; % "ffe", "mlse", or "db"
|
||||
acquisitionAggregation = "min"; % "min", "median", or "mean"
|
||||
fecThreshold = 1e-2;
|
||||
polyfitOrderMax = 4;
|
||||
showRateAsDatarate = false;
|
||||
showRawRopMarkers = true;
|
||||
showPolynomialFits = true;
|
||||
|
||||
loadedData = load(warehouseFile);
|
||||
if isfield(loadedData, "wh")
|
||||
wh = loadedData.wh;
|
||||
elseif isfield(loadedData, "obj")
|
||||
wh = loadedData.obj;
|
||||
else
|
||||
error("plot_reprocessed_wh:NoWarehouse", ...
|
||||
"Warehouse file must contain a variable named wh or obj.");
|
||||
end
|
||||
|
||||
wh.showInfo;
|
||||
|
||||
eqStyles = defaultEqStyles();
|
||||
availableEqStyles = eqStyles(hasWarehouseStorage(wh, eqStyles.storage), :);
|
||||
if isempty(availableEqStyles)
|
||||
error("plot_reprocessed_wh:NoEqStorage", ...
|
||||
"None of the configured BER storages are present in wh.sto.");
|
||||
end
|
||||
|
||||
rawData = warehouseToTable(wh, availableEqStyles);
|
||||
rawData = rawData(isfinite(rawData.ber) & rawData.ber > 0, :);
|
||||
allData = aggregateAcquisitions(rawData, acquisitionAggregation);
|
||||
[allData.rop_axis, ropAxisLabel] = deriveRopAxis(allData);
|
||||
|
||||
fprintf("Loaded %d finite acquisition BER rows from warehouse.\n", height(rawData));
|
||||
fprintf("Aggregated to %d grouped BER rows using acquisitionAggregation = %s.\n", ...
|
||||
height(allData), acquisitionAggregation);
|
||||
disp(groupcounts(allData, ["M", "eq"]));
|
||||
|
||||
pamVals = sort(unique(allData.M).');
|
||||
fsymVals = sort(unique(allData.fsym).');
|
||||
ropAttenVals = sort(unique(allData.rop_atten).');
|
||||
ropAxisVals = sort(unique(allData.rop_axis(isfinite(allData.rop_axis))).');
|
||||
|
||||
%% 2) BER versus baud rate at fixed ROP attenuation
|
||||
|
||||
fig = figure(450); clf;
|
||||
tiledlayout(1, numel(pamVals), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
ax = nexttile; hold(ax, "on");
|
||||
|
||||
for eqIdx = 1:height(availableEqStyles)
|
||||
eqStyle = availableEqStyles(eqIdx, :);
|
||||
rowMask = allData.M == pamLevel & ...
|
||||
allData.eq == eqStyle.eq & ...
|
||||
allData.rop_atten == selectedRopAttenForBaudrate;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curData = sortrows(allData(rowMask, :), "fsym_GBd");
|
||||
plot(ax, curData.fsym_GBd, curData.ber, ...
|
||||
"LineStyle", eqStyle.lineStyle, ...
|
||||
"Marker", eqStyle.marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.4, ...
|
||||
"Color", eqStyle.color, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", eqStyle.color, ...
|
||||
"DisplayName", eqStyle.name);
|
||||
end
|
||||
|
||||
plotFecLines(ax, fecThreshold);
|
||||
|
||||
title(ax, sprintf("PAM-%d, ROP atten. %.1f dB", ...
|
||||
pamLevel, selectedRopAttenForBaudrate));
|
||||
xlabel(ax, "Symbol rate [GBd]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [1e-5, 0.5]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
applyBerStyle();
|
||||
end
|
||||
|
||||
set(fig, "Position", 1e3 .* [0.1000 0.5500 1.4113 0.3200]);
|
||||
|
||||
%% 3) ROP attenuation curves for one EQ scheme
|
||||
|
||||
selectedRopEqStyle = availableEqStyles(availableEqStyles.eq == selectedEqForRopCurves, :);
|
||||
if isempty(selectedRopEqStyle)
|
||||
error("plot_reprocessed_wh:MissingSelectedEq", ...
|
||||
"selectedEqForRopCurves = %s is not available in this warehouse.", ...
|
||||
selectedEqForRopCurves);
|
||||
end
|
||||
|
||||
fig = figure(451); clf;
|
||||
tiledlayout(1, numel(pamVals), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
rateColors = rateColorMap(numel(fsymVals));
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
ax = nexttile; hold(ax, "on");
|
||||
|
||||
for fsymIdx = 1:numel(fsymVals)
|
||||
fsym = fsymVals(fsymIdx);
|
||||
rowMask = allData.M == pamLevel & ...
|
||||
allData.eq == selectedRopEqStyle.eq & ...
|
||||
allData.fsym == fsym;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curData = sortrows(allData(rowMask, :), "rop_axis");
|
||||
curveColor = rateColors(fsymIdx, :);
|
||||
displayName = sprintf("%.0f GBd", fsym .* 1e-9);
|
||||
|
||||
if showRawRopMarkers
|
||||
plot(ax, curData.rop_axis, curData.ber, ...
|
||||
"LineStyle", "none", ...
|
||||
"Marker", "o", ...
|
||||
"MarkerSize", 3, ...
|
||||
"LineWidth", 0.8, ...
|
||||
"Color", curveColor, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", curveColor, ...
|
||||
"DisplayName", displayName);
|
||||
end
|
||||
|
||||
if showPolynomialFits
|
||||
[xFit, yFit] = fitLogBerCurve(curData.rop_axis, ...
|
||||
curData.ber, polyfitOrderMax);
|
||||
if ~isempty(xFit)
|
||||
plot(ax, xFit, yFit, ...
|
||||
"LineStyle", "-", ...
|
||||
"LineWidth", 1.1, ...
|
||||
"Color", curveColor, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
title(ax, sprintf("PAM-%d, %s", pamLevel, selectedRopEqStyle.name));
|
||||
xlabel(ax, ropAxisLabel);
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [1e-5, 0.5]);
|
||||
xlim(ax, [min(ropAxisVals), max(ropAxisVals)]);
|
||||
plotFecLines(ax, fecThreshold);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
applyBerStyle();
|
||||
end
|
||||
|
||||
set(fig, "Position", 1e3 .* [0.1000 0.5500 1.4113 0.3200]);
|
||||
|
||||
%% 4) Required ROP attenuation at FEC threshold versus baud rate
|
||||
|
||||
fecData = computeFecCrossings(allData, availableEqStyles, fecThreshold, ...
|
||||
polyfitOrderMax);
|
||||
if isempty(fecData)
|
||||
warning("plot_reprocessed_wh:NoFecCrossings", ...
|
||||
"No FEC crossings were found for threshold %.3g.", fecThreshold);
|
||||
else
|
||||
fig = figure(452); clf;
|
||||
tiledlayout(1, numel(pamVals), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
ax = nexttile; hold(ax, "on");
|
||||
|
||||
for eqIdx = 1:height(availableEqStyles)
|
||||
eqStyle = availableEqStyles(eqIdx, :);
|
||||
rowMask = fecData.M == pamLevel & fecData.eq == eqStyle.eq;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curData = sortrows(fecData(rowMask, :), "fsym_GBd");
|
||||
if showRateAsDatarate
|
||||
xData = curData.datarate_Gbps;
|
||||
xLabelText = "Datarate [Gb/s]";
|
||||
else
|
||||
xData = curData.fsym_GBd;
|
||||
xLabelText = "Symbol rate [GBd]";
|
||||
end
|
||||
|
||||
plot(ax, xData, curData.required_rop_axis, ...
|
||||
"LineStyle", eqStyle.lineStyle, ...
|
||||
"Marker", eqStyle.marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.4, ...
|
||||
"Color", eqStyle.color, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", eqStyle.color, ...
|
||||
"DisplayName", eqStyle.name);
|
||||
end
|
||||
|
||||
title(ax, sprintf("PAM-%d, BER = %.2g", pamLevel, fecThreshold));
|
||||
xlabel(ax, xLabelText);
|
||||
ylabel(ax, "Required " + ropAxisLabel);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
set(fig, "Position", 1e3 .* [0.1000 0.5500 1.4113 0.3200]);
|
||||
end
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function styles = defaultEqStyles()
|
||||
styles = table( ...
|
||||
["ffe"; "mlse"; "db"], ...
|
||||
["ber_ffe"; "ber_mlse"; "ber_db"], ...
|
||||
["FFE"; "VNLE + PF + MLSE"; "VNLE DBt. + MLSE"], ...
|
||||
["o"; "square"; "diamond"], ...
|
||||
["-"; "-"; "-"], ...
|
||||
[clr.Paired.red; clr.Paired.green; clr.Paired.blue], ...
|
||||
'VariableNames', ["eq", "storage", "name", "marker", ...
|
||||
"lineStyle", "color"]);
|
||||
end
|
||||
|
||||
function keep = hasWarehouseStorage(wh, storageNames)
|
||||
stoFields = string(fieldnames(wh.sto));
|
||||
keep = ismember(storageNames, stoFields);
|
||||
end
|
||||
|
||||
function data = warehouseToTable(wh, eqStyles)
|
||||
lastIdx = wh.getLastLinIndice();
|
||||
rows = cell(lastIdx * height(eqStyles), 11);
|
||||
rowIdx = 0;
|
||||
|
||||
for eqIdx = 1:height(eqStyles)
|
||||
eqStyle = eqStyles(eqIdx, :);
|
||||
for linIdx = 1:lastIdx
|
||||
[phys, value] = wh.getPhysAndValueByLinIndex(eqStyle.storage, linIdx);
|
||||
if isempty(value) || ~isnumeric(value) || ~isscalar(value)
|
||||
continue
|
||||
end
|
||||
|
||||
fsym = double(phys.fsym);
|
||||
pamLevel = double(phys.M);
|
||||
ropAtten = double(phys.rop_atten);
|
||||
acquisitionIdx = double(phys.acquisition_idx);
|
||||
ropValue = getOptionalStoredScalarByLinIndex(wh, "rop", linIdx);
|
||||
pdInValue = getOptionalStoredScalarByLinIndex(wh, "pd_in", linIdx);
|
||||
|
||||
rowIdx = rowIdx + 1;
|
||||
rows(rowIdx, :) = { ...
|
||||
fsym, ...
|
||||
fsym .* 1e-9, ...
|
||||
ropAtten, ...
|
||||
pamLevel, ...
|
||||
acquisitionIdx, ...
|
||||
eqStyle.eq, ...
|
||||
eqStyle.storage, ...
|
||||
double(value), ...
|
||||
ropValue, ...
|
||||
pdInValue, ...
|
||||
fsym .* floor(log2(pamLevel) * 10) / 10 .* 1e-9};
|
||||
end
|
||||
end
|
||||
|
||||
rows = rows(1:rowIdx, :);
|
||||
data = cell2table(rows, 'VariableNames', ...
|
||||
["fsym", "fsym_GBd", "rop_atten", "M", "acquisition_idx", ...
|
||||
"eq", "storage", "ber", "rop_dBm", "pd_in_dBm", "datarate_Gbps"]);
|
||||
data.eq = string(data.eq);
|
||||
data.storage = string(data.storage);
|
||||
end
|
||||
|
||||
function data = aggregateAcquisitions(rawData, aggregationMode)
|
||||
groupVars = ["fsym", "fsym_GBd", "rop_atten", "M", "eq", ...
|
||||
"storage", "rop_dBm", "pd_in_dBm", "datarate_Gbps"];
|
||||
|
||||
switch aggregationMode
|
||||
case "min"
|
||||
data = groupsummary(rawData, groupVars, "min", "ber");
|
||||
data.ber = data.min_ber;
|
||||
data = removevars(data, "min_ber");
|
||||
case "median"
|
||||
data = groupsummary(rawData, groupVars, "median", "ber");
|
||||
data.ber = data.median_ber;
|
||||
data = removevars(data, "median_ber");
|
||||
case "mean"
|
||||
data = groupsummary(rawData, groupVars, "mean", "ber");
|
||||
data.ber = data.mean_ber;
|
||||
data = removevars(data, "mean_ber");
|
||||
otherwise
|
||||
error("plot_reprocessed_wh:UnknownAggregation", ...
|
||||
"Unknown acquisitionAggregation: %s", aggregationMode);
|
||||
end
|
||||
end
|
||||
|
||||
function value = getOptionalStoredScalarByLinIndex(wh, storageName, linIdx)
|
||||
value = NaN;
|
||||
if ~isfield(wh.sto, storageName)
|
||||
return
|
||||
end
|
||||
|
||||
storedValue = wh.sto.(storageName){linIdx};
|
||||
if isnumeric(storedValue) && isscalar(storedValue)
|
||||
value = double(storedValue);
|
||||
end
|
||||
end
|
||||
|
||||
function [ropAxis, ropAxisLabel] = deriveRopAxis(data)
|
||||
if ismember("rop_dBm", string(data.Properties.VariableNames)) && ...
|
||||
any(isfinite(data.rop_dBm))
|
||||
ropAxis = data.rop_dBm;
|
||||
ropAxisLabel = "ROP [dBm]";
|
||||
elseif ismember("pd_in_dBm", string(data.Properties.VariableNames)) && ...
|
||||
any(isfinite(data.pd_in_dBm))
|
||||
ropAxis = data.pd_in_dBm;
|
||||
ropAxisLabel = "PD input power [dBm]";
|
||||
else
|
||||
ropAxis = data.rop_atten;
|
||||
ropAxisLabel = "ROP attenuation [dB]";
|
||||
end
|
||||
end
|
||||
|
||||
function cmap = rateColorMap(numColors)
|
||||
anchors = [ ...
|
||||
clr.Paired.lightblue; ...
|
||||
clr.Paired.blue; ...
|
||||
clr.Paired.green; ...
|
||||
clr.Paired.orange; ...
|
||||
clr.Paired.red; ...
|
||||
clr.Paired.purple];
|
||||
if numColors <= size(anchors, 1)
|
||||
cmap = anchors(1:numColors, :);
|
||||
return
|
||||
end
|
||||
|
||||
xAnchor = linspace(0, 1, size(anchors, 1));
|
||||
xQuery = linspace(0, 1, numColors);
|
||||
cmap = interp1(xAnchor, anchors, xQuery, "linear");
|
||||
end
|
||||
|
||||
function [xFit, yFit] = fitLogBerCurve(x, y, maxOrder)
|
||||
valid = isfinite(x) & isfinite(y) & y > 0;
|
||||
x = x(valid);
|
||||
y = y(valid);
|
||||
|
||||
if numel(unique(x)) < 2
|
||||
xFit = [];
|
||||
yFit = [];
|
||||
return
|
||||
end
|
||||
|
||||
[x, orderIdx] = sort(x(:));
|
||||
y = y(orderIdx);
|
||||
fitOrder = min(maxOrder, numel(unique(x)) - 1);
|
||||
coeff = polyfit(x, log10(y), fitOrder);
|
||||
xFit = linspace(min(x), max(x), 300).';
|
||||
yFit = 10 .^ polyval(coeff, xFit);
|
||||
end
|
||||
|
||||
function fecData = computeFecCrossings(data, eqStyles, fecThreshold, maxOrder)
|
||||
pamVals = unique(data.M).';
|
||||
fsymVals = unique(data.fsym).';
|
||||
rows = cell(height(eqStyles) * numel(pamVals) * numel(fsymVals), 6);
|
||||
rowIdx = 0;
|
||||
|
||||
for eqIdx = 1:height(eqStyles)
|
||||
eqStyle = eqStyles(eqIdx, :);
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
for fsymIdx = 1:numel(fsymVals)
|
||||
fsym = fsymVals(fsymIdx);
|
||||
rowMask = data.eq == eqStyle.eq & ...
|
||||
data.M == pamLevel & ...
|
||||
data.fsym == fsym;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curData = sortrows(data(rowMask, :), "rop_axis");
|
||||
requiredRopAxis = fecCrossingFromCurve( ...
|
||||
curData.rop_axis, curData.ber, fecThreshold, maxOrder);
|
||||
if ~isfinite(requiredRopAxis)
|
||||
continue
|
||||
end
|
||||
|
||||
rowIdx = rowIdx + 1;
|
||||
rows(rowIdx, :) = { ...
|
||||
fsym, ...
|
||||
fsym .* 1e-9, ...
|
||||
pamLevel, ...
|
||||
eqStyle.eq, ...
|
||||
requiredRopAxis, ...
|
||||
fsym .* floor(log2(pamLevel) * 10) / 10 .* 1e-9};
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
if rowIdx == 0
|
||||
fecData = table();
|
||||
else
|
||||
rows = rows(1:rowIdx, :);
|
||||
fecData = cell2table(rows, 'VariableNames', ...
|
||||
["fsym", "fsym_GBd", "M", "eq", "required_rop_axis", ...
|
||||
"datarate_Gbps"]);
|
||||
fecData.eq = string(fecData.eq);
|
||||
end
|
||||
end
|
||||
|
||||
function requiredRopAtten = fecCrossingFromCurve( ...
|
||||
ropAtten, ber, fecThreshold, maxOrder)
|
||||
[xFit, yFit] = fitLogBerCurve(ropAtten, ber, maxOrder);
|
||||
requiredRopAtten = NaN;
|
||||
|
||||
if isempty(xFit)
|
||||
return
|
||||
end
|
||||
|
||||
crossingMask = isfinite(yFit) & yFit > 0;
|
||||
xFit = xFit(crossingMask);
|
||||
yFit = yFit(crossingMask);
|
||||
if numel(xFit) < 2
|
||||
return
|
||||
end
|
||||
|
||||
delta = log10(yFit) - log10(fecThreshold);
|
||||
crossingIdx = find(delta(1:end-1) .* delta(2:end) <= 0, 1, "first");
|
||||
if isempty(crossingIdx)
|
||||
return
|
||||
end
|
||||
|
||||
x1 = xFit(crossingIdx);
|
||||
x2 = xFit(crossingIdx + 1);
|
||||
y1 = delta(crossingIdx);
|
||||
y2 = delta(crossingIdx + 1);
|
||||
requiredRopAtten = x1 - y1 .* (x2 - x1) ./ (y2 - y1);
|
||||
end
|
||||
|
||||
function plotFecLines(ax, fecThreshold)
|
||||
xl = xlim(ax);
|
||||
h = plot(ax, xl, [fecThreshold, fecThreshold], ...
|
||||
"LineStyle", "--", ...
|
||||
"LineWidth", 1, ...
|
||||
"Color", [0.25 0.25 0.25], ...
|
||||
"HandleVisibility", "off");
|
||||
h.Annotation.LegendInformation.IconDisplayStyle = "off";
|
||||
end
|
||||
|
||||
function applyBerStyle()
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, ...
|
||||
"setmarkers", false, "changemarkers", false);
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,377 @@
|
||||
%% 400G BER over ROP: best normal algorithms plus duobinary signaling
|
||||
% Normal algorithms are reduced to the best BER per ROP point across
|
||||
% db_mode 0/1, pre-emphasis on/off, and BER/BER_precoded result variants.
|
||||
% Duobinary signaling uses db_mode = 2 and only the sequence-detection BER
|
||||
% stored in the BER field.
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Query data
|
||||
|
||||
selectedPamLevels = [4, 6, 8];
|
||||
selectedFiberLengthKm = 1;
|
||||
selectedWavelengthNm = 1310;
|
||||
selectedBitrateGbps = 360;
|
||||
selectedIsMpi = 0; % set [] to use all entries
|
||||
maxPowerPdIn = []; % set a numeric limit to enable
|
||||
|
||||
normalDbModes = [double(db_mode.no_db)];
|
||||
duobinaryDbMode = double(db_mode.db_encoded);
|
||||
|
||||
maxBerForPlot = 0.5;
|
||||
showRawEntries = false;
|
||||
showBestLine = true;
|
||||
|
||||
algoStyles = defaultAlgorithmStyles();
|
||||
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor_highspeed", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', selectedFiberLengthKm);
|
||||
fp.where('Runs', 'wavelength', 'EQUALS', selectedWavelengthNm);
|
||||
fp.where('Runs', 'bitrate', 'EQUALS', selectedBitrateGbps .* 1e9);
|
||||
if ~isempty(selectedIsMpi)
|
||||
fp.where('Runs', 'is_mpi', 'EQUALS', selectedIsMpi);
|
||||
end
|
||||
if ~isempty(maxPowerPdIn)
|
||||
fp.where('Runs', 'power_pd_in', 'LESS_THAN', maxPowerPdIn);
|
||||
end
|
||||
|
||||
selectedFields = [ ...
|
||||
db.getTableFieldNames('power_state_info'); ...
|
||||
db.getTableFieldNames('dashboard_ungrouped_alltime')];
|
||||
selectedFields = appendMissingFields(selectedFields, ...
|
||||
{'Runs.precomp_amp'; 'Runs.is_mpi'; 'Runs.power_pd_in'});
|
||||
selectedFields = selectedFields(:);
|
||||
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d 400G ROP result rows.\n", height(rawData));
|
||||
|
||||
%% 2) Clean data and build the five plotted curves
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["result_id", "run_id", "eq_id", "bitrate", "grossrate", ...
|
||||
"symbolrate", "pam_level", "wavelength", "fiber_length", "db_mode", ...
|
||||
"rop_attenuation", "precomp_amp", "is_mpi", "power_rop", ...
|
||||
"power_mzm", "power_pd_in", "voa_atten", "numBits", "numBitErr", ...
|
||||
"BER", "numBitErr_precoded", "BER_precoded", "STD", "STDrx", ...
|
||||
"GMI", "AIR", "NGMI", "EVM", "Alpha"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
|
||||
if ~ismember("precomp_amp", string(data.Properties.VariableNames))
|
||||
warning("plot_rop_best_algos:NoPrecompAmp", ...
|
||||
"Runs.precomp_amp was not returned. Falling back to pre_emphasis = (db_mode == 0).");
|
||||
data.pre_emphasis = data.db_mode == double(db_mode.no_db);
|
||||
else
|
||||
data.pre_emphasis = derivePreEmphasis(data.precomp_amp, data.db_mode);
|
||||
end
|
||||
|
||||
normalRows = data(ismember(data.db_mode, normalDbModes), :);
|
||||
normalPlotData = buildNormalMetricRows(normalRows);
|
||||
normalPlotData = normalPlotData(isfinite(normalPlotData.BER_plot) & ...
|
||||
normalPlotData.BER_plot > 0 & normalPlotData.BER_plot < maxBerForPlot, :);
|
||||
|
||||
duobinaryRows = data(data.db_mode == duobinaryDbMode, :);
|
||||
if ismember("equalizer_structure", string(duobinaryRows.Properties.VariableNames))
|
||||
duobinaryRows = duobinaryRows( ...
|
||||
equalizerMask(duobinaryRows.equalizer_structure, ...
|
||||
equalizer_structure.db_encoded), :);
|
||||
end
|
||||
duobinaryPlotData = buildDuobinarySignalingRows(duobinaryRows);
|
||||
duobinaryPlotData = duobinaryPlotData(isfinite(duobinaryPlotData.BER_plot) & ...
|
||||
duobinaryPlotData.BER_plot > 0 & ...
|
||||
duobinaryPlotData.BER_plot < maxBerForPlot, :);
|
||||
|
||||
plotData = [normalPlotData; duobinaryPlotData];
|
||||
if isempty(plotData)
|
||||
warning("plot_rop_best_algos:NoRows", ...
|
||||
"No rows remain after length/PAM/rate/BER filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
[plotData.rop_axis, ropAxisLabel] = deriveRopAxis(plotData);
|
||||
plotData.rop_axis = round(plotData.rop_axis, 4);
|
||||
plotData.bitrate_Gbps = plotData.bitrate .* 1e-9;
|
||||
plotData.grossrate_Gbps = plotData.grossrate .* 1e-9;
|
||||
plotData = plotData(isfinite(plotData.rop_axis), :);
|
||||
|
||||
fprintf("Remaining candidate BER rows: %d\n", height(plotData));
|
||||
disp(groupcounts(plotData, ["algorithm_key", "db_mode", "pre_emphasis", "precode"]));
|
||||
|
||||
bestPlotData = bestBerByAlgorithmAndRop(plotData);
|
||||
fprintf("Keeping %d best-BER rows across PAM/algorithm/ROP groups.\n", ...
