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Merge branch 'main' of cau-git.rz.uni-kiel.de:nt/mitarbeiter/silas/imdd_simulation

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silas (home)
2026-01-30 12:18:15 +01:00
parent 5f3fd61519 7eb3364814
commit 67689bb70f
24 changed files with 1501 additions and 299 deletions
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6
Classes/00_signals/Signal.m
View File

@@ -971,8 +971,8 @@ classdef Signal
maxA = max(sig(100:end-100))*1.3;
minA = min(sig(100:end-100))*1.3;
% maxA = 0.12;
% minA = -0.08;
maxA = 0.12;
minA = -0.08;
difference= maxA-minA;
@@ -993,7 +993,9 @@ classdef Signal
% beautify
cm=flip(cbrewer2("RdYlBu",4096));
% cm=flip(cbrewer2("RdBu",4096));
% cm=flip(cbrewer2("Blues",4096));
cm(1,:) = [1,1,1]; % set zeros to white => clean background
colormap(cm);
% colormap('turbo');
% ax.CLim = [0 50];

515
Classes/04_DSP/Equalizer/ML_MLSE.m
View File

@@ -1,3 +1,499 @@
% classdef ML_MLSE < handle
% % ALGORITHM DESCRIBED IN:
% % W. Lanneer and Y. Lefevre, “Machine Learning-Based Pre-Equalizers for
% % Maximum Likelihood Sequence Estimation in High-Speed PONs,”
% % in 2023 31st European Signal Processing Conference
%
% % Further ML Refs:
% % https://machinelearningmastery.com/cross-entropy-for-machine-learning/
% % https://docs.pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html
%
% % The central idea is to overcome the (white-) noise assumption within the previously described
% % Viterbi algorithm, more precisely a closed-loop optimization is proposed that finds a suitable
% % filter-set to directly compute the branch metrics c_k (s,s^' ). These can directly be used to
% % carry out the conventional Viterbi algorithm. The system consists of S^L S=F linear FIR filters,
% % combined with one bias coefficient respectively. These filters take the received input samples to
% % compute the branch metrics estimates (c_k ) ̂(s,s^' ) according toThe central idea is to overcome
% % the (white-) noise assumption within the previously described Viterbi algorithm, more precisely
% % a closed-loop optimization is proposed that finds a suitable filter-set to directly compute the
% % branch metrics c_k (s,s^' ). These can directly be used to carry out the conventional Viterbi
% % algorithm. The system consists of S^L S=F linear FIR filters, combined with one bias coefficient
% % respectively. These filters take the received input samples to compute the branch metrics
% % estimates. Finally, the usual Viterbi is carried out...
%
% % Recommended Settings and some findings:
%
% % Requires many training epochs. According to ML people, 100,200 or
% % even up to 1000 epochs are normal for ML-convergence
%
% % The mu parameter _can_ be adaptive - using the cross entropy and when
% % analyzing the isolated training it looks very promisig. However, is
% % later use I found this is not as stable as a fixed learning rate.
% % mu = 0.1 worked good for me
%
% % Longer orders/ filter length are not always better. For me order=11
% % was good.
%
% % Delay factor (delta) is good when the order is also increased. With
% % order = 11, a delta of =4 shows good results
%
% properties
% sps % usually 2
% order
% e
% e_tr
% error
%
% len_tr
% mu_tr