|
||||
height(bestPlotData));
|
||||
disp(groupcounts(bestPlotData, "algorithm_key"));
|
||||
|
||||
%% 3) Plot one 1x3 figure with five lines per PAM
|
||||
|
||||
availableStyles = algoStyles(hasAlgorithmRows(bestPlotData, algoStyles), :);
|
||||
if isempty(availableStyles)
|
||||
warning("plot_rop_best_algos:NoSelectedAlgorithms", ...
|
||||
"None of the configured algorithm styles match the queried rows.");
|
||||
return
|
||||
end
|
||||
|
||||
fig = figure(432); clf;
|
||||
t = tiledlayout(fig, 1, numel(selectedPamLevels), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
selectedPamLevel = selectedPamLevels(pamIdx);
|
||||
ax = nexttile(t); hold(ax, "on");
|
||||
pamMask = bestPlotData.pam_level == selectedPamLevel;
|
||||
|
||||
for styleIdx = 1:height(availableStyles)
|
||||
style = availableStyles(styleIdx, :);
|
||||
rowMask = pamMask & bestPlotData.algorithm_key == style.algorithm_key;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
algoData = sortrows(bestPlotData(rowMask, :), "rop_axis");
|
||||
|
||||
if showRawEntries
|
||||
scatter(ax, algoData.rop_axis, algoData.BER_plot, ...
|
||||
9, ...
|
||||
"Marker", ".", ...
|
||||
"MarkerEdgeColor", style.color, ...
|
||||
"MarkerFaceColor", style.color, ...
|
||||
"MarkerEdgeAlpha", 0.25, ...
|
||||
"MarkerFaceAlpha", 0.25, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
|
||||
if showBestLine
|
||||
plot(ax, algoData.rop_axis, algoData.BER_plot, ...
|
||||
"LineStyle", style.lineStyle, ...
|
||||
"Marker", style.marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.5, ...
|
||||
"Color", style.color, ...
|
||||
"MarkerFaceColor", style.markerFaceColor, ...
|
||||
"MarkerEdgeColor", style.color, ...
|
||||
"DisplayName", style.name);
|
||||
end
|
||||
end
|
||||
|
||||
yline(ax, [2.2e-4, 4.85e-3, 2e-2], ...
|
||||
"LineWidth", 1, ...
|
||||
"LineStyle", "--", ...
|
||||
"Color", [0.25 0.25 0.25], ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
xlabel(ax, ropAxisLabel);
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [8e-5, 0.1]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
|
||||
xTicks = unique(bestPlotData.rop_axis(pamMask & ...
|
||||
isfinite(bestPlotData.rop_axis)));
|
||||
if ~isempty(xTicks)
|
||||
if isscalar(xTicks)
|
||||
xlim(ax, xTicks + [-0.5, 0.5]);
|
||||
else
|
||||
xlim(ax, [min(xTicks), max(xTicks)]);
|
||||
end
|
||||
end
|
||||
|
||||
legend(ax, "Location", "northeast", "Interpreter", "none");
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, ...
|
||||
"setmarkers", false, "changemarkers", false);
|
||||
end
|
||||
end
|
||||
|
||||
set(fig, "Position", 1e3 .* [0.1070 0.5497 1.0585 0.2282]);
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function fields = appendMissingFields(fields, extraFields)
|
||||
fields = cellstr(fields);
|
||||
extraFields = cellstr(extraFields);
|
||||
for idx = 1:numel(extraFields)
|
||||
if ~any(strcmp(fields, extraFields{idx}))
|
||||
fields{end+1, 1} = extraFields{idx}; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function styles = defaultAlgorithmStyles()
|
||||
styles = table( ...
|
||||
["vnle"; ...
|
||||
"vnle_pf_mlse"; ...
|
||||
"vnle_db_mlse"; ...
|
||||
"ml_mlse"; ...
|
||||
"db_encoded"], ...
|
||||
[equalizer_structure.vnle; ...
|
||||
equalizer_structure.vnle_pf_mlse; ...
|
||||
equalizer_structure.vnle_db_mlse; ...
|
||||
equalizer_structure.ml_mlse; ...
|
||||
equalizer_structure.db_encoded], ...
|
||||
["VNLE"; ...
|
||||
"VNLE + PF + MLSE"; ...
|
||||
"VNLE DBt. + MLSE"; ...
|
||||
"ML pre-EQ + Viterbi"; ...
|
||||
"DBS + VNLE + MLSE"], ...
|
||||
["o"; "square"; "diamond"; "^"; "v"], ...
|
||||
["-"; "-"; "-"; "-"; "-"], ...
|
||||
["w"; "w"; "w"; "w"; "w"], ...
|
||||
[clr.Paired.red; ...
|
||||
clr.Paired.green; ...
|
||||
clr.Paired.blue; ...
|
||||
clr.Paired.purple; ...
|
||||
clr.Paired.orange], ...
|
||||
'VariableNames', ["algorithm_key", "eq", "name", "marker", ...
|
||||
"lineStyle", "markerFaceColor", "color"]);
|
||||
end
|
||||
|
||||
function preEmphasis = derivePreEmphasis(precompAmp, dbMode)
|
||||
preEmphasis = false(size(dbMode));
|
||||
|
||||
validPrecomp = isfinite(precompAmp);
|
||||
preEmphasis(validPrecomp) = precompAmp(validPrecomp) > -45;
|
||||
|
||||
missingPrecomp = ~validPrecomp;
|
||||
preEmphasis(missingPrecomp) = dbMode(missingPrecomp) == double(db_mode.no_db);
|
||||
end
|
||||
|
||||
function plotData = buildNormalMetricRows(data)
|
||||
baseRows = data(isfinite(data.BER), :);
|
||||
baseRows.precode = false(height(baseRows), 1);
|
||||
baseRows.BER_plot = baseRows.BER;
|
||||
baseRows.algorithm_key = algorithmKeyFromEqualizer(baseRows.equalizer_structure);
|
||||
baseRows = baseRows(baseRows.algorithm_key ~= "", :);
|
||||
|
||||
if ismember("BER_precoded", string(data.Properties.VariableNames))
|
||||
precodedRows = data(isfinite(data.BER_precoded), :);
|
||||
precodedRows.precode = true(height(precodedRows), 1);
|
||||
precodedRows.BER_plot = precodedRows.BER_precoded;
|
||||
precodedRows.algorithm_key = algorithmKeyFromEqualizer( ...
|
||||
precodedRows.equalizer_structure);
|
||||
precodedRows = precodedRows(precodedRows.algorithm_key ~= "", :);
|
||||
plotData = [baseRows; precodedRows];
|
||||
else
|
||||
warning("plot_rop_best_algos:NoPrecodedBer", ...
|
||||
"BER_precoded was not returned. Plotting only BER rows for normal algorithms.");
|
||||
plotData = baseRows;
|
||||
end
|
||||
end
|
||||
|
||||
function plotData = buildDuobinarySignalingRows(data)
|
||||
plotData = data(isfinite(data.BER), :);
|
||||
plotData.precode = false(height(plotData), 1);
|
||||
plotData.BER_plot = plotData.BER;
|
||||
plotData.algorithm_key = repmat("db_encoded", height(plotData), 1);
|
||||
end
|
||||
|
||||
function [ropAxis, label] = deriveRopAxis(data)
|
||||
if ismember("power_mzm", string(data.Properties.VariableNames)) && ...
|
||||
any(isfinite(data.power_mzm))
|
||||
ropAxis = data.power_mzm;
|
||||
label = "ROP [dBm]";
|
||||
elseif ismember("power_pd_in", string(data.Properties.VariableNames)) && ...
|
||||
any(isfinite(data.power_pd_in))
|
||||
ropAxis = data.power_pd_in;
|
||||
label = "PD input power [dBm]";
|
||||
else
|
||||
ropAxis = data.rop_attenuation;
|
||||
label = "ROP attenuation [dB]";
|
||||
end
|
||||
end
|
||||
|
||||
function algorithmKey = algorithmKeyFromEqualizer(equalizerColumn)
|
||||
eqNumeric = equalizerNumeric(equalizerColumn);
|
||||
algorithmKey = strings(size(eqNumeric));
|
||||
|
||||
algorithmKey(eqNumeric == enumValue(equalizer_structure.vnle)) = "vnle";
|
||||
algorithmKey(eqNumeric == enumValue(equalizer_structure.vnle_pf_mlse)) = ...
|
||||
"vnle_pf_mlse";
|
||||
algorithmKey(eqNumeric == enumValue(equalizer_structure.vnle_db_mlse)) = ...
|
||||
"vnle_db_mlse";
|
||||
algorithmKey(eqNumeric == enumValue(equalizer_structure.ml_mlse)) = "ml_mlse";
|
||||
end
|
||||
|
||||
function mask = equalizerMask(equalizerColumn, eqValue)
|
||||
eqNumeric = equalizerNumeric(equalizerColumn);
|
||||
mask = eqNumeric == enumValue(eqValue);
|
||||
end
|
||||
|
||||
function eqNumeric = equalizerNumeric(equalizerColumn)
|
||||
if isa(equalizerColumn, "equalizer_structure")
|
||||
eqNumeric = double(equalizerColumn);
|
||||
elseif isnumeric(equalizerColumn)
|
||||
eqNumeric = double(equalizerColumn);
|
||||
else
|
||||
equalizerString = string(equalizerColumn);
|
||||
eqNumeric = str2double(equalizerString);
|
||||
|
||||
enumNames = ["vnle", "ffe", "dfe", "vnle_pf_mlse", ...
|
||||
"vnle_db_mlse", "db_encoded", "ml_mlse"];
|
||||
enumValues = [ ...
|
||||
enumValue(equalizer_structure.vnle), ...
|
||||
enumValue(equalizer_structure.ffe), ...
|
||||
enumValue(equalizer_structure.dfe), ...
|
||||
enumValue(equalizer_structure.vnle_pf_mlse), ...
|
||||
enumValue(equalizer_structure.vnle_db_mlse), ...
|
||||
enumValue(equalizer_structure.db_encoded), ...
|
||||
enumValue(equalizer_structure.ml_mlse)];
|
||||
|
||||
for idx = 1:numel(enumNames)
|
||||
missingNumeric = isnan(eqNumeric);
|
||||
eqNumeric(missingNumeric & equalizerString == enumNames(idx)) = ...
|
||||
enumValues(idx);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function value = enumValue(enumEntry)
|
||||
value = double(enumEntry);
|
||||
end
|
||||
|
||||
function bestData = bestBerByAlgorithmAndRop(data)
|
||||
groupVars = ["pam_level", "algorithm_key", "rop_axis"];
|
||||
groupId = findgroups(data(:, groupVars));
|
||||
keepIdx = NaN(max(groupId), 1);
|
||||
|
||||
for curGroup = 1:max(groupId)
|
||||
rowIdx = find(groupId == curGroup);
|
||||
[~, localBestIdx] = min(data.BER_plot(rowIdx));
|
||||
keepIdx(curGroup) = rowIdx(localBestIdx);
|
||||
end
|
||||
|
||||
bestData = sortrows(data(keepIdx, :), groupVars);
|
||||
end
|
||||
|
||||
function keep = hasAlgorithmRows(data, algoStyles)
|
||||
keep = false(height(algoStyles), 1);
|
||||
for idx = 1:height(algoStyles)
|
||||
keep(idx) = any(data.algorithm_key == algoStyles.algorithm_key(idx));
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,317 @@
|
||||
%% PAM4/6/8 SNR and uncoded BER versus symbolrate
|
||||
clear; clc;
|
||||
|
||||
warehouseFile = "C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\400G_revisit\results_snr_duobinary_and_partialresponse_pam468_10km.mat";
|
||||
pamLevels = [4, 6, 8];
|
||||
storageNames = ["mlse_package", "vnle_package", ...
|
||||
"dbtgt_package", "mlse_db_package"];
|
||||
techniqueNames = ["MLSE", "VNLE", "DB target", ...
|
||||
"duobinary signaling"];
|
||||
|
||||
%% Load warehouse and matching run metadata
|
||||
S = load(warehouseFile, "wh");
|
||||
wh = S.wh;
|
||||
|
||||
db = DBHandler("dataBase", "labor_highspeed", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 10);
|
||||
fp.where('Runs', 'wavelength', 'EQUALS', 1310);
|
||||
fp.where('Runs', 'rop_attenuation', 'EQUALS', 0);
|
||||
fp.where('Runs', 'is_mpi', 'EQUALS', 0);
|
||||
|
||||
[runTable, ~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||
runTable = runTable(ismember(double(runTable.pam_level), pamLevels), :);
|
||||
|
||||
%% Extract package metrics
|
||||
techniqueCol = strings(0, 1);
|
||||
pamCol = zeros(0, 1);
|
||||
dbModeCol = zeros(0, 1);
|
||||
symbolrateCol = zeros(0, 1);
|
||||
snrCol = zeros(0, 1);
|
||||
berCol = zeros(0, 1);
|
||||
|
||||
for techniqueIdx = 1:numel(storageNames)
|
||||
storageName = storageNames(techniqueIdx);
|
||||
if ~isfield(wh.sto, storageName)
|
||||
continue
|
||||
end
|
||||
|
||||
for k = 1:numel(wh.sto.(storageName))
|
||||
[phys, realizationResults] = wh.getPhysAndValueByLinIndex( ...
|
||||
storageName, k);
|
||||
if ~isfield(phys, "run_id") || isempty(realizationResults)
|
||||
continue
|
||||
end
|
||||
|
||||
runRow = find(double(runTable.run_id) == double(phys.run_id), 1);
|
||||
if isempty(runRow)
|
||||
continue
|
||||
end
|
||||
|
||||
for realization = 1:numel(realizationResults)
|
||||
package = realizationResults{realization};
|
||||
snr = readMetric(package, "SNR");
|
||||
ber = readMetric(package, "BER");
|
||||
if ~isfinite(snr) && ~isfinite(ber)
|
||||
continue
|
||||
end
|
||||
|
||||
techniqueCol(end+1, 1) = techniqueNames(techniqueIdx); %#ok<AGROW>
|
||||
pamCol(end+1, 1) = double(runTable.pam_level(runRow)); %#ok<AGROW>
|
||||
dbModeCol(end+1, 1) = double(runTable.db_mode(runRow)); %#ok<AGROW>
|
||||
symbolrateCol(end+1, 1) = double(runTable.symbolrate(runRow)) * 1e-9; %#ok<AGROW>
|
||||
snrCol(end+1, 1) = snr; %#ok<AGROW>
|
||||
berCol(end+1, 1) = ber; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
data = table(techniqueCol, pamCol, dbModeCol, symbolrateCol, snrCol, berCol, ...
|
||||
'VariableNames', ["technique", "pam_level", "db_mode", ...
|
||||
"symbolrate_GBd", "SNR", "BER"]);
|
||||
|
||||
% SNR: maximum realization value at each PAM/mode/symbolrate point.
|
||||
snrData = data(isfinite(data.SNR), :);
|
||||
snrData = groupsummary(snrData, ...
|
||||
["technique", "pam_level", "db_mode", "symbolrate_GBd"], ...
|
||||
"max", "SNR");
|
||||
snrData.Properties.VariableNames(end) = "SNR";
|
||||
|
||||
% BER: minimum positive, uncoded BER at each PAM/mode/symbolrate point.
|
||||
berData = data(isfinite(data.BER) & data.BER > 0, :);
|
||||
berData = groupsummary(berData, ...
|
||||
["technique", "pam_level", "db_mode", "symbolrate_GBd"], ...
|
||||
"min", "BER");
|
||||
berData.Properties.VariableNames(end) = "BER";
|
||||
|
||||
%% SNR figure: one tile per technique, PAM4/6/8 together
|
||||
figure(472); clf;
|
||||
tiledlayout(1, numel(pamLevels), "TileSpacing", "compact", "Padding", "compact");
|
||||
for pamLevel = pamLevels
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotMetric(ax, snrData, pamLevel, techniqueNames, "SNR");
|
||||
title(ax, sprintf("PAM-%d", pamLevel));
|
||||
xlabel(ax, "Symbolrate [GBd]");
|
||||
ylabel(ax, "SNR [dB]");
|
||||
ylim(ax, [15, 25]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("changemarkers", 0, "setcolors", false);
|
||||
end
|
||||
end
|
||||
|
||||
%% BER figure: one tile per PAM format
|
||||
figure(473); clf;
|
||||
tiledlayout(1, numel(pamLevels), "TileSpacing", "compact", "Padding", "compact");
|
||||
for pamLevel = pamLevels
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotMetric(ax, berData, pamLevel, techniqueNames, "BER");
|
||||
title(ax, sprintf("PAM-%d", pamLevel));
|
||||
xlabel(ax, "Symbolrate [GBd]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
%% Shared SNR figure: one tile per technique, PAM4/6/8 together
|
||||
figure(474); clf;
|
||||
tiledlayout(1, 4, "TileSpacing", "compact", "Padding", "compact");
|
||||
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotTechniqueSNR(ax, snrData, "VNLE", pamLevels, ...
|
||||
[0, 1], [clr.Paired.lred; clr.Paired.dred]);
|
||||
title(ax, "Full Response");
|
||||
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotTechniqueSNR(ax, snrData, "MLSE", pamLevels, ...
|
||||
[0, 1], [clr.Paired.lblue; clr.Paired.dblue]);
|
||||
title(ax, "Partial Response");
|
||||
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotTechniqueSNR(ax, snrData, "DB target", pamLevels, ...
|
||||
[0, 1], [clr.Paired.lgreen; clr.Paired.dgreen]);
|
||||
title(ax, "Duobinary Target");
|
||||
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotTechniqueSNR(ax, snrData, "duobinary signaling", pamLevels, ...
|
||||
2, clr.Paired.dlila);
|
||||
title(ax, "Duobinary Signaling");
|
||||
|
||||
%% Shared BER figure: one tile per technique, PAM4/6/8 together
|
||||
figure(475); clf;
|
||||
tiledlayout(1, 4, "TileSpacing", "compact", "Padding", "compact");
|
||||
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotTechniqueBER(ax, berData, "VNLE", pamLevels, ...
|
||||
[0, 1], [clr.Paired.lred; clr.Paired.dred]);
|
||||
title(ax, "VNLE");
|
||||
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotTechniqueBER(ax, berData, "MLSE", pamLevels, ...
|
||||
[0, 1], [clr.Paired.lblue; clr.Paired.dblue]);
|
||||
title(ax, "MLSE");
|
||||
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotTechniqueBER(ax, berData, "DB target", pamLevels, ...
|
||||
[0, 1], [clr.Paired.lgreen; clr.Paired.dgreen]);
|
||||
title(ax, "DBt.");
|
||||
|
||||
ax = nexttile; hold(ax, "on");
|
||||
plotTechniqueBER(ax, berData, "duobinary signaling", pamLevels, ...
|
||||
2, clr.Paired.dlila);
|
||||
title(ax, "DB signaling");
|
||||
|
||||
%% Local helpers
|
||||
function value = readMetric(package, metricName)
|
||||
value = NaN;
|
||||
if isempty(package) || ~isstruct(package) || ~isfield(package, "metrics")
|
||||
return
|
||||
end
|
||||
|
||||
metrics = package.metrics;
|
||||
if isstruct(metrics) && isfield(metrics, metricName)
|
||||
value = double(metrics.(metricName));
|
||||
elseif isobject(metrics) && isprop(metrics, metricName)
|
||||
value = double(metrics.(metricName));
|
||||
end
|
||||
end
|
||||
|
||||
function plotMetric(ax, data, pamLevel, techniqueNames, metricName)
|
||||
markers = ["o", "s", "diamond", "^"];
|
||||
lineStyles = ["-", "--", ":"];
|
||||
|
||||
for techniqueIdx = 1:numel(techniqueNames)
|
||||
for dbMode = 0:2
|
||||
rows = data(data.pam_level == pamLevel & ...
|
||||
data.technique == techniqueNames(techniqueIdx) & ...
|
||||
data.db_mode == dbMode, :);
|
||||
if isempty(rows)
|
||||
continue
|
||||
end
|
||||
|
||||
rows = sortrows(rows, "symbolrate_GBd");
|
||||
plot(ax, rows.symbolrate_GBd, rows.(metricName), ...
|
||||
"LineStyle", lineStyles(dbMode + 1), ...
|
||||
"Marker", markers(techniqueIdx), ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.3, ...
|
||||
"Color", pamModeColor(pamLevel, dbMode), ...
|
||||
"DisplayName", sprintf("%s, %s", ...
|
||||
techniqueNames(techniqueIdx), modeLabel(dbMode)));
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function plotTechniqueSNR(ax, data, techniqueName, pamLevels, dbModes, colors)
|
||||
markers = ["square", "hexagram", "*"];
|
||||
lineStyles = ["-", "--", ":"];
|
||||
|
||||
for modeIdx = 1:numel(dbModes)
|
||||
dbMode = dbModes(modeIdx);
|
||||
for pamIdx = 1:numel(pamLevels)
|
||||
pamLevel = pamLevels(pamIdx);
|
||||
rows = data(data.technique == techniqueName & ...
|
||||
data.pam_level == pamLevel & data.db_mode == dbMode, :);
|
||||
if isempty(rows)
|
||||
continue
|
||||
end
|
||||
|
||||
rows = sortrows(rows, "symbolrate_GBd");
|
||||
plot(ax, rows.symbolrate_GBd, rows.SNR, ...
|
||||
"LineStyle", lineStyles(dbMode + 1), ...
|
||||
"Marker", markers(pamIdx), ...
|
||||
"MarkerSize", 6, ...
|
||||
"LineWidth", 1.3, ...
|
||||
"Color", pamModeColor(pamLevel, dbMode), ...
|
||||
"DisplayName", sprintf("%s, PAM-%d", ...
|
||||
modeLabel(dbMode), pamLevel));
|
||||
end
|
||||
end
|
||||
|
||||
xlabel(ax, "Symbolrate [GBd]");
|
||||
ylabel(ax, "SNR [dB]");
|
||||
ylim(ax, [13, 24]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
function plotTechniqueBER(ax, data, techniqueName, pamLevels, dbModes, colors)
|
||||
markers = ["square", "hexagram", "*"];
|
||||
lineStyles = ["-", "--", ":"];
|
||||
|
||||
for modeIdx = 1:numel(dbModes)
|
||||
dbMode = dbModes(modeIdx);
|
||||
for pamIdx = 1:numel(pamLevels)
|
||||
pamLevel = pamLevels(pamIdx);
|
||||
rows = data(data.technique == techniqueName & ...
|
||||
data.pam_level == pamLevel & data.db_mode == dbMode, :);
|
||||
if isempty(rows)
|
||||
continue
|
||||
end
|
||||
|
||||
rows = sortrows(rows, "symbolrate_GBd");
|
||||
plot(ax, rows.symbolrate_GBd, rows.BER, ...
|
||||
"LineStyle", lineStyles(dbMode + 1), ...
|
||||
"Marker", markers(pamIdx), ...
|
||||
"MarkerSize", 6, ...
|
||||
"LineWidth", 1.3, ...
|
||||
"Color", pamModeColor(pamLevel, dbMode), ...
|
||||
"DisplayName", sprintf("%s, PAM-%d", ...