% epochs_tr
%
% dd_mode % 1 or 0 to set DD-mode on or off
% mu_dd %weight update in dd mode
% epochs_dd
%
% adaptive_mu
%
% constellation
%
% L %viterbi memory length
%
% alpha
% DIR
% DIR_flip
% trellis_states
%
% traceback_depth
%
% % --- Added internal class variables used later ---
% S
% Nf
% delta
% nStates
% nFeasible
% combs
% first_sym
% last_sym
% valid
% valid_to_idx
% valid_from_idx
% w
%
% % --- New: fast state lookup ---
% true_to_state_idx
% state_dict % containers.Map: key(sequence)->state index
% key_fmt = '%.8g_'; % key format for sequence strings
% nSym % |constellation|
%
% ber = []
% ce = ones(1,1);
% end
%
% methods
% function obj = ML_MLSE(options)
% arguments(Input)
%
% options.sps = 2;
% options.order = 15;
%
% options.len_tr = 4096;
% options.mu_tr = 0;
% options.epochs_tr = 5;
%
% options.dd_mode = 1;
% options.mu_dd = 1e-5;
% options.epochs_dd = 5;
%
% options.adaptive_mu = 1;
%
% options.delta = 0;
% options.traceback_depth = 1024;
%
% options.L = 1
%
% end
%
% fn = fieldnames(options);
% for n = 1:numel(fn)
% obj.(fn{n}) = options.(fn{n});
% end
%
% obj.e = zeros(obj.order,1);
% obj.error = 0;
% end
%
% function [X,X_viterbi] = process(obj, X, D)
%
% % actual processing of the signal (steps 1. - 3.)
% % 1 normalize RMS
% X = X.normalize("mode","rms");
%
% % Use sorted constellation for deterministic mapping
% obj.constellation = sort(unique(D.signal),'ascend');
% obj.nSym = numel(obj.constellation);
%
% if length(X)/length(D) ~= obj.sps
% warning('Signal length does not fit to reference!');
% end
%
% % ==============================================================
% % INITIALIZATION (only before final epoch and detection mode)
% % ==============================================================
%
% % --- Parameters
% obj.S = numel(obj.constellation); % alphabet size
% obj.Nf = obj.order*obj.sps; % filter length
% % obj.delta = 3;%ceil(obj.Nf/2); % delay parameter
% obj.nStates = obj.S^obj.L;
% obj.nFeasible = obj.nStates*obj.S;
%
% % --- Trellis mapping
% obj.trellis_states = reshape(obj.constellation,1,[]);
% pre_comb_mat = repmat(obj.trellis_states, obj.L, 1);
% pre_comb_cell = mat2cell(pre_comb_mat, ones(1,obj.L), size(pre_comb_mat,2));
% obj.combs = fliplr(combvec(pre_comb_cell{:}).'); % rows: states, columns: [x_k, x_{k-1}, ...]
% obj.first_sym = obj.combs(:,1);
% obj.last_sym = obj.combs(:,end);
% obj.nStates = size(obj.combs,1);
%
% % --- Valid transitions
% obj.valid = false(obj.nStates);
% for from = 1:obj.nStates
% for to = 1:obj.nStates
% if all(obj.combs(to,2:end) == obj.combs(from,1:end-1))
% obj.valid(to,from) = true;
% end
% end
% end
% [obj.valid_to_idx, obj.valid_from_idx] = find(obj.valid);
%
% % --- Allocate vectors and weights
% % !! IF SHAPE FIT, then we already have smth there an we want
% % to start with the existing fitler-set
% if isempty(obj.w) || any(size(obj.w) ~= [obj.Nf+1,obj.nFeasible])
% obj.w = zeros(obj.Nf+1,obj.nFeasible); % filter weights per transition + bias tap
% obj.w = randn(obj.Nf+1,obj.nFeasible);
% end
%
% % --- Precompute dictionary for fast state lookup (sequence -> state)
% keys = cell(obj.nStates,1);
% for i = 1:obj.nStates
% keys{i} = obj.seq_key(obj.combs(i,:)); % combs row is already [x_k, x_{k-1}, ...]
% end
% obj.state_dict = containers.Map(keys, 1:obj.nStates);
%
% % ==============================================================
% % TRAINING
% % ==============================================================
%
% % Training Mode
% n = obj.len_tr;
% training = 1;
% obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,n,training);
% obj.e_tr = obj.e;
%
% % ==============================================================
% % DD-Mode / Fixed Mode
% % ==============================================================
%
% % Decision Directed Mode
% n = X.length;
% training = 0;
% [y,y_vit]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,n,training);
%
% X_viterbi = X;
%
% X.signal = y;
% X.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
% lbdesc = [num2str(obj.order),' tap FFE'];
% X = X.logbookentry(lbdesc); % append to logbook
%
% X_viterbi.signal = y_vit;
% X_viterbi.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
% lbdesc = [num2str(obj.order),'order FFE + PF + Viterbi'];
% X_viterbi = X_viterbi.logbookentry(lbdesc); % append to logbook
% end
%
% function [y,y_ref] = equalize(obj,x,d,mu,epochs,N,training)
% % ==============================================================
% % FFE + Whitening + ML-Based Branch Metric Estimation + Viterbi
% % ==============================================================
% debug = 1;
% showPlots = 1;
%
% % --- Input padding and preallocation
% y = zeros(N,1);
%
% % number of symbol steps in this block
% nSymbols = ceil(N/obj.sps);
%
% for epoch = 1:epochs
%
% % state metrics (log-domain costs): keep as column [nStates×1]
% pm = zeros(obj.nStates,1); % v_{k-1}(s′)
% c_hat = zeros(1,obj.nFeasible);
% v_tilde = zeros(1,obj.nFeasible);
% pred = zeros(nSymbols, obj.nStates, 'uint32');
% pm_sto = nan(obj.nStates, nSymbols,'like',pm);
% CE_accum = 0;
%
%
% %%% START IDX
% if training
% max_start = length(x) - ( (ceil(N/obj.sps)-1)*obj.sps + 1 );
% max_start = max(1, max_start); % safety
% start_sample = randi([1, max_start], 1); %rnd training; not really good
% start_sample = 1;
% end_sample = start_sample + (ceil(N/obj.sps)-1)*obj.sps;
% else
% start_sample = 1;%obj.len_tr;
% end_sample = N;
% end
%
% start_symbol = 1 + floor((start_sample - 1)/obj.sps); % ABSOLUTE symbol index
%
% if numel(d) >= obj.L && start_symbol >= obj.L
% init_seq = d(start_symbol-obj.L+1 : start_symbol); % [d_k-L+1 ... d_k]
% true_to_state_idx = obj.state_dict(obj.seq_key(flip(init_seq))); % [d_k ... d_k-L+1]
% else
% % Not enough history – fall back to state 1
% true_to_state_idx = uint32(1);
% end
%
% symbol = 0;
% for sample = start_sample:obj.sps:end_sample
% symbol = symbol + 1;
% k = symbol;
% sym_idx = start_symbol + (symbol - 1);
%
% % --- Build Δ-delayed observation window y_k
% i1 = sample - obj.Nf + 1 + obj.delta;
% i2 = sample + obj.delta;
% buf = x(max(1,i1):min(length(x),i2));
% padL = max(0,1 - i1);
% padR = max(0,i2 - length(x));
% yk = [zeros(padL,1); buf(:); zeros(padR,1)]; % Nf×1
% yk = [yk;1];
%
% % --- Predict branch metrics for all feasible transitions: c_hat
% c_hat = (yk.' * obj.w); % [1×nFeasible]
% c_hat = c_hat.'; % [nFeasible×1]
%
% % --- Extended path metrics: v_tilde = pm(from) + c_hat
% % normalize pm to avoid growth (invariant to additive const)