|
||||
modeLabel(dbMode), pamLevel));
|
||||
end
|
||||
end
|
||||
|
||||
xlabel(ax, "Symbolrate [GBd]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [1e-4, 1e-1]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
function color = pamModeColor(pamLevel, dbMode)
|
||||
switch pamLevel
|
||||
case 4
|
||||
lightColor = clr.Paired.lgreen;
|
||||
darkColor = clr.Paired.dgreen;
|
||||
case 6
|
||||
lightColor = clr.Paired.lblue;
|
||||
darkColor = clr.Paired.dblue;
|
||||
case 8
|
||||
lightColor = clr.Paired.lred;
|
||||
darkColor = clr.Paired.dred;
|
||||
otherwise
|
||||
error("plot_pam_comparison:UnknownPam", ...
|
||||
"Unsupported PAM level %d.", pamLevel);
|
||||
end
|
||||
|
||||
if dbMode == 0
|
||||
color = lightColor;
|
||||
else
|
||||
color = darkColor;
|
||||
end
|
||||
end
|
||||
|
||||
function label = modeLabel(dbMode)
|
||||
switch dbMode
|
||||
case 0
|
||||
label = "with preemphasis";
|
||||
case 1
|
||||
label = "no preemphasis";
|
||||
case 2
|
||||
label = "db encoded";
|
||||
otherwise
|
||||
label = sprintf("db\_mode = %d", dbMode);
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,440 @@
|
||||
%% Warehouse baud-rate and ROP analysis for FFE, MLSE, and duobinary target
|
||||
% Template source:
|
||||
% projects/Diss/400G_revisit/auswertung_baudrate.mlx
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Configuration and warehouse loading
|
||||
|
||||
warehouseFile = "W:\labdata\sioe_labor\baudrate_sweep_b2b\PAMX_b2b_baudrate20241024_210648_wh_final.mat";
|
||||
|
||||
selectedRopAttenForBaudrate = 0;
|
||||
selectedEqForRopCurves = "mlse"; % "ffe", "mlse", or "db"
|
||||
fecThreshold = 2.2e-2;
|
||||
polyfitOrderMax = 4;
|
||||
showRateAsDatarate = false;
|
||||
showRawRopMarkers = true;
|
||||
showPolynomialFits = true;
|
||||
|
||||
loadedData = load(warehouseFile);
|
||||
if isfield(loadedData, "obj")
|
||||
wh = loadedData.obj;
|
||||
elseif isfield(loadedData, "wh")
|
||||
wh = loadedData.wh;
|
||||
else
|
||||
error("plot_warehouse:NoWarehouse", ...
|
||||
"Warehouse file must contain a variable named obj or wh.");
|
||||
end
|
||||
|
||||
wh.showInfo;
|
||||
|
||||
fsymVals = double(wh.parameter.fsym.values(:).');
|
||||
ropAttenVals = double(wh.parameter.rop_atten.values(:).');
|
||||
pamVals = sort(double(wh.parameter.M.values(:).'));
|
||||
|
||||
eqStyles = defaultEqStyles();
|
||||
availableEqStyles = eqStyles(hasWarehouseStorage(wh, eqStyles.storage), :);
|
||||
if isempty(availableEqStyles)
|
||||
error("plot_warehouse:NoEqStorage", ...
|
||||
"None of the configured BER storages are present in wh.sto.");
|
||||
end
|
||||
|
||||
allData = warehouseToTable(wh, fsymVals, ropAttenVals, pamVals, availableEqStyles);
|
||||
allData = allData(isfinite(allData.ber) & allData.ber > 0, :);
|
||||
|
||||
fprintf("Loaded %d finite BER rows from warehouse.\n", height(allData));
|
||||
disp(groupcounts(allData, ["M", "eq"]));
|
||||
|
||||
for eqIdx = 1:height(availableEqStyles)
|
||||
if ~any(allData.eq == availableEqStyles.eq(eqIdx))
|
||||
warning("plot_warehouse:NoPositiveBer", ...
|
||||
"Storage %s exists, but contains no positive BER values to plot.", ...
|
||||
availableEqStyles.storage(eqIdx));
|
||||
end
|
||||
end
|
||||
|
||||
%% 2) BER versus baud rate at fixed ROP attenuation
|
||||
|
||||
fig = figure(440); clf;
|
||||
tiledlayout(1, numel(pamVals), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
ax = nexttile; hold(ax, "on");
|
||||
|
||||
for eqIdx = 1:height(availableEqStyles)
|
||||
eqStyle = availableEqStyles(eqIdx, :);
|
||||
rowMask = allData.M == pamLevel & ...
|
||||
allData.eq == eqStyle.eq & ...
|
||||
allData.rop_atten == selectedRopAttenForBaudrate;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curData = sortrows(allData(rowMask, :), "fsym_GBd");
|
||||
plot(ax, curData.fsym_GBd, curData.ber, ...
|
||||
"LineStyle", eqStyle.lineStyle, ...
|
||||
"Marker", eqStyle.marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.4, ...
|
||||
"Color", eqStyle.color, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", eqStyle.color, ...
|
||||
"DisplayName", eqStyle.name);
|
||||
end
|
||||
|
||||
yline(ax, fecThreshold, ...
|
||||
"LineStyle", "--", ...
|
||||
"LineWidth", 1, ...
|
||||
"Color", [0.25 0.25 0.25], ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
title(ax, sprintf("PAM-%d, ROP atten. %.1f dB", ...
|
||||
pamLevel, selectedRopAttenForBaudrate));
|
||||
xlabel(ax, "Symbol rate [GBd]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [1e-5, 0.5]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
applyBerStyle();
|
||||
end
|
||||
|
||||
set(fig, "Position", 1e3 .* [0.1000 0.5500 1.4113 0.3200]);
|
||||
|
||||
%% 3) ROP attenuation curves for one EQ scheme
|
||||
|
||||
selectedRopEqStyle = availableEqStyles(availableEqStyles.eq == selectedEqForRopCurves, :);
|
||||
if isempty(selectedRopEqStyle)
|
||||
error("plot_warehouse:MissingSelectedEq", ...
|
||||
"selectedEqForRopCurves = %s is not available in this warehouse.", ...
|
||||
selectedEqForRopCurves);
|
||||
end
|
||||
|
||||
fig = figure(441); clf;
|
||||
tiledlayout(1, numel(pamVals), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
rateColors = rateColorMap(numel(fsymVals));
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
ax = nexttile; hold(ax, "on");
|
||||
|
||||
for fsymIdx = 1:numel(fsymVals)
|
||||
fsym = fsymVals(fsymIdx);
|
||||
rowMask = allData.M == pamLevel & ...
|
||||
allData.eq == selectedRopEqStyle.eq & ...
|
||||
allData.fsym == fsym;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curData = sortrows(allData(rowMask, :), "rop_atten");
|
||||
curveColor = rateColors(fsymIdx, :);
|
||||
displayName = sprintf("%.0f GBd", fsym .* 1e-9);
|
||||
|
||||
if showRawRopMarkers
|
||||
plot(ax, curData.rop_atten, curData.ber, ...
|
||||
"LineStyle", "none", ...
|
||||
"Marker", "o", ...
|
||||
"MarkerSize", 3, ...
|
||||
"LineWidth", 0.8, ...
|
||||
"Color", curveColor, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", curveColor, ...
|
||||
"DisplayName", displayName);
|
||||
end
|
||||
|
||||
if showPolynomialFits
|
||||
[xFit, yFit] = fitLogBerCurve(curData.rop_atten, ...
|
||||
curData.ber, polyfitOrderMax);
|
||||
if ~isempty(xFit)
|
||||
plot(ax, xFit, yFit, ...
|
||||
"LineStyle", "-", ...
|
||||
"LineWidth", 1.1, ...
|
||||
"Color", curveColor, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
yline(ax, fecThreshold, ...
|
||||
"LineStyle", "--", ...
|
||||
"LineWidth", 1, ...
|
||||
"Color", [0.25 0.25 0.25], ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
title(ax, sprintf("PAM-%d, %s", pamLevel, selectedRopEqStyle.name));
|
||||
xlabel(ax, "ROP attenuation [dB]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
ylim(ax, [1e-5, 0.5]);
|
||||
xlim(ax, [min(ropAttenVals), max(ropAttenVals)]);
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
applyBerStyle();
|
||||
end
|
||||
|
||||
set(fig, "Position", 1e3 .* [0.1000 0.5500 1.4113 0.3200]);
|
||||
|
||||
%% 4) Required power at FEC threshold versus baud rate
|
||||
|
||||
fecData = computeFecCrossings(allData, availableEqStyles, fecThreshold, ...
|
||||
polyfitOrderMax);
|
||||
if isempty(fecData)
|
||||
warning("plot_warehouse:NoFecCrossings", ...
|
||||
"No FEC crossings were found for threshold %.3g.", fecThreshold);
|
||||
else
|
||||
fig = figure(442); clf;
|
||||
tiledlayout(1, numel(pamVals), ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
ax = nexttile; hold(ax, "on");
|
||||
|
||||
for eqIdx = 1:height(availableEqStyles)
|
||||
eqStyle = availableEqStyles(eqIdx, :);
|
||||
rowMask = fecData.M == pamLevel & fecData.eq == eqStyle.eq;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curData = sortrows(fecData(rowMask, :), "fsym_GBd");
|
||||
if showRateAsDatarate
|
||||
xData = curData.datarate_Gbps;
|
||||
xLabelText = "Datarate [Gb/s]";
|
||||
else
|
||||
xData = curData.fsym_GBd;
|
||||
xLabelText = "Symbol rate [GBd]";
|
||||
end
|
||||
|
||||
plot(ax, xData, curData.required_rop_dBm, ...
|
||||
"LineStyle", eqStyle.lineStyle, ...
|
||||
"Marker", eqStyle.marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.4, ...
|
||||
"Color", eqStyle.color, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", eqStyle.color, ...
|
||||
"DisplayName", eqStyle.name);
|
||||
end
|
||||
|
||||
title(ax, sprintf("PAM-%d, BER = %.2g", pamLevel, fecThreshold));
|
||||
xlabel(ax, xLabelText);
|
||||
ylabel(ax, "Required ROP [dBm]");
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
set(fig, "Position", 1e3 .* [0.1000 0.5500 1.4113 0.3200]);
|
||||
end
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function styles = defaultEqStyles()
|
||||
styles = table( ...
|
||||
["ffe"; "mlse"; "db"], ...
|
||||
["ber_ffe"; "ber_mlse"; "ber_db"], ...
|
||||
["FFE"; "MLSE"; "Duobinary target"], ...
|
||||
["o"; "square"; "diamond"], ...
|
||||
["-"; "-"; "-"], ...
|
||||
[clr.Paired.red; clr.Paired.green; clr.Paired.blue], ...
|
||||
'VariableNames', ["eq", "storage", "name", "marker", ...
|
||||
"lineStyle", "color"]);
|
||||
end
|
||||
|
||||
function keep = hasWarehouseStorage(wh, storageNames)
|
||||
stoFields = string(fieldnames(wh.sto));
|
||||
keep = ismember(storageNames, stoFields);
|
||||
end
|
||||
|
||||
function data = warehouseToTable(wh, fsymVals, ropAttenVals, pamVals, eqStyles)
|
||||
rows = {};
|
||||
|
||||
for eqIdx = 1:height(eqStyles)
|
||||
eqStyle = eqStyles(eqIdx, :);
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
for fsymIdx = 1:numel(fsymVals)
|
||||
fsym = fsymVals(fsymIdx);
|
||||
|
||||
berValues = wh.getStoValue(eqStyle.storage, fsym, ...
|
||||
ropAttenVals, pamLevel);
|
||||
ropValues = getOptionalStoValues(wh, "rop", fsym, ...
|
||||
ropAttenVals, pamLevel);
|
||||
pdInValues = getOptionalStoValues(wh, "pd_in", fsym, ...
|
||||
ropAttenVals, pamLevel);
|
||||
|
||||
berValues = berValues(:);
|
||||
ropValues = ropValues(:);
|
||||
pdInValues = pdInValues(:);
|
||||
|
||||
for ropIdx = 1:numel(ropAttenVals)
|
||||
rows(end+1, :) = { ... %#ok<AGROW>
|
||||
fsym, ...
|
||||
fsym .* 1e-9, ...
|
||||
ropAttenVals(ropIdx), ...
|
||||
pamLevel, ...
|
||||
eqStyle.eq, ...
|
||||
eqStyle.storage, ...
|
||||
berValues(ropIdx), ...
|
||||
ropValues(ropIdx), ...
|
||||
pdInValues(ropIdx), ...
|
||||
fsym .* floor(log2(pamLevel) * 10) / 10 .* 1e-9};
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
data = cell2table(rows, 'VariableNames', ...
|
||||
["fsym", "fsym_GBd", "rop_atten", "M", "eq", "storage", ...
|
||||
"ber", "rop_dBm", "pd_in_dBm", "datarate_Gbps"]);
|
||||
data.eq = string(data.eq);
|
||||
data.storage = string(data.storage);
|
||||
end
|
||||
|
||||
function values = getOptionalStoValues(wh, storageName, fsym, ropAttenVals, pamLevel)
|
||||
if ismember(storageName, string(fieldnames(wh.sto)))
|
||||
values = wh.getStoValue(storageName, fsym, ropAttenVals, pamLevel);
|
||||
else
|
||||
values = nan(size(ropAttenVals));
|
||||
end
|
||||
end
|
||||
|
||||
function cmap = rateColorMap(numColors)
|
||||
anchors = [ ...
|
||||
clr.Paired.lightblue; ...
|
||||
clr.Paired.blue; ...
|
||||
clr.Paired.green; ...
|
||||
clr.Paired.orange; ...
|
||||
clr.Paired.red; ...
|
||||
clr.Paired.purple];
|
||||
if numColors <= size(anchors, 1)
|
||||
cmap = anchors(1:numColors, :);
|
||||
return
|
||||
end
|
||||
|
||||
xAnchor = linspace(0, 1, size(anchors, 1));
|
||||
xQuery = linspace(0, 1, numColors);
|
||||
cmap = interp1(xAnchor, anchors, xQuery, "linear");
|
||||
end
|
||||
|
||||
function [xFit, yFit] = fitLogBerCurve(x, y, maxOrder)
|
||||
valid = isfinite(x) & isfinite(y) & y > 0;
|
||||
x = x(valid);
|
||||
y = y(valid);
|
||||
|
||||
if numel(unique(x)) < 2
|
||||
xFit = [];
|
||||
yFit = [];
|
||||
return
|
||||
end
|
||||
|
||||
[x, orderIdx] = sort(x(:));
|
||||
y = y(orderIdx);
|
||||
fitOrder = min(maxOrder, numel(unique(x)) - 1);
|
||||
coeff = polyfit(x, log10(y), fitOrder);
|
||||
xFit = linspace(min(x), max(x), 300).';
|
||||
yFit = 10 .^ polyval(coeff, xFit);
|
||||
end
|
||||
|
||||
function fecData = computeFecCrossings(data, eqStyles, fecThreshold, maxOrder)
|
||||
rows = {};
|
||||
pamVals = unique(data.M).';
|
||||
fsymVals = unique(data.fsym).';
|
||||
|
||||
for eqIdx = 1:height(eqStyles)
|
||||
eqStyle = eqStyles(eqIdx, :);
|
||||
for pamIdx = 1:numel(pamVals)
|
||||
pamLevel = pamVals(pamIdx);
|
||||
for fsymIdx = 1:numel(fsymVals)
|
||||
fsym = fsymVals(fsymIdx);
|
||||
rowMask = data.eq == eqStyle.eq & ...
|
||||
data.M == pamLevel & ...
|
||||
data.fsym == fsym;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curData = sortrows(data(rowMask, :), "rop_atten");
|
||||
[requiredRopAtten, requiredRop] = fecCrossingFromCurve( ...
|
||||
curData.rop_atten, curData.ber, curData.rop_dBm, ...
|
||||
fecThreshold, maxOrder);
|
||||
if ~isfinite(requiredRop)
|
||||
continue
|
||||
end
|
||||
|
||||
rows(end+1, :) = { ... %#ok<AGROW>
|
||||
fsym, ...
|
||||
fsym .* 1e-9, ...
|
||||
pamLevel, ...
|
||||
eqStyle.eq, ...
|
||||
requiredRopAtten, ...
|
||||
requiredRop, ...
|
||||
fsym .* floor(log2(pamLevel) * 10) / 10 .* 1e-9};
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
if isempty(rows)
|
||||
fecData = table();
|
||||
else
|
||||
fecData = cell2table(rows, 'VariableNames', ...
|
||||
["fsym", "fsym_GBd", "M", "eq", "required_rop_atten", ...
|
||||
"required_rop_dBm", "datarate_Gbps"]);
|
||||
fecData.eq = string(fecData.eq);
|
||||
end
|
||||
end
|
||||
|
||||
function [requiredRopAtten, requiredRop] = fecCrossingFromCurve( ...
|
||||
ropAtten, ber, rop, fecThreshold, maxOrder)
|
||||
[xFit, yFit] = fitLogBerCurve(ropAtten, ber, maxOrder);
|
||||
requiredRopAtten = NaN;
|
||||
requiredRop = NaN;
|
||||
|
||||
if isempty(xFit)
|
||||
return
|
||||
end
|
||||
|
||||
crossingMask = isfinite(yFit) & yFit > 0;
|
||||
xFit = xFit(crossingMask);
|
||||
yFit = yFit(crossingMask);
|
||||
if numel(xFit) < 2
|
||||
return
|
||||
end
|
||||
|
||||
delta = log10(yFit) - log10(fecThreshold);
|
||||
crossingIdx = find(delta(1:end-1) .* delta(2:end) <= 0, 1, "first");
|
||||
if isempty(crossingIdx)
|
||||
return
|
||||
end
|
||||
|
||||
x1 = xFit(crossingIdx);
|
||||
x2 = xFit(crossingIdx + 1);
|
||||
y1 = delta(crossingIdx);
|
||||
y2 = delta(crossingIdx + 1);
|
||||
requiredRopAtten = x1 - y1 .* (x2 - x1) ./ (y2 - y1);
|
||||
|
||||
validRop = isfinite(ropAtten) & isfinite(rop);
|
||||
if nnz(validRop) >= 2
|
||||
requiredRop = interp1(ropAtten(validRop), rop(validRop), ...
|
||||
requiredRopAtten, "linear", "extrap");
|
||||
else
|
||||
requiredRop = requiredRopAtten;
|
||||
end
|
||||
end
|
||||
|
||||
function applyBerStyle()
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, ...
|
||||
"setmarkers", false, "changemarkers", false);
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,691 @@
|
||||
%% 400G BER over wavelength: best normal algorithms plus duobinary signaling
|
||||
% Normal algorithms are reduced to the best BER per wavelength across
|
||||
% db_mode 0/1, pre-emphasis on/off, and BER/BER_precoded result variants.
|
||||
% Duobinary signaling uses db_mode = 2 and only the sequence-detection BER
|
||||
% stored in the BER field.
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Query data
|
||||
|
||||
selectedPamLevels = [4, 6, 8];
|
||||
selectedFiberLengthKm = 5;
|
||||
selectedBitratesGbps = 300:30:480; % set [] to use all available bitrates
|
||||
selectedRopAttenuation = 0; % set [] to use all ROP attenuation values
|
||||
selectedIsMpi = 0; % set [] to use all entries
|
||||
|
||||
normalDbModes = [double(db_mode.no_db), double(db_mode.db_precoded)];
|
||||
duobinaryDbMode = double(db_mode.db_encoded);
|
||||
|
||||
maxBerForPlot = 0.5;
|
||||
showRawEntries = false;
|
||||
showBestLine = true;
|
||||
showPolynomialFits = true;
|
||||
polyfitOrder = 4;
|
||||
fecThreshold = 2e-2;
|
||||
|
||||
algoStyles = defaultAlgorithmStyles();
|
||||
areaResults = cell(numel(selectedPamLevels), 1);
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
selectedPamLevel = selectedPamLevels(pamIdx);
|
||||
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor_highspeed", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', selectedFiberLengthKm);
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', selectedPamLevel);
|
||||
if ~isempty(selectedRopAttenuation)
|
||||
fp.where('Runs', 'rop_attenuation', 'EQUALS', selectedRopAttenuation);
|
||||
end
|
||||
if ~isempty(selectedIsMpi)
|
||||
fp.where('Runs', 'is_mpi', 'EQUALS', selectedIsMpi);
|
||||
end
|
||||
|
||||
selectedFields = db.getTableFieldNames('dashboard_ungrouped_alltime');
|
||||
selectedFields = appendMissingFields(selectedFields, ...
|
||||
{'Runs.precomp_amp'; 'Runs.is_mpi'});
|
||||
selectedFields = selectedFields(:);
|
||||
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d wavelength-sweep result rows.\n", height(rawData));
|
||||
|
||||
%% 2) Clean data and build the five plotted curves
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["result_id", "run_id", "eq_id", "bitrate", "grossrate", ...
|
||||
"symbolrate", "pam_level", "wavelength", "fiber_length", "db_mode", ...
|
||||
"rop_attenuation", "precomp_amp", "is_mpi", "numBits", "numBitErr", ...
|
||||
"BER", "numBitErr_precoded", "BER_precoded", "STD", "STDrx", ...
|
||||
"GMI", "AIR", "NGMI", "EVM", "Alpha"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
data.bitrate_Gbps = data.bitrate .* 1e-9;
|
||||
if ~isempty(selectedBitratesGbps)
|
||||
data = data(ismember(round(data.bitrate_Gbps, 6), selectedBitratesGbps), :);
|
||||
end
|
||||
|
||||
if ~ismember("precomp_amp", string(data.Properties.VariableNames))
|
||||
warning("plot_wavelength_best_algos:NoPrecompAmp", ...
|
||||
"Runs.precomp_amp was not returned. Falling back to pre_emphasis = (db_mode == 0).");
|
||||
data.pre_emphasis = data.db_mode == double(db_mode.no_db);
|
||||
else
|
||||
data.pre_emphasis = derivePreEmphasis(data.precomp_amp, data.db_mode);
|
||||
end
|
||||
|
||||
normalRows = data(ismember(data.db_mode, normalDbModes), :);
|
||||
normalPlotData = buildNormalMetricRows(normalRows);
|
||||
normalPlotData = normalPlotData(isfinite(normalPlotData.BER_plot) & ...
|
||||
normalPlotData.BER_plot > 0 & normalPlotData.BER_plot < maxBerForPlot, :);
|
||||
|
||||
duobinaryRows = data(data.db_mode == duobinaryDbMode, :);
|
||||
if ismember("equalizer_structure", string(duobinaryRows.Properties.VariableNames))
|
||||
duobinaryRows = duobinaryRows( ...
|
||||
equalizerMask(duobinaryRows.equalizer_structure, ...
|
||||
equalizer_structure.db_encoded), :);
|
||||
end
|
||||
duobinaryPlotData = buildDuobinarySignalingRows(duobinaryRows);
|
||||
duobinaryPlotData = duobinaryPlotData(isfinite(duobinaryPlotData.BER_plot) & ...
|
||||
duobinaryPlotData.BER_plot > 0 & ...
|
||||
duobinaryPlotData.BER_plot < maxBerForPlot, :);
|
||||
|
||||
plotData = [normalPlotData; duobinaryPlotData];
|
||||
if isempty(plotData)
|
||||
warning("plot_wavelength_best_algos:NoRows", ...
|
||||
"No rows remain after length/PAM/rate/BER filtering.");
|
||||
continue
|
||||
end
|
||||
|
||||
plotData.bitrate_Gbps = plotData.bitrate .* 1e-9;
|
||||
plotData.grossrate_Gbps = plotData.grossrate .* 1e-9;
|
||||
|
||||
fprintf("Remaining candidate BER rows: %d\n", height(plotData));
|
||||
disp(groupcounts(plotData, ["algorithm_key", "db_mode", "pre_emphasis", "precode"]));
|
||||
|
||||
bestPlotData = bestBerByAlgorithmAndWavelength(plotData);
|
||||
fprintf("Keeping %d best-BER rows across algorithm/wavelength groups.\n", ...
|
||||
height(bestPlotData));
|
||||
disp(groupcounts(bestPlotData, "algorithm_key"));
|
||||
|
||||
%% 3) Plot one tile per algorithm
|
||||
|
||||
availableStyles = algoStyles(hasAlgorithmRows(bestPlotData, algoStyles), :);
|
||||
if isempty(availableStyles)
|
||||
warning("plot_wavelength_best_algos:NoSelectedAlgorithms", ...
|
||||
"None of the configured algorithm styles match the queried rows.");
|
||||
continue
|
||||
end
|
||||
|
||||
fig = figure(432 + pamIdx); clf;
|
||||
t = tiledlayout(fig, 1, height(availableStyles), ...
|
||||
"TileSpacing","compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
availableBitratesGbps = unique(bestPlotData.bitrate_Gbps(isfinite( ...
|
||||
bestPlotData.bitrate_Gbps))).';
|
||||
bitrateMarkers = bitrateMarkerSet(numel(availableBitratesGbps));
|
||||
|
||||
for styleIdx = 1:height(availableStyles)
|
||||
style = availableStyles(styleIdx, :);
|
||||
ax = nexttile(t); hold(ax, "on");
|
||||
bitrateColors = sequentialColors(style.color, numel(availableBitratesGbps), ...
|
||||
style.algorithm_key);
|
||||
|
||||
for bitrateIdx = 1:numel(availableBitratesGbps)
|
||||
bitrateGbps = availableBitratesGbps(bitrateIdx);
|
||||
rowMask = bestPlotData.algorithm_key == style.algorithm_key & ...
|
||||
bestPlotData.bitrate_Gbps == bitrateGbps;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
algoData = sortrows(bestPlotData(rowMask, :), "wavelength");
|
||||
curveColor = bitrateColors(bitrateIdx, :);
|
||||
|
||||
if showRawEntries
|
||||
scatter(ax, algoData.wavelength, algoData.BER_plot, ...
|
||||
9, ...
|
||||
"Marker", ".", ...
|
||||
"MarkerEdgeColor", curveColor, ...
|
||||
"MarkerFaceColor", curveColor, ...
|
||||
"MarkerEdgeAlpha", 0.25, ...
|
||||
"MarkerFaceAlpha", 0.25, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
|
||||
if showBestLine
|
||||
plot(ax, algoData.wavelength, algoData.BER_plot, ...
|
||||
"LineStyle", style.lineStyle, ...
|
||||
"Marker", bitrateMarkers(bitrateIdx), ...
|
||||
"MarkerSize", 2.5, ...
|
||||
"LineWidth", 1.0, ...
|
||||
"Color", curveColor, ...
|
||||
"MarkerFaceColor", style.markerFaceColor, ...
|
||||
"MarkerEdgeColor", curveColor, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
|
||||
if showPolynomialFits
|
||||
[xFit, yFit] = fitLogBerCurve(algoData.wavelength, ...
|
||||
algoData.BER_plot, polyfitOrder);
|
||||
if ~isempty(xFit)
|
||||
plot(ax, xFit, yFit, ...
|
||||
"LineStyle", ":", ...
|
||||
"LineWidth", 1.1, ...
|
||||
"Color", curveColor, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% title(ax, style.name, "Interpreter", "none");
|
||||
xlabel(ax, "Wavelength [nm]");
|
||||
ylabel(ax, "BER");
|
||||
set(ax, "YScale", "log");
|
||||
|
||||
if selectedPamLevel == 4
|
||||
ylim(ax, [1e-6, 0.3]);
|
||||
elseif selectedPamLevel == 6
|
||||
ylim(ax, [1e-4, 0.3]);
|
||||
elseif selectedPamLevel == 8
|
||||
ylim(ax, [1e-4, 0.3]);
|
||||
end
|
||||
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
|
||||
if styleIdx > 1
|
||||
ylabel(ax, "");
|
||||
yticklabels(ax, {});
|
||||
end
|
||||
|
||||
xTicks = unique(bestPlotData.wavelength(isfinite(bestPlotData.wavelength)));
|
||||
xTicks = xTicks(1:2:end);
|
||||
xTicks_own = [1300,1310,1320];
|
||||
set(ax, "XTick", xTicks_own);
|
||||
|
||||
if ~isempty(xTicks)
|
||||
xlim(ax, [min(xTicks)-1, max(xTicks)+1]);
|
||||
end
|
||||
|
||||
plotFecLines(ax, fecThreshold);
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, ...
|
||||
"setmarkers", false, "changemarkers", false);
|
||||
end
|
||||
|
||||
text(ax, 0.02, 0.02, style.name, ...
|
||||
"Units", "normalized", ...
|
||||
"HorizontalAlignment", "left", ...
|
||||
"VerticalAlignment", "bottom", ...
|
||||
"BackgroundColor", "white", ...
|
||||
"EdgeColor", [0.60 0.60 0.60], ...
|
||||
"LineWidth", 0.5, ...
|
||||
"Margin", 2, ...
|
||||
"FontSize", 8, ...
|
||||
"Interpreter", "none", ...
|
||||
"Clipping", "on");
|
||||
|
||||
end
|
||||
set(fig, "Position", 1e3 .* [0.1070 0.5497 1.4113 0.3253]);
|
||||
|
||||
wavelengthTikzPath = sprintf( ...
|
||||
"C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/400g/10km_wavelength_compare_pam%d.tikz", ...
|
||||
selectedPamLevel);
|
||||
% mat2tikz_improved(wavelengthTikzPath, "cleanfigure", 0);
|
||||
|
||||
areaData = computePermissibleWavelengthAreas(bestPlotData, availableStyles, ...
|
||||
fecThreshold, polyfitOrder);
|
||||
if isempty(areaData)
|
||||
warning("plot_wavelength_best_algos:NoPermissibleAreas", ...
|
||||
"No measured wavelength samples are at or below the BER threshold %.3g.", ...
|
||||
fecThreshold);
|
||||
else
|
||||
areaResults{pamIdx} = areaData;
|
||||
end
|
||||
end
|
||||
|
||||
%% 4) Permissible wavelength area versus gross rate for all PAM levels
|
||||
|
||||
areaFig = figure(436); clf;
|
||||
areaLayout = tiledlayout(areaFig, 1, 3, ...
|
||||
"TileSpacing", "compact", ...
|
||||
"Padding", "compact");
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
areaAx = nexttile(areaLayout); hold(areaAx, "on");
|
||||
areaData = areaResults{pamIdx};
|
||||
|
||||
if isempty(areaData)
|
||||
grid(areaAx, "on");
|
||||
box(areaAx, "on");
|
||||
else
|
||||
for styleIdx = 1:height(algoStyles)
|
||||
style = algoStyles(styleIdx, :);
|
||||
rowMask = areaData.algorithm_key == style.algorithm_key;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
algoAreaData = sortrows(areaData(rowMask, :), "grossrate_Gbps");
|
||||
plot(areaAx, algoAreaData.grossrate_Gbps, ...
|
||||
algoAreaData.permissible_wavelength_area_nm, ...
|
||||
"LineStyle", style.lineStyle, ...
|
||||
"Marker", style.marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.4, ...
|
||||
"Color", style.color, ...
|
||||
"MarkerFaceColor", style.markerFaceColor, ...
|
||||
"MarkerEdgeColor", style.color, ...
|
||||
"DisplayName", style.name, ...
|
||||
"HandleVisibility", "on");
|
||||
end
|
||||
end
|
||||
ylim([5 35]);
|
||||
xlabel(areaAx, "Gross rate [Gb/s]");
|
||||
if pamIdx == 1
|
||||
ylabel(areaAx, "Permissible wavelength area [nm]");
|
||||
else
|
||||
ylabel(areaAx, "");
|
||||
% Ensure yticklabels refers to the function, not a variable
|
||||
if exist("yticklabels", "var")
|
||||
clear yticklabels
|
||||
end
|
||||
yticks(areaAx,[5:5:35]);
|
||||
yticklabels(areaAx, "");
|
||||
end
|
||||
grid(areaAx, "on");
|
||||
box(areaAx, "on");
|
||||
text(areaAx, 0.2, 0.06, sprintf("PAM%d", selectedPamLevels(pamIdx)), ...
|
||||
"Units", "normalized", ...
|
||||
"HorizontalAlignment", "left", ...
|
||||
"VerticalAlignment", "bottom", ...
|
||||
"BackgroundColor", "white", ...
|
||||
"EdgeColor", [0.60 0.60 0.60], ...
|
||||
"LineWidth", 0.5, ...
|
||||
"Margin", 2, ...
|
||||
"FontSize", 8, ...
|
||||
"Interpreter", "none", ...
|
||||
"Clipping", "on");
|
||||
|
||||
end
|
||||
legend
|
||||
set(areaFig, "Position", 1e3 .* [0.3500 0.3500 0.7000 0.8000]);
|
||||
% mat2tikz_improved( ...
|
||||
% "C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/400g/10km_permissible_wavelength.tikz", ...
|
||||
% "cleanfigure", 0);
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function fields = appendMissingFields(fields, extraFields)
|
||||
fields = cellstr(fields);
|
||||
extraFields = cellstr(extraFields);
|
||||
for idx = 1:numel(extraFields)
|
||||
if ~any(strcmp(fields, extraFields{idx}))
|
||||
fields{end+1, 1} = extraFields{idx}; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function styles = defaultAlgorithmStyles()
|
||||
styles = table( ...
|
||||
["vnle"; ...
|
||||
"vnle_pf_mlse"; ...
|
||||
"vnle_db_mlse"; ...
|
||||
"ml_mlse"; ...
|
||||
"db_encoded"], ...
|
||||
[equalizer_structure.vnle; ...
|
||||
equalizer_structure.vnle_pf_mlse; ...
|
||||
equalizer_structure.vnle_db_mlse; ...
|
||||
equalizer_structure.ml_mlse; ...
|
||||
equalizer_structure.db_encoded], ...
|
||||
["VNLE"; ...
|
||||
"VNLE + PF + MLSE"; ...
|
||||
"VNLE DBt. + MLSE"; ...
|
||||
"ML pre-EQ + Viterbi"; ...
|
||||
"DBS + VNLE + MLSE"], ...
|
||||
["o"; "square"; "diamond"; "^"; "v"], ...
|
||||
["-"; "-"; "-"; "-"; "-"], ...
|
||||
["w"; "w"; "w"; "w"; "w"], ...
|
||||
[clr.Paired.red; ...
|
||||
clr.Paired.green; ...
|
||||
clr.Paired.blue; ...
|
||||
clr.Paired.purple; ...
|
||||
clr.Paired.orange], ...
|
||||
'VariableNames', ["algorithm_key", "eq", "name", "marker", ...
|
||||
"lineStyle", "markerFaceColor", "color"]);
|
||||
end
|
||||
|
||||
function preEmphasis = derivePreEmphasis(precompAmp, dbMode)
|
||||
preEmphasis = false(size(dbMode));
|
||||
|
||||
validPrecomp = isfinite(precompAmp);
|
||||
preEmphasis(validPrecomp) = precompAmp(validPrecomp) > -45;
|
||||
|
||||
missingPrecomp = ~validPrecomp;
|
||||
preEmphasis(missingPrecomp) = dbMode(missingPrecomp) == double(db_mode.no_db);
|
||||
end
|
||||
|
||||
function plotData = buildNormalMetricRows(data)
|
||||
baseRows = data(isfinite(data.BER), :);
|
||||
baseRows.precode = false(height(baseRows), 1);
|
||||
baseRows.BER_plot = baseRows.BER;
|
||||
baseRows.algorithm_key = algorithmKeyFromEqualizer(baseRows.equalizer_structure);
|
||||
baseRows = baseRows(baseRows.algorithm_key ~= "", :);
|
||||
|
||||
if ismember("BER_precoded", string(data.Properties.VariableNames))
|
||||
precodedRows = data(isfinite(data.BER_precoded), :);
|
||||
precodedRows.precode = true(height(precodedRows), 1);
|
||||
precodedRows.BER_plot = precodedRows.BER_precoded;
|
||||
precodedRows.algorithm_key = algorithmKeyFromEqualizer( ...
|
||||
precodedRows.equalizer_structure);
|
||||
precodedRows = precodedRows(precodedRows.algorithm_key ~= "", :);
|
||||
plotData = [baseRows; precodedRows];
|
||||
else
|
||||
warning("plot_wavelength_best_algos:NoPrecodedBer", ...
|
||||
"BER_precoded was not returned. Plotting only BER rows for normal algorithms.");
|
||||
plotData = baseRows;
|
||||
end
|
||||
end
|
||||
|
||||
function plotData = buildDuobinarySignalingRows(data)
|
||||
plotData = data(isfinite(data.BER), :);
|
||||
plotData.precode = false(height(plotData), 1);
|
||||
plotData.BER_plot = plotData.BER;
|
||||
plotData.algorithm_key = repmat("db_encoded", height(plotData), 1);
|
||||
end
|
||||
|
||||
function algorithmKey = algorithmKeyFromEqualizer(equalizerColumn)
|
||||
eqNumeric = equalizerNumeric(equalizerColumn);
|
||||
algorithmKey = strings(size(eqNumeric));
|
||||
|
||||
algorithmKey(eqNumeric == enumValue(equalizer_structure.vnle)) = "vnle";
|
||||
algorithmKey(eqNumeric == enumValue(equalizer_structure.vnle_pf_mlse)) = ...
|
||||
"vnle_pf_mlse";
|
||||
algorithmKey(eqNumeric == enumValue(equalizer_structure.vnle_db_mlse)) = ...
|
||||
"vnle_db_mlse";
|
||||
algorithmKey(eqNumeric == enumValue(equalizer_structure.ml_mlse)) = "ml_mlse";
|
||||
end
|
||||
|
||||
function mask = equalizerMask(equalizerColumn, eqValue)
|
||||
eqNumeric = equalizerNumeric(equalizerColumn);
|
||||
mask = eqNumeric == enumValue(eqValue);
|
||||
end
|
||||
|
||||
function eqNumeric = equalizerNumeric(equalizerColumn)
|
||||
if isa(equalizerColumn, "equalizer_structure")
|
||||
eqNumeric = double(equalizerColumn);
|
||||
elseif isnumeric(equalizerColumn)
|
||||
eqNumeric = double(equalizerColumn);
|
||||
else
|
||||
equalizerString = string(equalizerColumn);
|
||||
eqNumeric = str2double(equalizerString);
|
||||
|
||||
enumNames = ["vnle", "ffe", "dfe", "vnle_pf_mlse", ...
|
||||
"vnle_db_mlse", "db_encoded", "ml_mlse"];
|
||||
enumValues = [ ...
|
||||
enumValue(equalizer_structure.vnle), ...
|
||||
enumValue(equalizer_structure.ffe), ...
|
||||
enumValue(equalizer_structure.dfe), ...
|
||||
enumValue(equalizer_structure.vnle_pf_mlse), ...
|
||||
enumValue(equalizer_structure.vnle_db_mlse), ...
|
||||
enumValue(equalizer_structure.db_encoded), ...
|
||||
enumValue(equalizer_structure.ml_mlse)];
|
||||
|
||||
for idx = 1:numel(enumNames)
|
||||
missingNumeric = isnan(eqNumeric);
|
||||
eqNumeric(missingNumeric & equalizerString == enumNames(idx)) = ...
|
||||
enumValues(idx);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function value = enumValue(enumEntry)
|
||||
value = double(enumEntry);
|
||||
end
|
||||
|
||||
function bestData = bestBerByAlgorithmAndWavelength(data)
|
||||
groupVars = ["algorithm_key", "bitrate_Gbps", "wavelength"];
|
||||
groupId = findgroups(data(:, groupVars));
|
||||
keepIdx = NaN(max(groupId), 1);
|
||||
|
||||
for curGroup = 1:max(groupId)
|
||||
rowIdx = find(groupId == curGroup);
|
||||
[~, localBestIdx] = min(data.BER_plot(rowIdx));
|
||||
keepIdx(curGroup) = rowIdx(localBestIdx);
|
||||
end
|
||||
|
||||
bestData = sortrows(data(keepIdx, :), groupVars);
|
||||
end
|
||||
|
||||
function [xFit, yFit, fitModel] = fitLogBerCurve(x, y, maxOrder)
|
||||
valid = isfinite(x) & isfinite(y) & y > 0;
|
||||
x = x(valid);
|
||||
y = y(valid);
|
||||
|
||||
[x, orderIdx] = sort(x(:));
|
||||
y = y(orderIdx);
|
||||
[x, uniqueIdx] = unique(x);
|
||||
y = y(uniqueIdx);
|
||||
if numel(x) < 2
|
||||
xFit = [];
|
||||
yFit = [];
|
||||
fitModel = [];
|
||||
return
|
||||
end
|
||||
|
||||
% Keep the requested fourth-order fit when enough wavelength points exist.
|
||||
% With fewer than five unique points, use the highest identifiable order.
|
||||
fitOrder = min(maxOrder, numel(x) - 1);
|
||||
[coefficients, ~, mu] = polyfit(x, log10(y), fitOrder);
|
||||
fitModel.coefficients = coefficients;
|
||||
fitModel.mu = mu;
|
||||
xFit = linspace(min(x), max(x), 300).';
|
||||
yFit = 10 .^ polyval(coefficients, (xFit - mu(1)) ./ mu(2));
|
||||
end
|
||||
|
||||
function areaData = computePermissibleWavelengthAreas(data, algoStyles, ...
|
||||
fecThreshold, maxOrder)
|
||||
grossrateValues = unique(data.grossrate_Gbps(isfinite( ...
|
||||
data.grossrate_Gbps))).';
|
||||
wavelengthGrid = unique(data.wavelength(isfinite(data.wavelength))).';
|
||||
rows = cell(height(algoStyles) * numel(grossrateValues), 5);
|
||||
rowIdx = 0;
|
||||
|
||||
for styleIdx = 1:height(algoStyles)
|
||||
style = algoStyles(styleIdx, :);
|
||||
algorithmMask = data.algorithm_key == style.algorithm_key;
|
||||
|
||||
for grossrateIdx = 1:numel(grossrateValues)
|
||||
grossrateGb = grossrateValues(grossrateIdx);
|
||||
rowMask = algorithmMask & data.grossrate_Gbps == grossrateGb;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curveData = sortrows(data(rowMask, :), "wavelength");
|
||||
[wavelengthMin, wavelengthMax] = permissibleWavelengthRange( ...
|
||||
curveData.wavelength, curveData.BER_plot, fecThreshold, ...
|
||||
maxOrder, wavelengthGrid);
|
||||
if any(~isfinite([wavelengthMin, wavelengthMax]))
|
||||
continue
|
||||
end
|
||||
|
||||
rowIdx = rowIdx + 1;
|
||||
rows(rowIdx, :) = { ...
|
||||
style.algorithm_key, ...
|
||||
grossrateGb, ...
|
||||
wavelengthMin, ...
|
||||
wavelengthMax, ...
|
||||
wavelengthMax - wavelengthMin};
|
||||
end
|
||||
end
|
||||
|
||||
if rowIdx == 0
|
||||
areaData = table();
|
||||
else
|
||||
rows = rows(1:rowIdx, :);
|
||||
areaData = cell2table(rows, "VariableNames", ...
|
||||
["algorithm_key", "grossrate_Gbps", ...
|
||||
"wavelength_min_allowed_nm", "wavelength_max_allowed_nm", ...
|
||||
"permissible_wavelength_area_nm"]);
|
||||
areaData.algorithm_key = string(areaData.algorithm_key);
|
||||
end
|
||||
end
|
||||
|
||||
function [wavelengthMin, wavelengthMax] = permissibleWavelengthRange(x, y, ...
|
||||
fecThreshold, maxOrder, wavelengthGrid)
|
||||
wavelengthMin = NaN;
|
||||
wavelengthMax = NaN;
|
||||
valid = isfinite(x) & isfinite(y) & y > 0;
|
||||
x = x(valid);
|
||||
y = y(valid);
|
||||
if isempty(x)
|
||||
return
|
||||
end
|
||||
|
||||
[x, orderIdx] = sort(x(:));
|
||||
y = y(orderIdx);
|
||||
[x, uniqueIdx] = unique(x);
|
||||
y = y(uniqueIdx);
|
||||
wavelengthGrid = sort(wavelengthGrid(:));
|
||||
if isempty(wavelengthGrid)
|
||||
return
|
||||
end
|
||||
|
||||
globalRange = [wavelengthGrid(1), wavelengthGrid(end)];
|
||||
edgeTolerance = max(1e-9, 1e-9 .* max(abs(globalRange)));
|
||||
missingLeftEdge = x(1) > globalRange(1) + edgeTolerance;
|
||||
missingRightEdge = x(end) < globalRange(2) - edgeTolerance;
|
||||
|
||||
% If all observed points are below the FEC, use the complete sweep span
|
||||
% instead of requiring polynomial roots outside the observed range.
|
||||
if all(y <= fecThreshold)
|
||||
wavelengthMin = globalRange(1);
|
||||
wavelengthMax = globalRange(2);
|
||||
return
|
||||
end
|
||||
|
||||
[~, ~, fitModel] = fitLogBerCurve(x, y, maxOrder);
|
||||
if isempty(fitModel)
|
||||
return
|
||||
end
|
||||
|
||||
xRange = [x(1), x(end)];
|
||||
crossingCoefficients = fitModel.coefficients;
|
||||
crossingCoefficients(end) = crossingCoefficients(end) - log10(fecThreshold);
|
||||
rootsAtThreshold = roots(crossingCoefficients);
|
||||
realRoots = real(rootsAtThreshold(abs(imag(rootsAtThreshold)) < ...
|
||||
1e-7 .* max(1, abs(real(rootsAtThreshold)))));
|
||||
realRoots = realRoots .* fitModel.mu(2) + fitModel.mu(1);
|
||||
realRoots = sort(realRoots(realRoots >= xRange(1) & realRoots <= xRange(2)));
|
||||
if isempty(realRoots)
|
||||
return
|
||||
end
|
||||
|
||||
% Include the observed boundaries in the interval search. This lets each
|
||||
% side be handled independently: a side that remains below FEC uses the
|
||||
% corresponding observed edge, while a side that crosses FEC uses its root.
|
||||
intervalEdges = [xRange(1); realRoots(:); xRange(2)];
|
||||
intervalWidths = diff(intervalEdges);
|
||||
intervalIsAllowed = false(size(intervalWidths));
|
||||
thresholdLogBer = log10(fecThreshold);
|
||||
for intervalIdx = 1:numel(intervalEdges)-1
|
||||
midpoint = mean(intervalEdges(intervalIdx:intervalIdx + 1));
|
||||
normalizedMidpoint = (midpoint - fitModel.mu(1)) ./ fitModel.mu(2);
|
||||
intervalIsAllowed(intervalIdx) = polyval(fitModel.coefficients, ...
|
||||
normalizedMidpoint) <= thresholdLogBer;
|
||||
end
|
||||
|
||||
allowedIntervals = find(intervalIsAllowed);
|
||||
if isempty(allowedIntervals)
|
||||
return
|
||||
end
|
||||
|
||||
[~, widestAllowedIdx] = max(intervalWidths(allowedIntervals));
|
||||
selectedInterval = allowedIntervals(widestAllowedIdx);
|
||||
wavelengthMin = intervalEdges(selectedInterval);
|
||||
wavelengthMax = intervalEdges(selectedInterval + 1);
|
||||
|
||||
% A missing terminal wavelength sample is treated as below FEC when the
|
||||
% permissible interval reaches that side of the observed curve.
|
||||
if selectedInterval == 1 && missingLeftEdge
|
||||
wavelengthMin = globalRange(1);
|
||||
end
|
||||
if selectedInterval == numel(intervalWidths) && missingRightEdge
|
||||
wavelengthMax = globalRange(2);
|
||||
end
|
||||
end
|
||||
|
||||
function keep = hasAlgorithmRows(data, algoStyles)
|
||||
keep = false(height(algoStyles), 1);
|
||||
for idx = 1:height(algoStyles)
|
||||
keep(idx) = any(data.algorithm_key == algoStyles.algorithm_key(idx));
|
||||
end
|
||||
end
|
||||
|
||||
function plotFecLines(ax, fecLevels)
|
||||
xl = xlim(ax);
|
||||
for idx = 1:numel(fecLevels)
|
||||
h = plot(ax, xl, [fecLevels(idx), fecLevels(idx)], ...