% pm = pm - min(pm);
% v_tilde = pm(obj.valid_from_idx) + c_hat; % [nFeasible×1]
%
% % ===== Gradient update (Algorithm 1) =====
%
% if 1 %training
% % --- allocate storage once
% if epoch == 1 && symbol == 1
% obj.true_to_state_idx = ones(ceil(N/obj.sps),1,'uint32');
% end
%
% % --- previous "to" becomes current "from"
% if symbol > 1
% true_from_state_idx = obj.true_to_state_idx(symbol-1);
% else
% true_from_state_idx = 1;
% end
%
% % --- compute or reuse "to" state
% if epoch == 1
% % only compute in first epoch
% if sym_idx >= obj.L
% key_to = obj.seq_key(flip(d(sym_idx-obj.L+1 : sym_idx)));
% if isKey(obj.state_dict, key_to)
% obj.true_to_state_idx(symbol) = obj.state_dict(key_to);
% else
% obj.true_to_state_idx(symbol) = true_from_state_idx;
% end
% else
% obj.true_to_state_idx(symbol) = true_from_state_idx;
% end
% end
%
% % --- reuse cached state from second epoch onward
% true_to_state_idx = obj.true_to_state_idx(symbol);
%
% % --- ensure valid (from,to)
% dirac = zeros(obj.nFeasible,1);
% mask = obj.valid_from_idx==true_from_state_idx & ...
% obj.valid_to_idx ==true_to_state_idx;
% if any(mask)
% dirac(mask) = 1;
% else
% idx = find(obj.valid_from_idx==true_from_state_idx,1,'first');
% dirac(idx) = 1;
% obj.true_to_state_idx(symbol) = obj.valid_to_idx(idx);
% end
%
%
%
%
% % softmax over -v_tilde (numerically safe shift)
% v_shift = -(v_tilde - min(v_tilde)); % shift to small positive numbers
% v_shift = min(v_shift, 100); % clamp exponent argument (≈ exp(50)=3e21)
% expv = exp(v_shift);
% p = expv ./ (sum(expv) + eps);
%
% % for logging only:
% CE_symbol(symbol) = -log(p(dirac==1) + eps);
%
% if sym_idx > obj.L
% CE_smooth(symbol) = 0.01*CE_symbol(symbol) + 0.99*CE_smooth(symbol-1);
% else
% if epoch > 1
% CE_smooth(symbol) = obj.ce(end); %use ce from last epoch or =1 for very first round?!
% else
% CE_smooth(symbol) = CE_symbol(symbol);
% end
% end
%
% CE_accum = CE_symbol(symbol) + CE_accum;
%
%
% % gradient term (t - p)
% dmp = (dirac - p)'; % 1×nFeasible
%
% % Per-feature gradient; implicit expansion gives (Nf+1)×nFeasible
% dL_Dw = (yk) .* dmp;
%
% % Start updates only when the ABSOLUTE symbol index has ≥ L history
% if sym_idx >= obj.L
% if obj.adaptive_mu
% mu_eff = CE_smooth(sym_idx);
% mu_eff = max(min(mu_eff, 0.2), 1e-4);
% else
% mu_eff = mu;
% end
%
% obj.w = obj.w - mu_eff .* dL_Dw; % (Nf+1)×nFeasible
% end
%
% % if debug && epoch > 2
% % figure(100);
% % subplot(4,1,1);
% % heatmap(p');
% % title('Probs')
% % subplot(4,1,2);
% % heatmap(dmp);
% % title('Update')
% % subplot(4,1,3);
% % heatmap(dL_Dw);
% % title('Update')
% % subplot(4,1,4);
% % heatmap(bj.w);
% % title('Update')
% %
% % end
%
% end
%
%
%
% % --- Compare-Select (matrix form, min of costs)
% v_tilde_mat = inf(obj.nStates, obj.nStates);
% v_tilde_mat(obj.valid) = v_tilde;
% [pm_next, pred(k,:)] = min(v_tilde_mat, [], 2);
%
% % re-center to keep metrics bounded (decision-invariant)
% pm_next = pm_next - min(pm_next);
%
% pm = pm_next;
% pm_sto(:,symbol) = pm;
% end
%
% % --- Traceback (full; you can window with traceback_depth if desired)
% [~, s_end] = min(pm);
% viterbi_path = zeros(symbol,1,'uint32');
% viterbi_path(symbol) = s_end;
% for n = symbol:-1:2