|
||||
"LineWidth", 1, ...
|
||||
"LineStyle", "--", ...
|
||||
"Color", [0.25 0.25 0.25], ...
|
||||
"HandleVisibility", "off");
|
||||
h.Annotation.LegendInformation.IconDisplayStyle = "off";
|
||||
end
|
||||
end
|
||||
|
||||
function markers = bitrateMarkerSet(n)
|
||||
markerOptions = ["o", "square", "diamond", "^", "v", ">", "<"];
|
||||
if n <= numel(markerOptions)
|
||||
markers = markerOptions(1:n);
|
||||
else
|
||||
markers = markerOptions(mod(0:n-1, numel(markerOptions)) + 1);
|
||||
end
|
||||
end
|
||||
|
||||
function colors = sequentialColors(baseColor, n, algorithmKey)
|
||||
if n <= 1
|
||||
colors = baseColor;
|
||||
return
|
||||
end
|
||||
|
||||
lightBlend = linspace(0.72, 0.00, n).';
|
||||
darkBlend = linspace(0.00, 0.35, n).';
|
||||
if algorithmKey == "db_encoded"
|
||||
lightBlend = linspace(0.62, 0.00, n).';
|
||||
darkBlend = linspace(0.00, 0.12, n).';
|
||||
end
|
||||
|
||||
colors = zeros(n, 3);
|
||||
for idx = 1:n
|
||||
color = (1 - lightBlend(idx)) .* baseColor + lightBlend(idx) .* [1 1 1];
|
||||
color = (1 - darkBlend(idx)) .* color;
|
||||
colors(idx, :) = color;
|
||||
end
|
||||
colors = flip(colors);
|
||||
end
|
||||
85
projects/Diss/400G_revisit/REPLOT_OPT_FILT_ANALYSIS_BY_PAM.m
Normal file
85
projects/Diss/400G_revisit/REPLOT_OPT_FILT_ANALYSIS_BY_PAM.m
Normal file
@@ -0,0 +1,85 @@
|
||||
%% Replot opt_filt_analysis.fig grouped by PAM level
|
||||
clear; close all; clc;
|
||||
|
||||
figPath = fullfile(fileparts(mfilename("fullpath")), "opt_filt_analysis.fig");
|
||||
figIn = openfig(figPath, "invisible");
|
||||
|
||||
srcAx = findall(figIn, "Type", "axes");
|
||||
srcAx = srcAx(1);
|
||||
srcLines = flipud(findall(srcAx, "Type", "line"));
|
||||
|
||||
% Get every plotted datapoint and its display name.
|
||||
data = arrayfun(@(h) struct( ...
|
||||
"XData", h.XData, ...
|
||||
"YData", h.YData, ...
|
||||
"DisplayName", string(h.DisplayName)), srcLines);
|
||||
displayNames = string({data.DisplayName})';
|
||||
disp(displayNames);
|
||||
|
||||
selectedEqType = "ffe"; % choose "ffe" or "mlse"
|
||||
eqtype = ["ffe", "mlse"];
|
||||
eqGroup = repmat("", size(displayNames));
|
||||
for k = 1:numel(eqtype)
|
||||
eqGroup(contains(lower(displayNames), eqtype(k))) = eqtype(k);
|
||||
end
|
||||
if ~ismember(selectedEqType, eqtype)
|
||||
error("selectedEqType must be either 'ffe' or 'mlse'.");
|
||||
end
|
||||
|
||||
keep = eqGroup == selectedEqType;
|
||||
srcLines = srcLines(keep);
|
||||
data = data(keep);
|
||||
displayNames = displayNames(keep);
|
||||
|
||||
pamLevels = ["pam4", "pam6", "pam8"];
|
||||
pamGroup = repmat("", size(displayNames));
|
||||
for k = 1:numel(pamLevels)
|
||||
pamGroup(contains(lower(displayNames), pamLevels(k))) = pamLevels(k);
|
||||
end
|
||||
|
||||
% Group by the first part of the display name.
|
||||
groupLabels = ["A", "B", "C", "D", "E"];
|
||||
groupGroup = repmat("", size(displayNames));
|
||||
nameLower = lower(displayNames);
|
||||
groupGroup(startsWith(nameLower, "120 gb") | ...
|
||||
startsWith(nameLower, "144 gb") | startsWith(nameLower, "170 gb")) = "A";
|
||||
groupGroup(startsWith(nameLower, "optfil")) = "B";
|
||||
groupGroup(startsWith(nameLower, "thormax")) = "C";
|
||||
groupGroup(startsWith(nameLower, "thormax optfil")) = "E";
|
||||
groupGroup(startsWith(nameLower, "thormax optfil ohne fl")) = "D";
|
||||
|
||||
groupColors = [clr.Paired.red; clr.Paired.blue; clr.Paired.green; ...
|
||||
clr.Paired.orange; clr.Paired.purple];
|
||||
groupStyles = {"-", "--", ":", ":", ":"};
|
||||
groupMarkers = {"o", "s", "^", "d", "v"};
|
||||
|
||||
figOut = figure("Name", "opt_filt_analysis by " + upper(selectedEqType) + " and PAM");
|
||||
layout = tiledlayout(figOut, 1, 3, "TileSpacing", "compact", "Padding", "compact");
|
||||
|
||||
for k = 1:numel(pamLevels)
|
||||
ax = nexttile(layout);
|
||||
hold(ax, "on");
|
||||
|
||||
for lineIdx = find(pamGroup == pamLevels(k))'
|
||||
newLine = copyobj(srcLines(lineIdx), ax);
|
||||
groupIdx = find(groupLabels == groupGroup(lineIdx), 1);
|
||||
newLine.Color = groupColors(groupIdx, :);
|
||||
newLine.Marker = groupMarkers{groupIdx};
|
||||
newLine.LineWidth = 1;
|
||||
newLine.LineStyle = groupStyles{groupIdx};
|
||||
newLine.MarkerSize = 3;
|
||||
newLine.DisplayName = groupGroup(lineIdx);
|
||||
end
|
||||
|
||||
ax.XScale = srcAx.XScale;
|
||||
ax.YScale = srcAx.YScale;
|
||||
title(ax, upper(pamLevels(k)));
|
||||
xlabel(ax, srcAx.XLabel.String);
|
||||
ylabel(ax, srcAx.YLabel.String);
|
||||
grid(ax, "on");
|
||||
legend(ax, "show", "Interpreter", "none", "Location", "best");
|
||||
|
||||
beautifyBERplot("setcolors", false, "setmarkers", false);
|
||||
end
|
||||
|
||||
close(figIn);
|
||||
@@ -0,0 +1,430 @@
|
||||
%% Reprocess baudrate-sweep MAT files with dsp_400g_recipe
|
||||
% File naming convention:
|
||||
% PAMX_b2b_baudrate20241024_210648PAM_4_fsym_100_bits.mat
|
||||
% PAMX_b2b_baudrate20241024_210648PAM_4_fsym_100_symbols.mat
|
||||
% PAMX_b2b_baudrate20241024_210648PAM_4_fsym_100_rop_0_5_rx_signal.mat
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% Configuration
|
||||
|
||||
dataDir = "D:\baudrate_sweep_b2b";
|
||||
filePrefix = "PAMX_b2b_baudrate20241024_210648";
|
||||
outputFile = fullfile(dataDir, filePrefix + "_dsp400g_reprocessed_wh.mat");
|
||||
originalWarehouseFile = fullfile(dataDir, filePrefix + "_wh_final.mat");
|
||||
|
||||
pamLevels = [4, 6, 8];
|
||||
fsymGBd = 100:6:196;
|
||||
ropAtten = 0:0.5:7;
|
||||
% ropAtten = 0:0.5:7;
|
||||
|
||||
useParallel = true;
|
||||
batchSize = 24; % warehouse writes happen after each batch
|
||||
maxJobs = inf; % use a small number for smoke tests
|
||||
saveEveryBatches = 1;
|
||||
debugPlots = false;
|
||||
storeDspOutput = false; % full output objects can make the warehouse very large
|
||||
copyOriginalPowerMetadata = true;
|
||||
|
||||
recipeParams = struct( ...
|
||||
"run_ffe", true, ...
|
||||
"run_vnle_mlse", true, ...
|
||||
"run_dbtgt", true, ...
|
||||
"run_ml_mlse", true, ...
|
||||
"run_ml_mlse_db", false, ...
|
||||
"run_mlse_db", false, ...
|
||||
"plot_input_signal", false, ...
|
||||
"plot_output_signals", false);
|
||||
|
||||
%% Discover all file/acquisition jobs
|
||||
|
||||
jobs = buildJobList(dataDir, filePrefix, pamLevels, fsymGBd, ropAtten);
|
||||
if isempty(jobs)
|
||||
error("reprocess:NoJobs", "No processable RX acquisition jobs were found.");
|
||||
end
|
||||
|
||||
if isfinite(maxJobs)
|
||||
jobs = jobs(1:min(numel(jobs), maxJobs));
|
||||
end
|
||||
|
||||
maxAcquisitionIdx = max([jobs.acquisition_idx]);
|
||||
fprintf("Discovered %d acquisition jobs, max acquisition index S{%d}.\n", ...
|
||||
numel(jobs), maxAcquisitionIdx);
|
||||
|
||||
powerLookup = loadOriginalPowerLookup(originalWarehouseFile, ...
|
||||
copyOriginalPowerMetadata);
|
||||
|
||||
%% Build output warehouse
|
||||
|
||||
params = struct();
|
||||
params.fsym = fsymGBd .* 1e9;
|
||||
params.rop_atten = ropAtten;
|
||||
params.M = pamLevels;
|
||||
params.acquisition_idx = 1:maxAcquisitionIdx;
|
||||
|
||||
wh = DataStorage(params);
|
||||
wh.addStorage("ber_ffe");
|
||||
wh.addStorage("ber_mlse");
|
||||
wh.addStorage("ber_db");
|
||||
wh.addStorage("ber_ffe_precoded");
|
||||
wh.addStorage("ber_mlse_precoded");
|
||||
wh.addStorage("ber_db_precoded");
|
||||
wh.addStorage("rop");
|
||||
wh.addStorage("pd_in");
|
||||
wh.addStorage("rx_file");
|
||||
wh.addStorage("status");
|
||||
wh.addStorage("error_message");
|
||||
if storeDspOutput
|
||||
wh.addStorage("dsp_output");
|
||||
end
|
||||
|
||||
parallelEnabled = useParallel && ensureParallelPool();
|
||||
if parallelEnabled
|
||||
pool = gcp("nocreate");
|
||||
fprintf("Processing with parfor on %d workers.\n", pool.NumWorkers);
|
||||
else
|
||||
fprintf("Processing serially.\n");
|
||||
end
|
||||
|
||||
%% Process jobs in parallel batches, write warehouse serially
|
||||
|
||||
numBatches = ceil(numel(jobs) / batchSize);
|
||||
for batchIdx = 1:numBatches
|
||||
firstJob = (batchIdx - 1) * batchSize + 1;
|
||||
lastJob = min(batchIdx * batchSize, numel(jobs));
|
||||
batchJobs = jobs(firstJob:lastJob);
|
||||
batchResults = cell(numel(batchJobs), 1);
|
||||
|
||||
fprintf("Batch %d/%d: jobs %d-%d of %d\n", ...
|
||||
batchIdx, numBatches, firstJob, lastJob, numel(jobs));
|
||||
|
||||
if parallelEnabled
|
||||
parfor localIdx = 1:numel(batchJobs)
|
||||
batchResults{localIdx} = processOneJob( ...
|
||||
batchJobs(localIdx), recipeParams, debugPlots, storeDspOutput);
|
||||
end
|
||||
else
|
||||
for localIdx = 1:numel(batchJobs)
|
||||
batchResults{localIdx} = processOneJob( ...
|
||||
batchJobs(localIdx), recipeParams, debugPlots, storeDspOutput);
|
||||
end
|
||||
end
|
||||
|
||||
for localIdx = 1:numel(batchResults)
|
||||
wh = writeResultToWarehouse(wh, batchResults{localIdx}, ...
|
||||
storeDspOutput, powerLookup);
|
||||
end
|
||||
|
||||
if mod(batchIdx, saveEveryBatches) == 0 || batchIdx == numBatches
|
||||
save(outputFile, "wh", "recipeParams", "dataDir", "filePrefix", ...
|
||||
"jobs", "storeDspOutput", "copyOriginalPowerMetadata", ...
|
||||
"originalWarehouseFile", "-v7.3");
|
||||
fprintf("Saved checkpoint to %s\n", outputFile);
|
||||
end
|
||||
end
|
||||
|
||||
fprintf("Saved reprocessed warehouse to %s\n", outputFile);
|
||||
wh.showInfo;
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function jobs = buildJobList(dataDir, filePrefix, pamLevels, fsymGBd, ropAtten)
|
||||
jobs = struct( ...
|
||||
"M", {}, ...
|
||||
"fsym_GBd", {}, ...
|
||||
"fsym", {}, ...
|
||||
"rop_atten", {}, ...
|
||||
"acquisition_idx", {}, ...
|
||||
"bits_file", {}, ...
|
||||
"symbols_file", {}, ...
|
||||
"rx_file", {});
|
||||
|
||||
for M = pamLevels
|
||||
for fsymGb = fsymGBd
|
||||
fsym = fsymGb .* 1e9;
|
||||
bitsFile = fullfile(dataDir, sprintf("%sPAM_%d_fsym_%d_bits.mat", ...
|
||||
filePrefix, M, fsymGb));
|
||||
symbolsFile = fullfile(dataDir, sprintf("%sPAM_%d_fsym_%d_symbols.mat", ...
|
||||
filePrefix, M, fsymGb));
|
||||
|
||||
if ~isfile(bitsFile) || ~isfile(symbolsFile)
|
||||
warning("reprocess:MissingReference", ...
|
||||
"Skipping PAM-%d %.0f GBd: missing bits or symbols file.", ...
|
||||
M, fsymGb);
|
||||
continue
|
||||
end
|
||||
|
||||
for rop = ropAtten
|
||||
rxFile = fullfile(dataDir, sprintf("%sPAM_%d_fsym_%d_rop_%s_rx_signal.mat", ...
|
||||
filePrefix, M, fsymGb, ropToken(rop)));
|
||||
if ~isfile(rxFile)
|
||||
warning("reprocess:MissingRx", "Missing RX file: %s", rxFile);
|
||||
continue
|
||||
end
|
||||
|
||||
numAcquisitions = acquisitionCount(rxFile);
|
||||
for acqIdx = 1:numAcquisitions
|
||||
jobs(end+1) = struct( ... %#ok<AGROW>
|
||||
"M", M, ...
|
||||
"fsym_GBd", fsymGb, ...
|
||||
"fsym", fsym, ...
|
||||
"rop_atten", rop, ...
|
||||
"acquisition_idx", acqIdx, ...
|
||||
"bits_file", bitsFile, ...
|
||||
"symbols_file", symbolsFile, ...
|
||||
"rx_file", rxFile);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function numAcq = acquisitionCount(rxFile)
|
||||
info = whos("-file", rxFile, "S");
|
||||
if isempty(info)
|
||||
warning("reprocess:MissingS", "RX file has no variable S: %s", rxFile);
|
||||
numAcq = 0;
|
||||
else
|
||||
numAcq = prod(info.size);
|
||||
end
|
||||
end
|
||||
|
||||
function result = processOneJob(job, recipeParams, debugPlots, storeDspOutput)
|
||||
result = emptyJobResult(job);
|
||||
try
|
||||
bitsData = load(job.bits_file, "Bits");
|
||||
symbolsData = load(job.symbols_file, "Symbols");
|
||||
ScpeSigRaw = loadRxSignal(job.rx_file, job.acquisition_idx);
|
||||
dataTable = makeRecipeDataTable(job);
|
||||
|
||||
fprintf("PAM-%d, %.0f GBd, ROP atten %.1f dB, S{%d}\n", ...
|
||||
job.M, job.fsym_GBd, job.rop_atten, job.acquisition_idx);
|
||||
|
||||
dspOut = dsp_400g_recipe(ScpeSigRaw, symbolsData.Symbols, bitsData.Bits, ...
|
||||
"fsym", job.fsym, ...
|
||||
"M", job.M, ...
|
||||
"duob_mode", db_mode.no_db, ...
|
||||
"dataTable", dataTable, ...
|
||||
"userParameters", recipeParams, ...
|
||||
"debug_plots", debugPlots);
|
||||
|
||||
result.ber_ffe = extractBer(dspOut, "ffe_package", "BER");
|
||||
result.ber_mlse = extractBer(dspOut, "mlse_package", "BER");
|
||||
result.ber_db = extractBer(dspOut, "dbtgt_package", "BER");
|
||||
result.ber_ffe_precoded = extractBer(dspOut, "ffe_package", "BER_precoded");
|
||||
result.ber_mlse_precoded = extractBer(dspOut, "mlse_package", "BER_precoded");
|
||||
result.ber_db_precoded = extractBer(dspOut, "dbtgt_package", "BER_precoded");
|
||||
result.status = "ok";
|
||||
if storeDspOutput
|
||||
result.dsp_output = dspOut;
|
||||
end
|
||||
catch err
|
||||
result.status = "failed";
|
||||
result.error_message = string(err.message);
|
||||
warning("reprocess:DspFailed", ...
|
||||
"DSP failed for PAM-%d %.0f GBd ROP %.1f dB S{%d}: %s", ...
|
||||
job.M, job.fsym_GBd, job.rop_atten, job.acquisition_idx, err.message);
|
||||
end
|
||||
end
|
||||
|
||||
function result = emptyJobResult(job)
|
||||
result = struct( ...
|
||||
"M", job.M, ...
|
||||
"fsym", job.fsym, ...
|
||||
"fsym_GBd", job.fsym_GBd, ...
|
||||
"rop_atten", job.rop_atten, ...
|
||||
"acquisition_idx", job.acquisition_idx, ...
|
||||
"rx_file", job.rx_file, ...
|
||||
"ber_ffe", NaN, ...
|
||||
"ber_mlse", NaN, ...
|
||||
"ber_db", NaN, ...
|
||||
"ber_ffe_precoded", NaN, ...
|
||||
"ber_mlse_precoded", NaN, ...
|
||||
"ber_db_precoded", NaN, ...
|
||||
"status", "not_run", ...
|
||||
"error_message", "", ...
|
||||
"dsp_output", []);
|
||||
end
|
||||
|
||||
function wh = writeResultToWarehouse(wh, result, storeDspOutput, powerLookup)
|
||||
idx = {result.fsym, result.rop_atten, result.M, result.acquisition_idx};
|
||||
[ropValue, pdInValue] = lookupOriginalPower(powerLookup, ...
|
||||
result.fsym, result.rop_atten, result.M);
|
||||
|
||||
wh.addValueToStorage(result.ber_ffe, "ber_ffe", idx{:});
|
||||
wh.addValueToStorage(result.ber_mlse, "ber_mlse", idx{:});
|
||||
wh.addValueToStorage(result.ber_db, "ber_db", idx{:});
|
||||
wh.addValueToStorage(result.ber_ffe_precoded, "ber_ffe_precoded", idx{:});
|
||||
wh.addValueToStorage(result.ber_mlse_precoded, "ber_mlse_precoded", idx{:});
|
||||
wh.addValueToStorage(result.ber_db_precoded, "ber_db_precoded", idx{:});
|
||||
wh.addValueToStorage(ropValue, "rop", idx{:});
|
||||
wh.addValueToStorage(pdInValue, "pd_in", idx{:});
|
||||
wh.addValueToStorage(char(result.rx_file), "rx_file", idx{:});
|
||||
wh.addValueToStorage(char(result.status), "status", idx{:});
|
||||
wh.addValueToStorage(char(result.error_message), "error_message", idx{:});
|
||||
if storeDspOutput
|
||||
wh.addValueToStorage(result.dsp_output, "dsp_output", idx{:});
|
||||
end
|
||||
end
|
||||
|
||||
function powerLookup = loadOriginalPowerLookup(originalWarehouseFile, enabled)
|
||||
powerLookup = struct("enabled", false, "data", table());
|
||||
if ~enabled
|
||||
return
|
||||
end
|
||||
if ~isfile(originalWarehouseFile)
|
||||
warning("reprocess:MissingOriginalWarehouse", ...
|
||||
"Original warehouse not found, storing NaN for rop/pd_in: %s", ...
|
||||
originalWarehouseFile);
|
||||
return
|
||||
end
|
||||
|
||||
loadedData = load(originalWarehouseFile);
|
||||
if isfield(loadedData, "wh")
|
||||
originalWh = loadedData.wh;
|
||||
elseif isfield(loadedData, "obj")
|
||||
originalWh = loadedData.obj;
|
||||
else
|
||||
warning("reprocess:NoOriginalWarehouseObject", ...
|
||||
"Original warehouse file contains neither wh nor obj: %s", ...
|
||||
originalWarehouseFile);
|
||||
return
|
||||
end
|
||||
|
||||
requiredStorages = ["rop", "pd_in"];
|
||||
availableStorages = string(fieldnames(originalWh.sto));
|
||||
if ~all(ismember(requiredStorages, availableStorages))
|
||||
warning("reprocess:MissingPowerStorage", ...
|
||||
"Original warehouse has no complete rop/pd_in storage. Storing NaN.");
|
||||
return
|
||||
end
|
||||
|
||||
fsymVals = double(originalWh.parameter.fsym.values(:).');
|
||||
ropAttenVals = double(originalWh.parameter.rop_atten.values(:).');
|
||||
pamVals = double(originalWh.parameter.M.values(:).');
|
||||
rows = cell(numel(fsymVals) * numel(ropAttenVals) * numel(pamVals), 5);
|
||||
rowIdx = 0;
|
||||
|
||||
for pamLevel = pamVals
|
||||
for fsym = fsymVals
|
||||
for ropAtten = ropAttenVals
|
||||
rowIdx = rowIdx + 1;
|
||||
rows(rowIdx, :) = { ...
|
||||
fsym, ...
|
||||
ropAtten, ...
|
||||
pamLevel, ...
|
||||
getOriginalScalar(originalWh, "rop", fsym, ropAtten, pamLevel), ...
|
||||
getOriginalScalar(originalWh, "pd_in", fsym, ropAtten, pamLevel)};
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
powerLookup.enabled = true;
|
||||
powerLookup.data = cell2table(rows, 'VariableNames', ...
|
||||
["fsym", "rop_atten", "M", "rop", "pd_in"]);
|
||||
fprintf("Loaded %d original rop/pd_in metadata rows from %s\n", ...
|
||||
height(powerLookup.data), originalWarehouseFile);
|
||||
end
|
||||
|
||||
function value = getOriginalScalar(originalWh, storageName, fsym, ropAtten, pamLevel)
|
||||
value = NaN;
|
||||
try
|
||||
rawValue = originalWh.getStoValue(storageName, fsym, ropAtten, pamLevel);
|
||||
catch
|
||||
return
|
||||
end
|
||||
if isnumeric(rawValue) && isscalar(rawValue)
|
||||
value = double(rawValue);
|
||||
end
|
||||
end
|
||||
|
||||
function [ropValue, pdInValue] = lookupOriginalPower(powerLookup, fsym, ropAtten, pamLevel)
|
||||
ropValue = NaN;
|
||||
pdInValue = NaN;
|
||||
if ~powerLookup.enabled || isempty(powerLookup.data)
|
||||
return
|
||||
end
|
||||
|
||||
match = powerLookup.data.fsym == fsym & ...
|
||||
powerLookup.data.rop_atten == ropAtten & ...
|
||||
powerLookup.data.M == pamLevel;
|
||||
if ~any(match)
|
||||
return
|
||||
end
|
||||
|
||||
firstMatch = find(match, 1, "first");
|
||||
ropValue = powerLookup.data.rop(firstMatch);
|
||||
pdInValue = powerLookup.data.pd_in(firstMatch);
|
||||
end
|
||||
|
||||
function sig = loadRxSignal(rxFile, acquisitionIdx)
|
||||
rxData = load(rxFile, "S");
|
||||
if ~iscell(rxData.S) || isempty(rxData.S)
|
||||
error("RX file %s does not contain a nonempty cell array S.", rxFile);
|
||||
end
|
||||
if acquisitionIdx > numel(rxData.S)
|
||||
error("Requested acquisition S{%d}, but %s only contains %d acquisitions.", ...
|
||||
acquisitionIdx, rxFile, numel(rxData.S));
|
||||
end
|
||||
sig = rxData.S{acquisitionIdx};
|
||||
end
|
||||
|
||||
function token = ropToken(rop)
|
||||
token = strrep(sprintf("%.1f", rop), ".", "_");
|
||||
token = regexprep(token, "_0$", "");
|
||||
end
|
||||
|
||||
function dataTable = makeRecipeDataTable(job)
|
||||
dataTable = table();
|
||||
dataTable.run_id = makeRunId(job.M, job.fsym, job.rop_atten, job.acquisition_idx);
|
||||
dataTable.pam_level = job.M;
|
||||
dataTable.symbolrate = job.fsym;
|
||||
dataTable.bitrate = job.fsym .* log2(job.M);
|
||||
dataTable.grossrate = dataTable.bitrate;
|
||||
dataTable.fiber_length = 0;
|
||||
dataTable.wavelength = 1310;
|
||||
dataTable.rop_attenuation = job.rop_atten;
|
||||
dataTable.acquisition_idx = job.acquisition_idx;
|
||||
dataTable.rx_file = string(job.rx_file);
|
||||
end
|
||||
|
||||
function runId = makeRunId(M, fsym, rop, acquisitionIdx)
|
||||
runId = M .* 1e10 + round(fsym .* 1e-6) .* 1e2 + ...
|
||||
round(rop .* 10) .* 10 + acquisitionIdx;
|
||||
end
|
||||
|
||||
function ber = extractBer(dspOut, packageName, metricName)
|
||||
ber = NaN;
|
||||
if ~isfield(dspOut, packageName)
|
||||
return
|
||||
end
|
||||
pkg = dspOut.(packageName);
|
||||
if ~isfield(pkg, "metrics")
|
||||
return
|
||||
end
|
||||
|
||||
if isobject(pkg.metrics) && isprop(pkg.metrics, metricName)
|
||||
ber = pkg.metrics.(metricName);
|
||||
elseif isstruct(pkg.metrics) && isfield(pkg.metrics, metricName)
|
||||
ber = pkg.metrics.(metricName);
|
||||
end
|
||||
end
|
||||
|
||||
function ok = ensureParallelPool()
|
||||
ok = false;
|
||||
try
|
||||
pool = gcp("nocreate");
|
||||
if ~isempty(pool) && contains(string(class(pool)), "ThreadPool")
|
||||
delete(pool);
|
||||
pool = [];
|
||||
end
|
||||
if isempty(pool)
|
||||
pool = parpool("local");
|
||||
end
|
||||
ok = ~isempty(pool);
|
||||
catch err
|
||||
warning("reprocess:NoParallelPool", ...
|
||||
"Parallel pool unavailable, falling back to serial processing: %s", ...
|
||||
err.message);
|
||||
end
|
||||
end
|
||||
97
projects/Diss/400G_revisit/RX_sprectra.m
Normal file
97
projects/Diss/400G_revisit/RX_sprectra.m
Normal file
@@ -0,0 +1,97 @@
|
||||
|
||||
|
||||
|
||||
% === 400G DSP settings ===
|
||||
dsp_options = struct();
|
||||
dsp_options.mode = "run_id";
|
||||
dsp_options.recipe = @dsp_400g_recipe;
|
||||
dsp_options.append_to_db = false;
|
||||
% dsp_options.append_mpi_reduction_db = false;
|
||||
dsp_options.start_occurence = 1;
|
||||
dsp_options.max_occurences = 3;
|
||||
dsp_options.debug_plots = false;
|
||||
|
||||
|
||||
|
||||
dsp_options.database_type = "mysql";
|
||||
dsp_options.dataBase = "labor_highspeed";
|
||||
|
||||
if ismac
|
||||
dsp_options.storage_path = "/Volumes/media/labdata/sioe_labor";
|
||||
else
|
||||
dsp_options.storage_path = "W:\labdata\sioe_labor";
|
||||
end
|
||||
dsp_options.server = "192.168.178.192";
|
||||
dsp_options.port = 3306;
|
||||
dsp_options.user = "silas";
|
||||
dsp_options.password = "silas";
|
||||
|
||||
db = DBHandler("dataBase", [dsp_options.dataBase], ...
|
||||
"type", dsp_options.database_type, ...
|
||||
"server", dsp_options.server, ...
|
||||
"user", dsp_options.user, ...
|
||||
"password", dsp_options.password);
|
||||
|
||||
%% Load normal Signal w/o preemphasis
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','fiber_length','EQUALS', 10);
|
||||
fp.where('Runs','wavelength','EQUALS', 1310);
|
||||
fp.where('Runs','bitrate','EQUALS', 420e9);
|
||||
fp.where('Runs','pam_level','EQUALS', 4);
|
||||
fp.where('Runs','rop_attenuation','EQUALS', 0);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('Runs');
|
||||
[dataTable, query] = db.queryDB(fp, fields);
|
||||
|
||||
[~, Symbols_preemph, Scpe_cell_preemph, ~] = loadAndSyncRunSignals(dataTable(1,:), dsp_options);
|
||||
ScopeSignal = Scpe_cell_preemph{1};
|
||||
ScopeSignal_preemph = preprocessSignal(ScopeSignal, Symbols_preemph, Symbols_preemph.fs);
|
||||
|
||||
%% Load normal Signal w/o preemphasis
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','fiber_length','EQUALS', 10);
|
||||
fp.where('Runs','wavelength','EQUALS', 1310);
|
||||
fp.where('Runs','bitrate','EQUALS', 420e9);
|
||||
fp.where('Runs','pam_level','EQUALS', 4);
|
||||
fp.where('Runs','rop_attenuation','EQUALS', 0);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode','EQUALS', 1);
|
||||
|
||||
fields = db.getTableFieldNames('Runs');
|
||||
[dataTable, query] = db.queryDB(fp, fields);
|
||||
|
||||
[~, Symbols, Scpe_cell, ~] = loadAndSyncRunSignals(dataTable(1,:), dsp_options);
|
||||
ScopeSignal = Scpe_cell{1};
|
||||
|
||||
|
||||
|
||||
ScopeSignal_no_preemph = preprocessSignal(ScopeSignal, Symbols, Symbols.fs);
|
||||
|
||||
|
||||
|
||||
%% Duobinary
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','fiber_length','EQUALS', 10);
|
||||
fp.where('Runs','wavelength','EQUALS', 1310);
|
||||
fp.where('Runs','bitrate','EQUALS', 420e9);
|
||||
fp.where('Runs','pam_level','EQUALS', 4);
|
||||
fp.where('Runs','rop_attenuation','EQUALS', 0);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode','EQUALS', 2);
|
||||
|
||||
fields = db.getTableFieldNames('Runs');
|
||||
[dataTable, query] = db.queryDB(fp, fields);
|
||||
|
||||
[~, Symbols_db, Scpe_cell_db, found_sync] = loadAndSyncRunSignals(dataTable(1,:), dsp_options);
|
||||
ScopeSignal = Scpe_cell_db{1};
|
||||
ScopeSignal_DB = preprocessSignal(ScopeSignal, Symbols_db, Symbols_db.fs);
|
||||
|
||||
|
||||
%%
|
||||
|
||||
|
||||
ScopeSignal_no_preemph.spectrum("displayname",'Full Response w/o preemphasis','fignum',2,'normalizeTo0dB',0,'color',clr.Paired.dblue);
|
||||
ScopeSignal_preemph.spectrum("displayname",'Full Response w/ preemphasis','fignum',2,'normalizeTo0dB',0,'color',clr.Paired.dgreen);
|
||||
ScopeSignal_DB.spectrum("displayname",'DB Response w/ preemphasis','fignum',2,'normalizeTo0dB',0,'color',clr.Paired.dorange);
|
||||
115
projects/Diss/400G_revisit/TX_spectra.m
Normal file
115
projects/Diss/400G_revisit/TX_spectra.m
Normal file
@@ -0,0 +1,115 @@
|
||||
|
||||
rates = [420e9];
|
||||
rcalpha = 0.05;
|
||||
fsym = rates/2;
|
||||
apply_pulsef = 1;
|
||||
|
||||
M = 4;
|
||||
|
||||
|
||||
%% Normal Tx Signal
|
||||
duob_mode = db_mode.no_db;
|
||||
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
[Digi_sig,~,~] = PAMsource(...
|
||||
"fsym",fsym,"M",M,"order",21,"useprbs",0,...
|
||||
"fs_out",256e9,...
|
||||
"applyclipping",0,"clipfactor",1.5,...
|
||||
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||
"randkey",1,...
|
||||
'duobinary_mode',duob_mode,...
|
||||
"mrds_code",0,"mrds_blocklength",512).process();
|
||||
Digi_sig= Digi_sig.normalize("mode","oneone");
|
||||
Digi_sig = Digi_sig-mean(Digi_sig.signal);
|
||||
|
||||
|
||||
maxamp = -28; %optimized for DB!
|
||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
|
||||
precomp_path = "W:\labdata\sioe_labor\precomp";
|
||||
precomp_fn = "lab_high_speed";
|
||||
Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
Digi_sig_pre.signal = reshape(Digi_sig_pre.signal,[],1);
|
||||
Digi_sig_pre = Digi_sig_pre.resample("fs_out",256e9);
|
||||
Digi_sig_pre= Digi_sig_pre.normalize("mode","oneone");
|
||||
Digi_sig_pre = Digi_sig_pre-mean(Digi_sig_pre.signal);
|
||||
|
||||
|
||||
|
||||
% precomp_est.plot
|
||||
Digi_sig_rx = precomp_est.apply(...
|
||||
Digi_sig,'maxampdb',0,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
|
||||
Digi_sig_pre_rx = precomp_est.apply(...
|
||||
Digi_sig_pre,'maxampdb',0,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
|
||||
Digi_sig.spectrum(...
|
||||
"displayname",'Tx w/o pre-emphasis',...
|
||||
"fignum",2,"normalizeTo0dB",0,"color",[0,0,0]);
|
||||
Digi_sig_pre.spectrum(...
|
||||
"displayname",'Tx w/ pre-emphasis',...
|
||||
"fignum",2,"normalizeTo0dB",0,"color",clr.Paired.lblue);
|
||||
|
||||
Digi_sig_rx.spectrum(...
|
||||
"displayname",'Rx w/o pre-emphasis',...
|
||||
"fignum",2,"normalizeTo0dB",0,"color",clr.Paired.dred);
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Digi_sig_pre_rx.spectrum(...
|
||||
"displayname",'Rx w/ pre-emphasis',...
|
||||
"fignum",2,"normalizeTo0dB",0,"color",clr.Paired.dblue);
|
||||
|
||||
|
||||
|
||||
|
||||
%% Duobinary Encoded Tx Signal
|
||||
duob_mode = db_mode.db_encoded;
|
||||
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
[Digi_sig_DB,Symbols,Tx_bits] = PAMsource(...
|
||||
"fsym",fsym,"M",M,"order",21,"useprbs",0,...
|
||||
"fs_out",256e9,...
|
||||
"applyclipping",0,"clipfactor",1.5,...
|
||||
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||
"randkey",1,...
|
||||
'duobinary_mode',duob_mode,...
|
||||
"mrds_code",0,"mrds_blocklength",512).process();
|
||||
Digi_sig_DB= Digi_sig_DB.normalize("mode","oneone");
|
||||
|
||||
maxamp = -38; %optimized for DB!
|
||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig_DB.fs);
|
||||
precomp_path = "W:\labdata\sioe_labor\precomp";
|
||||
precomp_fn = "lab_high_speed";
|
||||
Digi_sig_DB_pre = precomp_est.precomp(Digi_sig_DB,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
Digi_sig_DB_pre.signal = reshape(Digi_sig_DB_pre.signal,[],1);
|
||||
Digi_sig_DB_pre = Digi_sig_DB_pre.resample("fs_out",256e9);
|
||||
Digi_sig_DB_pre = Digi_sig_DB_pre.normalize("mode","oneone");
|
||||
|
||||
%%
|
||||
|
||||
AWG_ = M8199B("kover",4);
|
||||
% AWG_ = AWG("fdac",256e9,"f_cutoff",fsym,"lpf_active",0,"kover",4,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1);
|
||||
|
||||
|
||||
|
||||
El_sig = AWG_.process(Digi_sig);
|
||||
El_sig_preemph = AWG_.process(Digi_sig_pre);
|
||||
El_sig_channel = AWG_.process(Digi_sig_channel);
|
||||
|
||||
|
||||
|
||||
El_sig.spectrum("displayname",'Full Response w/o preemphasis','fignum',1,'normalizeTo0dB',0,'color',clr.Paired.lblue);
|
||||
El_sig_preemph.spectrum("displayname",'Full Response w/ preemphasis','fignum',1,'normalizeTo0dB',0,'color',clr.Paired.dblue);
|
||||
El_sig_channel.spectrum("displayname",'Full Response w/ measured channel','fignum',1,'normalizeTo0dB',0,'color',clr.Paired.blue);
|
||||
|
||||
|
||||
El_sig_DB_preemph = AWG_.process(Digi_sig_DB_pre);
|
||||
El_sig_DB = AWG_.process(Digi_sig_DB);
|
||||
|
||||
|
||||
El_sig_DB.spectrum("displayname",'DB Response w/o preemphasis','fignum',1,'normalizeTo0dB',0,'color',clr.Paired.lorange);
|
||||
El_sig_DB_preemph.spectrum("displayname",'DB Response w/ preemphasis','fignum',1,'normalizeTo0dB',0,'color',clr.Paired.dorange);
|
||||
BIN
projects/Diss/400G_revisit/auswertung_baudrate.mlx
Normal file
BIN
projects/Diss/400G_revisit/auswertung_baudrate.mlx
Normal file
Binary file not shown.
@@ -5,9 +5,11 @@ dsp_options.recipe = @dsp_400g_recipe;
|
||||
dsp_options.append_to_db = false;
|
||||
% dsp_options.append_mpi_reduction_db = false;
|
||||
dsp_options.start_occurence = 1;
|
||||
dsp_options.max_occurences = 1;
|
||||
dsp_options.max_occurences = 3;
|
||||
dsp_options.debug_plots = false;
|
||||
|
||||
|
||||
|
||||
dsp_options.database_type = "mysql";
|
||||
dsp_options.dataBase = "labor_highspeed";
|
||||
|
||||
@@ -34,15 +36,15 @@ maxRunIds = 1; % keep small until the recipe settings are sett
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','fiber_length','EQUALS', 10);
|
||||
fp.where('Runs','wavelength','EQUALS', 1310);
|
||||
fp.where('Runs','bitrate','EQUALS', 330e9);
|
||||
fp.where('Runs','pam_level','EQUALS', 4);
|
||||
% fp.where('Runs','bitrate','LESS_THAN', 480e9);
|
||||
% fp.where('Runs','pam_level','EQUALS', 4);
|
||||
fp.where('Runs','rop_attenuation','EQUALS', 0);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode','EQUALS', 2);
|
||||
% fp.where('Runs', 'db_mode','EQUALS', 2);
|
||||
|
||||
fields = db.getTableFieldNames('Runs');
|
||||
[dataTable, query] = db.queryDB(fp, fields);
|
||||
disp(query);
|
||||
% disp(query);
|
||||
|
||||
dataTable = sortrows(dataTable, {'bitrate', 'run_id'});
|
||||
|
||||
@@ -60,21 +62,12 @@ fprintf("Selected %d run_id(s): %s\n", numel(run_ids), mat2str(run_ids));
|
||||
%% Parameter sweep
|
||||
|
||||
dsp_options.userParameters = struct();
|
||||
dsp_options.userParameters.run_ml_mlse_db = true;
|
||||
dsp_options.userParameters.run_mlse_db = true;
|
||||
|
||||
% Enable/disable equalizer branches.
|
||||
% dsp_options.userParameters.run_ffe = false;
|
||||
% dsp_options.userParameters.run_vnle = false;
|
||||
% dsp_options.userParameters.run_dfe = false;
|
||||
% dsp_options.userParameters.run_vnle_mlse = true;
|
||||
% dsp_options.userParameters.run_dbtgt = true;
|
||||
|
||||
% Examples for parameter loops. DataStorage expands every vector-valued field.
|
||||
% dsp_options.userParameters.len_tr = 4096*2;
|
||||
% dsp_options.userParameters.pf_ncoeffs = 1;
|
||||
% dsp_options.userParameters.pf_ncoeffs = [1,2,3];
|
||||
|
||||
% dsp_options.userParameters.decoding_mode = [db_decoder.memoryless,db_decoder.sequencedetection];
|
||||
dsp_options.userParameters.decoding_mode = [db_decoder.sequencedetection];
|
||||
% dsp_options.userParameters.pf_ncoeffs = [1, 2, 3];
|
||||
% dsp_options.userParameters.mu_dc = [0, 1e-5, 1e-4];
|
||||
% dsp_options.userParameters.run_ml_mlse_db = [false, true];
|
||||
@@ -93,7 +86,7 @@ fprintf("-> [ %d run_id(s) x %d userParam combination(s) = %d job(s) ] x %d real
|
||||
|
||||
%% Run
|
||||
|
||||
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.serial, ...
|
||||
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.parallel, ...
|
||||
"wh", wh, ...
|
||||
"waitbar", true);
|
||||
|
||||
@@ -102,6 +95,7 @@ fprintf("-> [ %d run_id(s) x %d userParam combination(s) = %d job(s) ] x %d real
|
||||
|
||||
printBerSummary(wh);
|
||||
plotBerVsBitrateQuick(wh, dataTable);
|
||||
plotMlseBerPrecodedVsBitrateByPf(wh, dataTable);
|
||||
|
||||
function printBerSummary(wh)
|
||||
storageNames = fieldnames(wh.sto);
|
||||
@@ -182,7 +176,7 @@ for storageIdx = 1:numel(storageNames)
|
||||
end
|
||||
|
||||
modeData = sortrows(plotData(rowMask, :), "bitrate_Gbps");
|
||||
summaryData = groupsummary(modeData, "bitrate_Gbps", "median", "BER");
|
||||
summaryData = groupsummary(modeData, "bitrate_Gbps", "min", "BER");
|
||||
summaryData = sortrows(summaryData, "bitrate_Gbps");
|
||||
color = quickPlotColor(modeIdx);
|
||||
marker = markers(1 + mod(storageIdx - 1, numel(markers)));
|
||||
@@ -196,7 +190,7 @@ for storageIdx = 1:numel(storageNames)
|
||||
"MarkerEdgeAlpha", 0.25, ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
plot(ax, summaryData.bitrate_Gbps, summaryData.median_BER, ...
|
||||
plot(ax, summaryData.bitrate_Gbps, summaryData.min_BER, ...
|
||||
"LineStyle", lineStyles(metricIdx), ...
|
||||
"Marker", marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
@@ -279,6 +273,108 @@ if ~isempty(plotData)
|
||||
end
|
||||
end
|
||||
|
||||
function plotMlseBerPrecodedVsBitrateByPf(wh, dataTable)
|
||||
storageName = "mlse_package";
|
||||
if ~isfield(wh.sto, storageName)
|
||||
fprintf("No %s storage available for BERp plot.\n", storageName);
|
||||
return
|
||||
end
|
||||
|
||||
runIds = dataTable.run_id(:);
|
||||
bitrates = dataTable.bitrate(:);
|
||||
pfCol = zeros(0, 1);
|
||||
bitrateCol = zeros(0, 1);
|
||||
berpCol = zeros(0, 1);
|
||||
|
||||
storageValues = wh.sto.(storageName);
|
||||
for linIdx = 1:numel(storageValues)
|
||||
[phys, storedValue] = wh.getPhysAndValueByLinIndex(storageName, linIdx);
|
||||
if ~isfield(phys, "pf_ncoeffs")
|
||||
continue
|
||||
end
|
||||
|
||||
berp = extractMinMetricValue(storedValue, "BER_precoded");
|
||||
if ~isfinite(berp) || berp <= 0
|
||||
continue
|
||||
end
|
||||
|
||||
runId = resolveRunId(phys, runIds);
|
||||
bitrate = resolveBitrate(runId, runIds, bitrates);
|
||||
if ~isfinite(bitrate)
|
||||
continue
|
||||
end
|
||||
|
||||
pfCol(end+1, 1) = double(phys.pf_ncoeffs); %#ok<AGROW>
|
||||
bitrateCol(end+1, 1) = double(bitrate) * 1e-9; %#ok<AGROW>
|
||||
berpCol(end+1, 1) = double(berp); %#ok<AGROW>
|
||||
end
|
||||
|
||||
if isempty(berpCol)
|
||||
fprintf("No BERp values available for %s.\n", storageName);
|
||||
return
|
||||
end
|
||||
|
||||
plotData = table(pfCol, bitrateCol, berpCol, ...
|
||||
'VariableNames', ["pf_ncoeffs", "bitrate_Gbps", "BERp"]);
|
||||
summaryData = groupsummary(plotData, ["pf_ncoeffs", "bitrate_Gbps"], ...
|
||||
"min", "BERp");
|
||||
|
||||
fig = figure(403); clf;
|
||||
ax = axes(fig); hold(ax, "on");
|
||||
pfValues = [1, 2, 3];
|
||||
markers = ["o", "square", "diamond"];
|
||||
colors = lines(numel(pfValues));
|
||||
|
||||
for pfIdx = 1:numel(pfValues)
|
||||
pfValue = pfValues(pfIdx);
|
||||
rowMask = summaryData.pf_ncoeffs == pfValue;
|
||||
if ~any(rowMask)
|
||||
continue
|
||||
end
|
||||
|
||||
curveData = sortrows(summaryData(rowMask, :), "bitrate_Gbps");
|
||||
plot(ax, curveData.bitrate_Gbps, curveData.min_BERp, ...
|
||||
"LineWidth", 1.3, ...
|
||||
"Marker", markers(pfIdx), ...
|
||||
"MarkerSize", 5, ...
|
||||
"Color", colors(pfIdx, :), ...
|
||||
"DisplayName", sprintf("pf\\_ncoeffs = %d", pfValue));
|
||||
end
|
||||
|
||||
xlabel(ax, "Bitrate [Gb/s]");
|
||||
ylabel(ax, "min BERp");
|
||||
title(ax, "MLSE BERp vs bitrate");
|
||||
set(ax, "YScale", "log");
|
||||
grid(ax, "on");
|
||||
box(ax, "on");
|
||||
legend(ax, "Location", "best", "Interpreter", "none");
|
||||
set(fig, "Position", [1200, 450, 560, 360]);
|
||||
end
|
||||
|
||||
function minValue = extractMinMetricValue(value, metricName)
|
||||
minValue = NaN;
|
||||
if isempty(value)
|
||||
return
|
||||
end
|
||||
if ~iscell(value)
|
||||
value = {value};
|
||||
end
|
||||
|
||||
metricValues = NaN(1, numel(value));
|
||||
for packageIdx = 1:numel(value)
|
||||
package = value{packageIdx};
|
||||
if ~isstruct(package) || ~isfield(package, "metrics")
|
||||
continue
|
||||
end
|
||||
metricValues(packageIdx) = readMetricValue(package.metrics, metricName);
|
||||
end
|
||||
|
||||
metricValues = metricValues(isfinite(metricValues) & metricValues > 0);
|
||||
if ~isempty(metricValues)
|
||||
minValue = min(metricValues);
|
||||
end
|
||||
end
|
||||
|
||||
function metricRows = extractBerMetricRows(values)
|
||||
metricNames = strings(0, 1);
|
||||
berValues = zeros(0, 1);
|
||||
|
||||
BIN
projects/Diss/400G_revisit/mlse_n_tap_pam4.mat
Normal file
BIN
projects/Diss/400G_revisit/mlse_n_tap_pam4.mat
Normal file
Binary file not shown.