% viterbi_path(n-1) = pred(n, viterbi_path(n));
% end
%
% y_ref = d(start_symbol:end);
% y = obj.first_sym(viterbi_path);
%
% if debug && training
% sym_start = start_symbol;
% sym_end = start_symbol + symbol - 1;
% ref_slice = d(sym_start : sym_end);
% err = sum(y ~= ref_slice(1:numel(y)));
%
% try
% ref_bits = PAMmapper(obj.S,0).demap(ref_slice);
% eq_bits = PAMmapper(obj.S,0).demap(y);
% [~, ~, ber, ~] = calc_ber(ref_bits, eq_bits, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
% fprintf('Epoch: %d - BER: %.1e \n',epoch, ber);
% obj.ber(epoch) = ber;
% catch
% ser = err./length(y);
% fprintf('Epoch: %d - SER: %.1e \n',epoch, ser);
% end
%
% obj.ce(epoch) = CE_accum./symbol;
%
% if showPlots
% figure(10);clf
% subplot(3,2,1:2);
% heatmap(obj.w);
% title('Filter')
%
% subplot(3,2,3);
% v_tildemat = NaN(obj.nStates, obj.nStates);
% v_tildemat(obj.valid) = v_tilde; % log-domain scores
% heatmap(v_tildemat);
% title('Path Metrics (v_tilde)')
%
% subplot(3,2,4);
% scatter(1:symbol,pm_sto,1,'.')
% title('Path Metric Winners')
%
% subplot(3,2,5);hold on
% scatter(1:symbol,CE_symbol,1,'.');
% scatter(1:symbol,CE_smooth,1,'.')
% title('Cross Entropy')
%
% subplot(3,2,6); hold on
%
% % Left y-axis: Cross Entropy (linear)
% yyaxis left
% scatter(1:length(obj.ce), obj.ce, 10, 's', 'filled')
% ylabel('Cross Entropy')
%
% % Right y-axis: BER (logarithmic)
% yyaxis right
% scatter(1:length(obj.ber), obj.ber, 10, 'd', 'filled')
% set(gca, 'YScale', 'log')
% ylabel('BER (log scale)')
%
% xlim([1, epochs])
% xlabel('Epoch')
% title('Cross Entropy // BER')
% grid on
%
% drawnow
% end
% end
% end
% end
% end
%
% methods (Access=private)
% function k = seq_key(obj, seq)
% % Build a stable key string for a sequence row vector in the *same order as combs rows* ([x_k, x_{k-1}, ...])
% % Use rounding via sprintf to avoid floating-point issues.
% % seq must be a row vector.
% k = sprintf(obj.key_fmt, seq);
% end
% end
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
classdef ML_MLSE < handle
% ---------------------------------------------------------------------
% W. Lanneer and Y. Lefevre,
@@ -101,7 +597,7 @@ classdef ML_MLSE < handle
obj.S = obj.nSym;
obj.Nf = obj.order * obj.sps;
obj.nStates = obj.S^obj.L;
obj.nFeasible = obj.nStates * obj.S;
obj.nFeasible = obj.nStates * obj.S; %feasible state transitions
% --- Trellis mapping
obj.trellis_states = reshape(obj.constellation,1,[]);
@@ -164,8 +660,8 @@ classdef ML_MLSE < handle
% EQUALIZE
% ==============================================================
function [y,y_ref] = equalize(obj,x,d,mu,epochs,N,training)
debug = 0;
showPlots = 0;
debug = 1;
showPlots = 1;
y = zeros(N,1);
nSymbols = ceil(N/obj.sps);
@@ -241,6 +737,19 @@ classdef ML_MLSE < handle
dirac(trans_idx)=1;
end
% --- ensure valid (from,to)
if ~any(dirac)
mask = obj.valid_from_idx==true_from_state_idx & ...
obj.valid_to_idx ==true_to_state_idx;
if any(mask)
dirac(mask) = 1;
else
idx = find(obj.valid_from_idx==true_from_state_idx,1,'first');
dirac(idx) = 1;
obj.true_to_state_idx(symbol) = obj.valid_to_idx(idx);
end
end
% ===================================================================
% TRAINING MODE (weight update)
% ===================================================================