BIN
projects/Diss/400G_revisit/mlse_n_tap_pam4_2km.mat
Normal file
BIN
projects/Diss/400G_revisit/mlse_n_tap_pam4_2km.mat
Normal file
Binary file not shown.
BIN
projects/Diss/400G_revisit/opt_filt_analysis.fig
Normal file
BIN
projects/Diss/400G_revisit/opt_filt_analysis.fig
Normal file
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
BIN
projects/Diss/400G_revisit/rop_vs_baudrate_sweep.mat
Normal file
BIN
projects/Diss/400G_revisit/rop_vs_baudrate_sweep.mat
Normal file
Binary file not shown.
@@ -0,0 +1,274 @@
|
||||
%AMPLIFIER_GAIN_ANALYSIS Compare the Aeon SOA and Thorlabs PDFA gain.
|
||||
%
|
||||
% The first figure shows gain versus wavelength for the pump levels that
|
||||
% are available in the wavelength-sweep warehouses. The second figure
|
||||
% shows gain versus pump level at -10 dBm input power and 1310 nm. The
|
||||
% third and fourth figures show output power and OSNR versus wavelength.
|
||||
% The fifth figure estimates the optical noise figure from the ASE level.
|
||||
%
|
||||
% The 2 dB setup correction follows amplifier_input_output_curve.m:
|
||||
% gain = Pout - (Pin - 2 dB) = Pout - Pin + 2 dB.
|
||||
|
||||
scriptDir = fileparts(mfilename("fullpath"));
|
||||
repoDir = fileparts(fileparts(scriptDir));
|
||||
warehouseClassDir = fullfile(repoDir, "Classes", "Warehouse_class", "classes");
|
||||
if ~contains(string(path), warehouseClassDir)
|
||||
addpath(warehouseClassDir);
|
||||
end
|
||||
|
||||
inputPowerDb = -10;
|
||||
inputPowerCorrectionDb = 2;
|
||||
correctedInputPowerDb = inputPowerDb - inputPowerCorrectionDb;
|
||||
wavelengthForPumpSweepNm = 1310;
|
||||
osaResolutionNm = 0.1;
|
||||
|
||||
% Wavelength-dependent measurements: pump values are 50, 75, and 100 %.
|
||||
lambdaSweepAeon = load(fullfile(scriptDir, "aeon_soa_measurement_lambda_plaser_pump.mat"), "wh");
|
||||
lambdaSweepThorlabs = load(fullfile(scriptDir, "thorlabs_pdfa_measurement_lambda_plaser_pump.mat"), "wh");
|
||||
whAeonLambda = lambdaSweepAeon.wh;
|
||||
whThorlabsLambda = lambdaSweepThorlabs.wh;
|
||||
|
||||
% Pump-level measurements: pump values are available from 0 to 100 % in 5 % steps.
|
||||
pumpSweepAeon = load(fullfile(scriptDir, "aeon_soa_measurement_pump_level_sweep.mat"), "wh");
|
||||
pumpSweepThorlabs = load(fullfile(scriptDir, "thorlabs_pdfa_measurement_pump_level_sweep.mat"), "wh");
|
||||
whAeonPump = pumpSweepAeon.wh;
|
||||
whThorlabsPump = pumpSweepThorlabs.wh;
|
||||
|
||||
if ~ismember(inputPowerDb, whAeonLambda.parameter.laserpower.values)
|
||||
error("Input power %g dBm is not present in the wavelength-sweep warehouse.", inputPowerDb);
|
||||
end
|
||||
if ~ismember(wavelengthForPumpSweepNm, whAeonPump.parameter.lambda.values)
|
||||
error("Wavelength %g nm is not present in the pump-sweep warehouse.", wavelengthForPumpSweepNm);
|
||||
end
|
||||
|
||||
% The warehouse uses physical values as query arguments. Vector queries
|
||||
% return one row per requested physical value.
|
||||
wavelengthsNm = whAeonLambda.parameter.lambda.values;
|
||||
pumpLevelsLambda = whAeonLambda.parameter.pump.values;
|
||||
pumpLevelsSweep = whAeonPump.parameter.pump.values;
|
||||
|
||||
%% Gain versus wavelength
|
||||
|
||||
figure("Name", "Amplifier gain versus wavelength", "Color", "w");
|
||||
tiledlayout(1, 2, "TileSpacing", "compact", "Padding", "compact");
|
||||
|
||||
lambdaWarehouses = {whAeonLambda, whThorlabsLambda};
|
||||
amplifierNames = ["Aeon SOA", "Thorlabs PDFA"];
|
||||
|
||||
for amplifierIndex = 1:numel(lambdaWarehouses)
|
||||
nexttile;
|
||||
hold on;
|
||||
colors = lines(numel(pumpLevelsLambda));
|
||||
wh = lambdaWarehouses{amplifierIndex};
|
||||
|
||||
for pumpIndex = 1:numel(pumpLevelsLambda)
|
||||
pumpLevel = pumpLevelsLambda(pumpIndex);
|
||||
|
||||
signalOutputDb = wh.getStoValue("psig_osa", inputPowerDb, wavelengthsNm, pumpLevel);
|
||||
totalOutputDb = wh.getStoValue("psig_total", inputPowerDb, wavelengthsNm, pumpLevel);
|
||||
|
||||
signalGainDb = signalOutputDb - correctedInputPowerDb;
|
||||
totalGainDb = totalOutputDb - correctedInputPowerDb;
|
||||
|
||||
plot(wavelengthsNm, signalGainDb, "-", "Color", colors(pumpIndex, :), ...
|
||||
"LineWidth", 1.5, ...
|
||||
"DisplayName", sprintf("Pump %g%%: signal", pumpLevel));
|
||||
plot(wavelengthsNm, totalGainDb, "--", "Color", colors(pumpIndex, :), ...
|
||||
"LineWidth", 1.2, ...
|
||||
"DisplayName", sprintf("Pump %g%%: total", pumpLevel));
|
||||
end
|
||||
|
||||
title(amplifierNames(amplifierIndex), "Interpreter", "none");
|
||||
xlabel("Wavelength [nm]", "Interpreter", "none");
|
||||
ylabel("Gain [dB]", "Interpreter", "none");
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
sgtitle(sprintf("Gain versus wavelength at P_{in} = %g dBm (2 dB setup correction)", inputPowerDb), ...
|
||||
"Interpreter", "tex");
|
||||
|
||||
%% Gain versus pump level at -10 dBm input power
|
||||
|
||||
figure("Name", "Amplifier gain versus pump level", "Color", "w");
|
||||
hold on;
|
||||
colors = lines(numel(lambdaWarehouses));
|
||||
pumpWarehouses = {whAeonPump, whThorlabsPump};
|
||||
|
||||
for amplifierIndex = 1:numel(pumpWarehouses)
|
||||
wh = pumpWarehouses{amplifierIndex};
|
||||
|
||||
signalOutputDb = wh.getStoValue("psig_osa", inputPowerDb, wavelengthForPumpSweepNm, pumpLevelsSweep);
|
||||
totalOutputDb = wh.getStoValue("psig_total", inputPowerDb, wavelengthForPumpSweepNm, pumpLevelsSweep);
|
||||
|
||||
signalGainDb = signalOutputDb - correctedInputPowerDb;
|
||||
totalGainDb = totalOutputDb - correctedInputPowerDb;
|
||||
|
||||
plot(pumpLevelsSweep, signalGainDb, "-", "Color", colors(amplifierIndex, :), ...
|
||||
"LineWidth", 1.6, ...
|
||||
"DisplayName", amplifierNames(amplifierIndex) + " - signal");
|
||||
plot(pumpLevelsSweep, totalGainDb, "--", "Color", colors(amplifierIndex, :), ...
|
||||
"LineWidth", 1.3, ...
|
||||
"DisplayName", amplifierNames(amplifierIndex) + " - total");
|
||||
end
|
||||
|
||||
title(sprintf("Gain versus pump level at P_{in} = %g dBm, %g nm", ...
|
||||
inputPowerDb, wavelengthForPumpSweepNm), "Interpreter", "tex");
|
||||
xlabel("Pump level [percent]", "Interpreter", "none");
|
||||
ylabel("Gain [dB]", "Interpreter", "none");
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "best", "Interpreter", "none");
|
||||
|
||||
%% Output power versus wavelength
|
||||
|
||||
figure("Name", "Amplifier output power versus wavelength", "Color", "w");
|
||||
tiledlayout(1, 2, "TileSpacing", "compact", "Padding", "compact");
|
||||
|
||||
for amplifierIndex = 1:numel(lambdaWarehouses)
|
||||
nexttile;
|
||||
hold on;
|
||||
colors = lines(numel(pumpLevelsLambda));
|
||||
wh = lambdaWarehouses{amplifierIndex};
|
||||
legendHandles = gobjects(numel(pumpLevelsLambda), 1);
|
||||
|
||||
for pumpIndex = 1:numel(pumpLevelsLambda)
|
||||
pumpLevel = pumpLevelsLambda(pumpIndex);
|
||||
|
||||
signalOutputDb = wh.getStoValue("psig_osa", inputPowerDb, wavelengthsNm, pumpLevel);
|
||||
totalOutputDb = wh.getStoValue("psig_total", inputPowerDb, wavelengthsNm, pumpLevel);
|
||||
|
||||
plot(wavelengthsNm, signalOutputDb, "-", "Color", colors(pumpIndex, :), ...
|
||||
"LineWidth", 1.5, ...
|
||||
"DisplayName", sprintf("Pump %g%%: signal", pumpLevel));
|
||||
plot(wavelengthsNm, totalOutputDb, "--", "Color", colors(pumpIndex, :), ...
|
||||
"LineWidth", 1.2, ...
|
||||
"DisplayName", sprintf("Pump %g%%: total", pumpLevel));
|
||||
end
|
||||
|
||||
title(amplifierNames(amplifierIndex), "Interpreter", "none");
|
||||
xlabel("Wavelength [nm]", "Interpreter", "none");
|
||||
ylabel("P_{out} [dBm]", "Interpreter", "tex");
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
sgtitle(sprintf("Output power versus wavelength at P_{in} = %g dBm", inputPowerDb), ...
|
||||
"Interpreter", "tex");
|
||||
|
||||
%% OSNR versus wavelength
|
||||
|
||||
figure("Name", "Amplifier OSNR versus wavelength", "Color", "w");
|
||||
tiledlayout(1, 2, "TileSpacing", "compact", "Padding", "compact");
|
||||
|
||||
for amplifierIndex = 1:numel(lambdaWarehouses)
|
||||
nexttile;
|
||||
hold on;
|
||||
colors = lines(numel(pumpLevelsLambda));
|
||||
wh = lambdaWarehouses{amplifierIndex};
|
||||
|
||||
for pumpIndex = 1:numel(pumpLevelsLambda)
|
||||
pumpLevel = pumpLevelsLambda(pumpIndex);
|
||||
|
||||
% osnr_osa is calculated from spectrum_osa during measurement.
|
||||
osnrResults = wh.getStoValue("osnr_osa", inputPowerDb, wavelengthsNm, pumpLevel);
|
||||
osnrDb = cellfun(@(result) result.corrected_dB, osnrResults);
|
||||
|
||||
plot(wavelengthsNm, osnrDb, "-", "Color", colors(pumpIndex, :), ...
|
||||
"LineWidth", 1.5, ...
|
||||
"DisplayName", sprintf("Pump %g%%", pumpLevel));
|
||||
end
|
||||
|
||||
title(amplifierNames(amplifierIndex), "Interpreter", "none");
|
||||
xlabel("Wavelength [nm]", "Interpreter", "none");
|
||||
ylabel("OSNR [dB]", "Interpreter", "none");
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
sgtitle(sprintf("OSNR versus wavelength at P_{in} = %g dBm", inputPowerDb), ...
|
||||
"Interpreter", "tex");
|
||||
|
||||
%% Noise figure versus wavelength
|
||||
|
||||
% The stored pase_osa values are OSA power readings per resolution
|
||||
% bandwidth. The measurement script configured the OSA to 0.1 nm.
|
||||
hPlanck = 6.62607015e-34;
|
||||
speedOfLight = 299792458;
|
||||
lambdaMeters = wavelengthsNm(:) .* 1e-9;
|
||||
opticalFrequencyHz = speedOfLight ./ lambdaMeters;
|
||||
opticalNoiseBandwidthHz = speedOfLight ./ lambdaMeters.^2 .* (osaResolutionNm * 1e-9);
|
||||
quantumNoisePowerW = hPlanck .* opticalFrequencyHz .* opticalNoiseBandwidthHz;
|
||||
|
||||
figure("Name", "Amplifier noise figure versus wavelength", "Color", "w");
|
||||
tiledlayout(1, 2, "TileSpacing", "compact", "Padding", "compact");
|
||||
|
||||
for amplifierIndex = 1:numel(lambdaWarehouses)
|
||||
nexttile;
|
||||
hold on;
|
||||
colors = lines(numel(pumpLevelsLambda));
|
||||
wh = lambdaWarehouses{amplifierIndex};
|
||||
|
||||
for pumpIndex = 1:numel(pumpLevelsLambda)
|
||||
pumpLevel = pumpLevelsLambda(pumpIndex);
|
||||
|
||||
signalOutputDb = wh.getStoValue("psig_osa", inputPowerDb, wavelengthsNm, pumpLevel);
|
||||
aseOutputDb = wh.getStoValue("pase_osa", inputPowerDb, wavelengthsNm, pumpLevel);
|
||||
|
||||
gainDb = signalOutputDb - correctedInputPowerDb;
|
||||
linearGain = 10.^(gainDb ./ 10);
|
||||
asePowerW = 10.^((aseOutputDb - 30) ./ 10);
|
||||
|
||||
% Exact estimate including the amplified input shot-noise term.
|
||||
noiseFactor = 1 ./ linearGain + ...
|
||||
asePowerW ./ (linearGain .* quantumNoisePowerW);
|
||||
noiseFigureDb = 10 .* log10(noiseFactor);
|
||||
|
||||
legendHandles(pumpIndex) = plot(wavelengthsNm(:), noiseFigureDb, "-", ...
|
||||
"Color", colors(pumpIndex, :), ...
|
||||
"LineWidth", 1.5, ...
|
||||
"DisplayName", sprintf("Pump %g%%", pumpLevel));
|
||||
end
|
||||
|
||||
title(amplifierNames(amplifierIndex), "Interpreter", "none");
|
||||
xlabel("Wavelength [nm]", "Interpreter", "none");
|
||||
ylabel("Noise figure [dB]", "Interpreter", "none");
|
||||
grid on;
|
||||
box on;
|
||||
legend(legendHandles, "Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
sgtitle(sprintf("Estimated noise figure versus wavelength at P_{in} = %g dBm", inputPowerDb), ...
|
||||
"Interpreter", "tex");
|
||||
|
||||
%% Final figure: Thorlabs gain versus wavelength
|
||||
|
||||
figure("Name", "Thorlabs PDFA gain versus wavelength", "Color", "w");
|
||||
hold on;
|
||||
colors = lines(numel(pumpLevelsLambda));
|
||||
gainHandles = gobjects(numel(pumpLevelsLambda), 1);
|
||||
|
||||
for pumpIndex = 1:numel(pumpLevelsLambda)
|
||||
pumpLevel = pumpLevelsLambda(pumpIndex);
|
||||
|
||||
signalOutputDb = whThorlabsLambda.getStoValue( ...
|
||||
"psig_osa", inputPowerDb, wavelengthsNm, pumpLevel);
|
||||
|
||||
gainDb = signalOutputDb - correctedInputPowerDb;
|
||||
|
||||
gainHandles(pumpIndex) = plot(wavelengthsNm, gainDb, "-", ...
|
||||
"Color", colors(pumpIndex, :), ...
|
||||
"LineWidth", 1.6, ...
|
||||
"DisplayName", sprintf("Pump %g%%: gain", pumpLevel));
|
||||
end
|
||||
|
||||
title(sprintf("Thorlabs PDFA: gain versus wavelength at P_{in} = %g dBm", ...
|
||||
inputPowerDb), "Interpreter", "tex");
|
||||
xlabel("Wavelength [nm]", "Interpreter", "none");
|
||||
ylabel("Gain [dB]", "Interpreter", "none");
|
||||
grid on;
|
||||
box on;
|
||||
legend(gainHandles, "Location", "best", "Interpreter", "none");
|
||||
@@ -0,0 +1,87 @@
|
||||
%AVAILABLE_OUTPUT_POWER_VS_WAVELENGTH Plot achieved laser output power.
|
||||
%
|
||||
% laser_outputpower_sweep_2_many_averages.mat contains the requested
|
||||
% wavelength and laser-power values. The corresponding 20-trace
|
||||
% measurements are stored in the local FIG file and are averaged here.
|
||||
|
||||
scriptDir = fileparts(mfilename("fullpath"));
|
||||
|
||||
parameterData = load(fullfile(scriptDir, "laser_outputpower_sweep_2_many_averages.mat"));
|
||||
laserparams = parameterData.laserparams;
|
||||
averagesPerSetpoint = 20;
|
||||
|
||||
measurementFigure = openfig( ...
|
||||
fullfile(scriptDir, "available_output_power_exfo_t100_oband_20_averages.fig"), ...
|
||||
"invisible");
|
||||
measurementAxes = findall(measurementFigure, "Type", "axes");
|
||||
measurementLines = findall(measurementAxes, "Type", "line");
|
||||
|
||||
numberOfSetpoints = numel(laserparams.laserpower);
|
||||
numberOfWavelengths = numel(laserparams.lambda);
|
||||
expectedLineCount = numberOfSetpoints * averagesPerSetpoint;
|
||||
if numel(measurementLines) ~= expectedLineCount
|
||||
close(measurementFigure);
|
||||
error("Expected %d measurement traces, found %d.", ...
|
||||
expectedLineCount, numel(measurementLines));
|
||||
end
|
||||
|
||||
measuredLambda = measurementLines(1).XData(:).';
|
||||
if numel(measuredLambda) ~= numberOfWavelengths || ...
|
||||
any(abs(measuredLambda - laserparams.lambda) > 1e-9)
|
||||
close(measurementFigure);
|
||||
error("Measurement wavelengths do not match laserparams.lambda.");
|
||||
end
|
||||
|
||||
achievedPowerDbm = zeros(numberOfSetpoints, numberOfWavelengths);
|
||||
for setpointIndex = 1:numberOfSetpoints
|
||||
firstLine = (setpointIndex - 1) * averagesPerSetpoint + 1;
|
||||
traces = zeros(averagesPerSetpoint, numberOfWavelengths);
|
||||
|
||||
for averageIndex = 1:averagesPerSetpoint
|
||||
trace = measurementLines(firstLine + averageIndex - 1).YData(:).';
|
||||
traces(averageIndex, :) = trace;
|
||||
end
|
||||
|
||||
achievedPowerDbm(setpointIndex, :) = mean(traces, 1, "omitnan");
|
||||
end
|
||||
close(measurementFigure);
|
||||
|
||||
% The measurement traces are stored in descending output-power order.
|
||||
% Sort them so they match the ascending laserparams.laserpower order.
|
||||
[~, sortIndex] = sort(mean(achievedPowerDbm, 2, "omitnan"));
|
||||
achievedPowerDbm = achievedPowerDbm(sortIndex, :);
|
||||
|
||||
figure("Name", "Available laser output power", "Color", "w");
|
||||
hold on;
|
||||
colors = lines(numberOfSetpoints);
|
||||
annotationX = laserparams.lambda(end) - 1;
|
||||
|
||||
for setpointIndex = 1:numberOfSetpoints
|
||||
yline(laserparams.laserpower(setpointIndex), ":", ...
|
||||
"Color", [0.65 0.65 0.65], "HandleVisibility", "off");
|
||||
plot(laserparams.lambda, achievedPowerDbm(setpointIndex, :), "-", ...
|
||||
"Color", colors(setpointIndex, :), "LineWidth", 1.6, ...
|
||||
"DisplayName", sprintf("Target %g dBm: measured", ...
|
||||
laserparams.laserpower(setpointIndex)));
|
||||
text(annotationX, laserparams.laserpower(setpointIndex), ...
|
||||
sprintf("P_{set} = %g dBm", laserparams.laserpower(setpointIndex)), ...
|
||||
"Color", colors(setpointIndex, :), "Interpreter", "tex", ...
|
||||
"HorizontalAlignment", "right", "VerticalAlignment", "bottom", ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
|
||||
title("Available laser output power versus wavelength", "Interpreter", "none");
|
||||
xlabel("Wavelength [nm]", "Interpreter", "none");
|
||||
ylabel("Achieved output power [dBm]", "Interpreter", "none");
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "northwest", "Interpreter", "none");
|
||||
|
||||
% Export the same figure with the repository-local matlab2tikz version.
|
||||
repoDir = fileparts(fileparts(scriptDir));
|
||||
addpath(fullfile(repoDir, "Libs", "mat2tikz", "src"));
|
||||
tikzOutputFile = fullfile(scriptDir, "available_output_power_vs_wavelength.tikz");
|
||||
matlab2tikz(char(tikzOutputFile), ...
|
||||
'showInfo', false, ...
|
||||
'showHiddenStrings', true, ...
|
||||
'extraAxisOptions', {'legend style={font=\footnotesize}'});
|
||||
@@ -0,0 +1,350 @@
|
||||
% This file was created by matlab2tikz.
|
||||
%
|
||||
\definecolor{mycolor1}{rgb}{0.89412,0.10196,0.10980}%
|
||||
\definecolor{mycolor2}{rgb}{0.21569,0.49412,0.72157}%
|
||||
\definecolor{mycolor3}{rgb}{0.30196,0.68627,0.29020}%
|
||||
\definecolor{mycolor4}{rgb}{0.59608,0.30588,0.63922}%
|
||||
\definecolor{mycolor5}{rgb}{1.00000,0.49804,0.00000}%
|
||||
\definecolor{mycolor6}{rgb}{0.12941,0.12941,0.12941}%
|
||||
%
|
||||
\begin{tikzpicture}
|
||||
|
||||
\begin{axis}[%
|
||||
width=6.458in,
|
||||
height=5.094in,
|
||||
at={(1.083in,0.688in)},
|
||||
scale only axis,
|
||||
xmin=1260,
|
||||
xmax=1360,
|
||||
xlabel style={font=\color{mycolor6}},
|
||||
xlabel={Wavelength [nm]},
|
||||
ymin=-2,
|
||||
ymax=12,
|
||||
ylabel style={font=\color{mycolor6}},
|
||||
ylabel={Achieved output power [dBm]},
|
||||
axis background/.style={fill=white},
|
||||
title style={font=\bfseries\color{mycolor6}},
|
||||
title={Available laser output power versus wavelength},
|
||||
xmajorgrids,
|
||||
ymajorgrids,
|
||||
grid style={dashed},
|
||||
legend style={at={(0.03,0.97)}, anchor=north west, legend cell align=left, align=left},
|
||||
legend style={font=\footnotesize}
|
||||
]
|
||||
\addplot [color=white!65!black, dotted, forget plot]
|
||||
table[row sep=crcr]{%
|
||||
1260 0\\
|
||||
1360 0\\
|
||||
};
|
||||
\addplot [color=mycolor1, line width=1.6pt]
|
||||
table[row sep=crcr]{%
|
||||
1260 -0.00174427500000025\\
|
||||
1262 0.0049530015000002\\
|
||||
1264 0.00258243800000004\\
|
||||
1266 0.047948272\\
|
||||
1268 0.035907209\\
|
||||
1270 0.039917379\\
|
||||
1272 0.0499743345000001\\
|
||||
1274 0.05230108\\
|
||||
1276 0.0532826289999999\\
|
||||
1278 0.0831857375000001\\
|
||||
1280 0.0627413579999998\\
|
||||
1282 0.0488831240000001\\
|
||||
1284 0.0447253150000001\\
|
||||
1286 0.0355354289999999\\
|
||||
1288 -0.00749986499999977\\
|
||||
1290 0.00176488450000019\\
|
||||
1292 -0.01833745\\
|
||||
1294 -0.0421456650000001\\
|
||||
1296 -0.05284091\\
|
||||
1298 -0.0608307149999999\\
|
||||
1300 -0.0629399749999999\\
|
||||
1302 -0.0542474850000001\\
|
||||
1304 -0.0943844899999999\\
|
||||
1306 -0.11916809\\
|
||||
1308 -0.088293575\\
|
||||
1310 -0.106838255\\
|
||||
1312 -0.142186045\\
|
||||
1314 -0.122909425\\
|
||||
1316 -0.136120425\\
|
||||
1318 -0.169754955\\
|
||||
1320 -0.14376953\\
|
||||
1322 -0.153371705\\
|
||||
1324 -0.19083209\\
|
||||
1326 -0.16585443\\
|
||||
1328 -0.16738367\\
|
||||
1330 -0.18946759\\
|
||||
1332 -0.16708397\\
|
||||
1334 -0.125356505\\
|
||||
1336 -0.125601985\\
|
||||
1338 -0.133365665\\
|
||||
1340 -0.07764601\\
|
||||
1342 -0.0349484850000002\\
|
||||
1344 -0.054868575\\
|
||||
1346 0.0190894669999999\\
|
||||
1348 0.0787105715000002\\
|
||||
1350 0.0655493160000001\\
|
||||
1352 0.0920785395000001\\
|
||||
1354 0.139883643\\
|
||||
1356 0.1389105975\\
|
||||
1358 0.15040566\\
|
||||
1360 0.1795823165\\
|
||||
};
|
||||
\addlegendentry{Target 0 dBm: measured}
|
||||
|
||||
\node[above left, align=right, inner sep=0, font=\color{mycolor1}]
|
||||
at (axis cs:1359,0) {$\text{P}_{\text{set}}\text{ = 0 dBm}$};
|
||||
\addplot [color=white!65!black, dotted, forget plot]
|
||||
table[row sep=crcr]{%
|
||||
1260 3\\
|
||||
1360 3\\
|
||||
};
|
||||
\addplot [color=mycolor2, line width=1.6pt]
|
||||
table[row sep=crcr]{%
|
||||
1260 3.001012505\\
|
||||
1262 3.006422054\\
|
||||
1264 3.008953025\\
|
||||
1266 3.0601149535\\
|
||||
1268 3.0454856245\\
|
||||
1270 3.047445995\\
|
||||
1272 3.060657117\\
|
||||
1274 3.071385682\\
|
||||
1276 3.06080489\\
|
||||
1278 3.079472899\\
|
||||
1280 3.061335045\\
|
||||
1282 3.050070503\\
|
||||
1284 3.055032579\\
|
||||
1286 3.042449593\\
|
||||
1288 3.0135694995\\
|
||||
1290 3.0167014675\\
|
||||
1292 3.0036652445\\
|
||||
1294 2.9777426285\\
|
||||
1296 2.9726872215\\
|
||||
1298 2.9398711715\\
|
||||
1300 2.916202023\\
|
||||
1302 2.927803093\\
|
||||
1304 2.904134328\\
|
||||
1306 2.888280249\\
|
||||
1308 2.8835689165\\
|
||||
1310 2.882305029\\
|
||||
1312 2.8897452415\\
|
||||
1314 2.8942784325\\
|
||||
1316 2.885521624\\
|
||||
1318 2.8517907965\\
|
||||
1320 2.843298358\\
|
||||
1322 2.855958141\\
|
||||
1324 2.8360024095\\
|
||||
1326 2.82446217\\
|
||||
1328 2.8612967225\\
|
||||
1330 2.8202706295\\
|
||||
1332 2.8185087195\\
|
||||
1334 2.8657736085\\
|
||||
1336 2.859368219\\
|
||||
1338 2.8706378305\\
|
||||
1340 2.9512009455\\
|
||||
1342 2.9559564765\\
|
||||
1344 2.9683251885\\
|
||||
1346 3.0288653\\
|
||||
1348 3.082960566\\
|
||||
1350 3.0701560325\\
|
||||
1352 3.1103352335\\
|
||||
1354 3.148218037\\
|
||||
1356 3.137503138\\
|
||||
1358 3.1635465145\\
|
||||
1360 3.1890315515\\
|
||||
};
|
||||
\addlegendentry{Target 3 dBm: measured}
|
||||
|
||||
\node[above left, align=right, inner sep=0, font=\color{mycolor2}]
|
||||
at (axis cs:1359,3) {$\text{P}_{\text{set}}\text{ = 3 dBm}$};
|
||||
\addplot [color=white!65!black, dotted, forget plot]
|
||||
table[row sep=crcr]{%
|
||||
1260 6\\
|
||||
1360 6\\
|
||||
};
|
||||
\addplot [color=mycolor3, line width=1.6pt]
|
||||
table[row sep=crcr]{%
|
||||
1260 5.987181772\\
|
||||
1262 6.01080855\\
|
||||
1264 6.0196549575\\
|
||||
1266 6.0658400605\\
|
||||
1268 6.0468314485\\
|
||||
1270 6.0572872865\\
|
||||
1272 6.0622857945\\
|
||||
1274 6.0605621715\\
|
||||
1276 6.056511485\\
|
||||
1278 6.075664353\\
|
||||
1280 6.0551906125\\
|
||||
1282 6.0599489405\\
|
||||
1284 6.0755984025\\
|
||||
1286 6.0270936715\\
|
||||
1288 6.0354817275\\
|
||||
1290 6.022740339\\
|
||||
1292 6.002313234\\
|
||||
1294 5.980794718\\
|
||||
1296 5.976163088\\
|
||||
1298 5.950657563\\
|
||||
1300 5.9223667825\\
|
||||
1302 5.935413683\\
|
||||
1304 5.9015896255\\
|
||||
1306 5.8993098045\\
|
||||
1308 5.891102095\\
|
||||
1310 5.881858564\\
|
||||
1312 5.8940111635\\
|
||||
1314 5.903941552\\
|
||||
1316 5.888497728\\
|
||||
1318 5.857725498\\
|
||||
1320 5.8567185045\\
|
||||
1322 5.861671574\\
|
||||
1324 5.8387118195\\
|
||||
1326 5.833706753\\
|
||||
1328 5.868907057\\
|
||||
1330 5.8273320035\\
|
||||
1332 5.8389400305\\
|
||||
1334 5.876151523\\
|
||||
1336 5.8620020595\\
|
||||
1338 5.892156925\\
|
||||
1340 5.9586369025\\
|
||||
1342 5.9567924075\\
|
||||
1344 5.982374356\\
|
||||
1346 6.0375119525\\
|
||||
1348 6.0890175215\\
|
||||
1350 6.086149783\\
|
||||
1352 6.1184705185\\
|
||||
1354 6.1594823235\\
|
||||
1356 6.1509530645\\
|
||||
1358 6.180046173\\
|
||||
1360 6.201404003\\
|
||||
};
|
||||
\addlegendentry{Target 6 dBm: measured}
|
||||
|
||||
\node[above left, align=right, inner sep=0, font=\color{mycolor3}]
|
||||
at (axis cs:1359,6) {$\text{P}_{\text{set}}\text{ = 6 dBm}$};
|
||||
\addplot [color=white!65!black, dotted, forget plot]
|
||||
table[row sep=crcr]{%
|
||||
1260 8\\
|
||||
1360 8\\
|
||||
};
|
||||
\addplot [color=mycolor4, line width=1.6pt]
|
||||
table[row sep=crcr]{%
|
||||
1260 6.031553038\\
|
||||
1262 6.475926562\\
|
||||
1264 6.9678239935\\
|
||||
1266 7.3812834605\\
|
||||
1268 7.6372069015\\
|
||||
1270 7.9191694625\\
|
||||
1272 8.0705430165\\
|
||||
1274 8.075813651\\
|
||||
1276 8.083409099\\
|
||||
1278 8.084946346\\
|
||||
1280 8.070698563\\
|
||||
1282 8.072232142\\
|
||||
1284 8.0797347955\\
|
||||
1286 8.0427192865\\
|
||||
1288 8.0515738335\\
|
||||
1290 8.0293537885\\
|
||||
1292 8.003326676\\
|
||||
1294 7.9976869805\\
|
||||
1296 7.99008813\\
|
||||
1298 7.9492751595\\
|
||||
1300 7.9346835125\\
|
||||
1302 7.9539901195\\
|
||||
1304 7.9017661065\\
|
||||
1306 7.911127979\\
|
||||
1308 7.907037724\\
|
||||
1310 7.896197244\\
|
||||
1312 7.907979363\\
|
||||
1314 7.9250423625\\
|
||||
1316 7.8998658815\\
|
||||
1318 7.869535715\\
|
||||
1320 7.8711403195\\
|
||||
1322 7.8730818405\\
|
||||
1324 7.845561009\\
|
||||
1326 7.85929528\\
|
||||
1328 7.8721726815\\
|
||||
1330 7.843708335\\
|
||||
1332 7.872582495\\
|
||||
1334 7.8930589925\\
|
||||
1336 7.882136431\\
|
||||
1338 7.920374461\\
|
||||
1340 7.9612389245\\
|
||||
1342 7.968047289\\
|
||||
1344 8.002771872\\
|
||||
1346 8.054855834\\
|
||||
1348 8.0969126465\\
|
||||
1350 8.1158346705\\
|
||||
1352 8.1537042675\\
|
||||
1354 8.181075273\\
|
||||
1356 8.1743607905\\
|
||||
1358 8.184566916\\
|
||||
1360 8.231713996\\
|
||||
};
|
||||
\addlegendentry{Target 8 dBm: measured}
|
||||
|
||||
\node[above left, align=right, inner sep=0, font=\color{mycolor4}]
|
||||
at (axis cs:1359,8) {$\text{P}_{\text{set}}\text{ = 8 dBm}$};
|
||||
\addplot [color=white!65!black, dotted, forget plot]
|
||||
table[row sep=crcr]{%
|
||||
1260 10\\
|
||||
1360 10\\
|
||||
};
|
||||
\addplot [color=mycolor5, line width=1.6pt]
|
||||
table[row sep=crcr]{%
|
||||
1260 5.7017284215\\
|
||||
1262 6.1986312315\\
|
||||
1264 6.7501614225\\
|
||||
1266 7.1907112785\\
|
||||
1268 7.478677345\\
|
||||
1270 7.8060430325\\
|
||||
1272 8.1067171935\\
|
||||
1274 8.276471823\\
|
||||
1276 8.4638177645\\
|
||||
1278 8.6111332305\\
|
||||
1280 8.743436757\\
|
||||
1282 8.832386923\\
|
||||
1284 8.91278028\\
|
||||
1286 9.04439012185\\
|
||||
1288 9.04376373615\\
|
||||
1290 9.0798988258\\
|
||||
1292 9.03395511625\\
|
||||
1294 8.9921718725\\
|
||||
1296 9.0235644005\\
|
||||
1298 8.984754639\\
|
||||
1300 9.14610934125\\
|
||||
1302 9.13106503035\\
|
||||
1304 9.2086507995\\
|
||||
1306 9.19881023815\\
|
||||
1308 9.265719913\\
|
||||
1310 9.09915279415\\
|
||||
1312 9.26554581525\\
|
||||
1314 9.52831931645\\
|
||||
1316 9.6669975241\\
|
||||
1318 9.7107443498\\
|
||||
1320 9.7898754663\\
|
||||
1322 9.83403242775\\
|
||||
1324 9.8726901865\\
|
||||
1326 9.8822262349\\
|
||||
1328 9.88426970855\\
|
||||
1330 9.84195532625\\
|
||||
1332 9.87982448925\\
|
||||
1334 9.90844454246\\
|
||||
1336 9.89706352885\\
|
||||
1338 9.9274772081\\
|
||||
1340 9.991583686247\\
|
||||
1342 9.983836191075\\
|
||||
1344 10.01432601913\\
|
||||
1346 10.08652218347\\
|
||||
1348 10.1116048911\\
|
||||
1350 10.1206856349\\
|
||||
1352 10.1690383129\\
|
||||
1354 10.191050599\\
|
||||
1356 10.19157208575\\
|
||||
1358 10.21642409705\\
|
||||
1360 10.24591036845\\
|
||||
};
|
||||
\addlegendentry{Target 10 dBm: measured}
|
||||
|
||||
\node[above left, align=right, inner sep=0, font=\color{mycolor5}]
|
||||
at (axis cs:1359,10) {$\text{P}_{\text{set}}\text{ = 10 dBm}$};
|
||||
\end{axis}
|
||||
\end{tikzpicture}%
|
||||
@@ -1,6 +1,6 @@
|
||||
wh_aeon = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Lab_analysis\aeon_soa_measurement_lambda_plaser_pump.mat");
|
||||
wh_aeon = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Lab_oband_laser_and_amp_analysis\aeon_soa_measurement_lambda_plaser_pump.mat");
|
||||
wh_aeon = wh_aeon.wh;
|
||||
wh_thor = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Lab_analysis\thorlabs_pdfa_measurement_lambda_plaser_pump.mat");
|
||||
wh_thor = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Lab_oband_laser_and_amp_analysis\thorlabs_pdfa_measurement_lambda_plaser_pump.mat");
|
||||
wh_thor = wh_thor.wh;
|
||||
|
||||
% Silas' custom "warehouse" datatype
|
||||
@@ -36,6 +36,7 @@ for p = 1:numel(plasers)
|
||||
plot(wavelength_osa,spectrum_osa,'DisplayName',sprintf('P_{in}: %d dB ',plasers(p)),'Color',cols(ccnt+1,:));
|
||||
|
||||
|
||||
|
||||
ylim([-60, 20]);
|
||||
beautifyBERplot("logscale",false,"setmarkers",0,"setcolors",0);
|
||||
end
|
||||
|
||||
@@ -0,0 +1,193 @@
|
||||
|
||||
if 1
|
||||
% A) RUN FULL LOOP
|
||||
M_format = [2,4,6,8];
|
||||
snr = 10:25;
|
||||
else
|
||||
% B) RUN FOR DEBUG AND TEST
|
||||
M_format = 4;
|
||||
snr = 20;
|
||||
end
|
||||
|
||||
for m = 1:length(M_format)
|
||||
% --- Parameters ---
|
||||
M = M_format(m); % PAM order (e.g., 2,4,8)
|
||||
Nsym = 1e5; % number of symbols
|
||||
h = [1, 0.5, 0.2]; % Impulse response to remove
|
||||
|
||||
b = log2(M);
|
||||
if M == 6 b = 5; end
|
||||
rng(1);
|
||||
bits_tx = logical(randi([0 1], Nsym, b, 'uint8'));
|
||||
|
||||
tx_symbols = pammap(bits_tx,M);
|
||||
|
||||
if M == 6
|
||||
states = unique(tx_symbols);
|
||||
pam6transitions = combvec(states',states')'; % pam6transitions =
|
||||
bitmapping = pamdemap(reshape(pam6transitions',1,[])',M);
|
||||
else
|
||||
bitmapping = pamdemap(unique(tx_symbols),M);
|
||||
end
|
||||
|
||||
scaling = sqrt(sum(unique(tx_symbols).^2)/numel(unique(tx_symbols)));
|
||||
tx_symbols = tx_symbols ./ scaling;
|
||||
|
||||
% apply impulse response to signal
|
||||
y_filt = filter(h, 1, tx_symbols);
|
||||
|
||||
for s = 1:length(snr)
|
||||
|
||||
% apply noise
|
||||
y = awgn(y_filt,snr(s),"measured",1);
|
||||
y = Electricalsignal(y);
|
||||
|
||||
% apply ml-MLSE
|
||||
adaptive_mu = 0;
|
||||
mu_lms = 0.15;
|
||||
ml_mlse_equalizer = ML_MLSE("epochs_tr",50,"epochs_dd",1,"len_tr",length(y)/2,...
|
||||
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",11,"sps",1,...
|
||||
"L",2,"delta",4,"adaptive_mu",adaptive_mu);
|
||||
|
||||
[ml_mlse_estimate,~] = ml_mlse_equalizer.process(y,tx_symbols);
|
||||
rx_symbols = ml_mlse_estimate .* scaling;
|
||||
bits_rx = pamdemap(rx_symbols,M);
|
||||
|
||||
BER_ml(m,s) = nnz(bits_tx ~= bits_rx) / numel(bits_tx);
|
||||
fprintf('BER = %.2e \n', BER_ml(m,s));
|
||||
|
||||
% apply bcjr
|
||||
BCJR = MLSE("DIR",h,"duobinary_output",0,"M",M,"trellis_states",unique(tx_symbols));
|
||||
[viterbi_estimate,LLR,GMI(m,s)] = BCJR.process(y,tx_symbols,bits_tx,bitmapping);
|
||||
|
||||
% decode LLR's
|
||||
bits_LLR = LLR > 0;
|
||||
|
||||
% demap viterbi symbols sequence
|
||||
rx_symbols = viterbi_estimate .* scaling;
|
||||
bits_rx = pamdemap(rx_symbols,M);
|
||||
|
||||
% BER calc
|
||||
BER_vit(m,s) = nnz(bits_tx ~= bits_LLR) / numel(bits_tx);
|
||||
fprintf('BER LLR = %.2e \n', BER_vit(m,s));
|
||||
|
||||
BER_llr(m,s) = nnz(bits_tx ~= bits_rx) / numel(bits_tx);
|
||||
fprintf('BER = %.2e \n', BER_llr(m,s));
|
||||
end
|
||||
end
|
||||
%%
|
||||
figure();hold on
|
||||
for m = 1:length(M_format)
|
||||
p=plot(snr,BER_llr(m,:),'DisplayName',sprintf('Viterbi: PAM %d',M_format(m)));
|
||||
plot(snr,BER_ml(m,:),'DisplayName',sprintf('ML-Based: PAM %d',M_format(m)),'LineStyle',':','Color',p.Color);
|
||||
end
|
||||
ylabel('BER');
|
||||
xlabel('SNR')
|
||||
title('BER vs. SNR');
|
||||
set(gca, 'XScale', 'linear', ...
|
||||
'YScale', 'log', ...
|
||||
'TickLabelInterpreter', 'latex', ...
|
||||
'FontSize', 11);
|
||||
|
||||
%%
|
||||
figure();hold on
|
||||
for m = 1:length(M_format)
|
||||
plot(snr,GMI(m,:),'DisplayName',sprintf('GMI PAM %d',M_format(m)))
|
||||
end
|
||||
ylabel('GMI');
|
||||
xlabel('SNR')
|
||||
title('GMI vs. SNR');
|
||||
set(gca, 'XScale', 'linear', ...
|
||||
'YScale', 'linear', ...
|
||||
'TickLabelInterpreter', 'latex', ...
|
||||
'FontSize', 11);
|
||||
|
||||
function symbols = pammap(bits,M)
|
||||
bits = logical(bits);
|
||||
if M == 2
|
||||
symbols = bits;
|
||||
elseif M == 4
|
||||
symbols= 2*bits(:,1) + (bits(:,1)==bits(:,2));
|
||||
symbols=2*symbols-3;
|
||||
|
||||
elseif M == 6
|
||||
|
||||
m = 1;
|
||||
|
||||
if size(bits,2)>size(bits,1)
|
||||
bits = bits'; %vector aufrecht stellen
|
||||
end
|
||||
bits = reshape(bits',1,[])';
|
||||
thres = [-3 5;-1 5;-3 -5;-1 -5;-5 3;-5 1;-5 -3;-5 -1;-1 3;-1 1;-1 -3;-1 -1;-3 3;-3 1;-3 -3;-3 -1;3 5;1 5;3 -5;1 -5;5 3;5 1;5 -3;5 -1;1 3;1 1;1 -3;1 -1;3 3;3 1;3 -3;3 -1];
|
||||
% LUT based mapping
|
||||
for k = 1:5:fix(length(bits)/5)*5
|
||||
symbols(m:m+1,1) = thres(bin2dec(int2str(bits(k:k+4)'))+1,:);
|
||||
m = m+2;
|
||||
end
|
||||
|
||||
elseif M == 8
|
||||
x1 = bits(:,1);
|
||||
x2 = (bits(:,1)==bits(:,3));
|
||||
x3 = x2~=bits(:,2);
|
||||
|
||||
symbols = 4*x1 + 2*x2 + x3;
|
||||
symbols=2*symbols-7;
|
||||
end
|
||||
end
|
||||
|
||||
function bits = pamdemap(symbols,M)
|
||||
|
||||
if M == 2
|
||||
thres=0;
|
||||
elseif M == 4
|
||||
thres=[-2,0,2];
|
||||
elseif M == 6
|
||||
thres = [-3 5;-1 5;-3 -5;-1 -5;-5 3;-5 1;-5 -3;-5 -1;-1 3;-1 1;-1 -3;-1 -1;-3 3;-3 1;-3 -3;-3 -1;3 5;1 5;3 -5;1 -5;5 3;5 1;5 -3;5 -1;1 3;1 1;1 -3;1 -1;3 3;3 1;3 -3;3 -1];
|
||||
elseif M == 8
|
||||
thres=-6:2:6;
|
||||
end
|
||||
|
||||
if M ~= 6
|
||||
symbols = symbols';
|
||||
a = squeeze(repmat(real(symbols),[1 1 length(thres)])); %Eingangssignal in 3 spalten
|
||||
b = squeeze(repmat(reshape(thres(:).',[1 1 length(thres)]),[1 length(symbols) 1])); %Threshold in 3 Spalten
|
||||
comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
|
||||
comp_real=repmat(real(symbols),[1 1 length(thres)]) > repmat(reshape(thres(:).',[1 1 length(thres)]),[1 length(symbols) 1]);
|
||||
s1=size(comp_real,1);
|
||||
s2=size(comp_real,2);
|
||||
end
|
||||
|
||||
if M == 2
|
||||
data_out=abs(comp_real(:,:,1));
|
||||
elseif M == 4
|
||||
data_out=[comp_real(:,:,2); ones(s1,s2) - comp_real(:,:,1) + comp_real(:,:,3)];
|
||||
elseif M == 6
|
||||
|
||||
if size(symbols,2) > 1
|
||||
symbols = symbols.';
|
||||
end
|
||||
|
||||
if length(symbols)/2 ~= round(length(symbols)/2)
|
||||
symbols = [symbols;0];
|
||||
end
|
||||
|
||||
m = 1;
|
||||
for n = 1:2:length(symbols)
|
||||
dist = sqrt((symbols(n)-thres(:,1)).^2+(symbols(n+1)-thres(:,2)).^2);
|
||||
[~,dd_idx] = min(dist);
|
||||
% dec_out(n:n+1) = LUT(dd_idx,:);
|
||||
data_out(m:m+4) = bitget(dd_idx-1,5:-1:1);
|
||||
m = m+5;
|
||||
end
|
||||
|
||||
data_out = reshape(data_out',5,[]);
|
||||
|
||||
elseif M == 8
|
||||
data_out=[comp_real(:,:,4);
|
||||
comp_real(:,:,1)-comp_real(:,:,3)+comp_real(:,:,5)-comp_real(:,:,7);
|
||||
1-comp_real(:,:,2)+comp_real(:,:,6)];
|
||||
end
|
||||
|
||||
bits = data_out';
|
||||
|
||||
end
|
||||
BIN
workerError.mat
BIN
workerError.mat
Binary file not shown.
Reference in New Issue
Block a user