10
Classes/04_DSP/Timing_Recovery.m
View File

@@ -9,7 +9,7 @@ classdef Timing_Recovery < handle
end
methods(Access=public)
function obj = FFE(options)
function obj = Timing_Recovery(options)
arguments(Input)
options.timing_error_detector = 'Gardner';
@@ -25,12 +25,11 @@ classdef Timing_Recovery < handle
obj.(fn{n}) = options.(fn{n});
end
obj.e = zeros(obj.order,1);
obj.error = 0;
end
function data_out = process(obj, data_in)
function [data_out,timing_error] = process(obj, data_in)
timing_synchronization = comm.SymbolSynchronizer( ...
"TimingErrorDetector", obj.timing_error_detector, ...
@@ -39,7 +38,8 @@ classdef Timing_Recovery < handle
"NormalizedLoopBandwidth", obj.normalized_loop_bandwidth, ...
"DetectorGain", obj.detector_gain);
data_out.signal = timing_synchronization(data_in.signal);
data_out = data_in;
[data_out.signal,timing_error] = timing_synchronization(data_in.signal);
end
end

7
Functions/EQ_structures/dsp_runid.m
View File

@@ -95,7 +95,7 @@ try
adaption= 1;
use_dd_mode = 1;
use_ffe = 0;
use_ffe = 1;
use_dfe = 0;
use_vnle_mlse = 0;
use_dbtgt = 0;
@@ -149,6 +149,9 @@ try
% Scpe_sig.spectrum("fignum",22233,"normalizeTo0dB",0,"displayname",'Rx');
% Scpe_sig.eye(fsym,M,"fignum",1024);
Scpe_sig.signal = Scpe_sig.signal(1:2*Symbols.length);
Scpe_sig.signal = real(Scpe_sig.signal);
if duob_mode ~= db_mode.db_encoded
if use_ffe
@@ -246,7 +249,7 @@ try
%ML-based MLSE (L=2)
mu_ml = 0.01; training_epochs = 100;
ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
"len_tr",length(Scpe_sig),"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
"len_tr",length(Scpe_sig)/4,"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
"traceback_depth",128,"L",1,"delta",4,"adaptive_mu",0);
[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Scpe_sig, Symbols, Tx_bits,"precode_mode",duob_mode);

23
Functions/EQ_structures/ffe.m
View File

@@ -157,7 +157,7 @@ function displayAnalysis(eq_noise, eq_signal_sd, rx_signal, eq_, tx_symbols, M,
% Initialize figure handles
% Corrected line - added tx_symbols as second positional argument
showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 100);
% showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 100);
warning off
showLevelScatter(eq_signal_sd, tx_symbols, "fignum", 101);
@@ -174,19 +174,20 @@ function displayAnalysis(eq_noise, eq_signal_sd, rx_signal, eq_, tx_symbols, M,
end
try
figure(339);
showEQfilter(eq_.e_tr, eq_signal_sd.fs.*2,"displayname",'training','fignum',339);
showEQfilter(eq_.e, eq_signal_sd.fs.*2,"displayname",'dec. directed','fignum',339);
figure(339);hold on
showEQfilter(eq_.e, eq_signal_sd.fs.*2,"displayname",'training','fignum',339);
% showEQfilter(eq_.e, eq_signal_sd.fs.*2,"displayname",'dec. directed','fignum',339);
legend on
end
try
figure(240); hold on; plot(pow2db(movmean(eq_.debug_struct.error_tr',100)));ylim([-30,3]);title('error training');
% try
% figure(240); hold on; plot(pow2db(movmean(eq_.debug_struct.error_tr',100)));ylim([-30,3]);title('error training');
%
% figure(241); hold on; plot(pow2db(movmean(eq_.debug_struct.update_tr',100)));title('update step training');
%
% figure(242); hold on; plot(pow2db(movmean(eq_.debug_struct.update',1000)));title('update step dd');
% end
figure(241); hold on; plot(pow2db(movmean(eq_.debug_struct.update_tr',100)));title('update step training');
figure(242); hold on; plot(pow2db(movmean(eq_.debug_struct.update',1000)));title('update step dd');
end
% eq_signal_sd.eye(eq_signal_sd.fs,M,"displayname",'Eye','fignum',105);
eq_signal_sd.eye(eq_signal_sd.fs,M,"displayname",'Eye','fignum',105);
end

9
Functions/EQ_structures/ml_mlse.m
View File

@@ -46,6 +46,12 @@ end
ml_mlse_results.config = Equalizerstruct();
eq_small = strip_eq(eq_, 10);
fn = fieldnames(eq_small);
for k = 1:numel(fn)
if issparse(eq_small.(fn{k}))
eq_small.(fn{k}) = full(eq_small.(fn{k}));
end
end
json_str = jsonencode(eq_small);
ml_mlse_results.config.eq = jsonencode(eq_);
@@ -135,6 +141,9 @@ function eq_out = strip_eq(eq_, max_elems)
eq_.(props{i}) = [];
end
end
if issparse(val)
eq_.(props{i}) = find(eq_.(props{i}));
end
end
eq_out = eq_;
end

2
Functions/EQ_structures/vnle_postfilter_mlse.m
View File

@@ -283,7 +283,7 @@ if ~isempty(postFFE)
showEQcoefficients('n1', postFFE.e, "displayname", 'Coefficients', 'fignum', 338);
end
showEQcoefficients('n1', eq_.e,'n2', eq_.e2,'n3', eq_.e3, "displayname", 'Coefficients', 'fignum', 339);
% showEQcoefficients('n1', eq_.e1,'n2', eq_.e2,'n3', eq_.e3, "displayname", 'Coefficients', 'fignum', 339);
showEQfilter(eq_.e, eq_signal_sd.fs.*2);
figure(340); clf;

140
Functions/Theory/Dissertation/mach_zehnder_modulator.m Normal file
View File

@@ -0,0 +1,140 @@
% Minimal MZM transfer-function demo (sinusoidal drive) — aligned with your notation
%
% Implements exactly:
% E_out(t) = E0 * exp(j*w0*t) * exp(-j*w0*L*n_eff/c0) * 1/2 * [ exp(-j*phi1(t)) + rho*exp(-j*phi2(t)) ]
% with phi_{1,2}(t) = pi * v_{1,2}(t)/Vpi
%
% Push-pull:
% v1(t) = +v_drive(t)/2 , v2(t) = -v_drive(t)/2 => phi1 = +pi/2 * v_drive/Vpi, phi2 = -pi/2 * v_drive/Vpi
%
% And the ideal TF (rho=1):
% E_out/E_in = exp(-j*w0*L*n_eff/c0) * cos( (pi/2) * v_drive/Vpi )
%
% Note: E_in(t) = E0 * exp(j*w0*t) in this script.
% Parameters
c0 = physconst('lightspeed'); % [m/s]
lambda0 = 1310e-9; % [m]
omega0 = 2*pi*c0/lambda0;
L = 5e-3; % [m] effective phase section length (set as needed)
n_eff = 2.2; % [-] effective index (set as needed)
E0 = 1; % field amplitude (arbitrary)
Vpi = 3.2; % [V] half-wave voltage (your V_pi)
% Drive
f0 = 1e9; % [Hz]
fs = 100e9; % [Hz]
Nper = 1; % number of periods
Vpp = 0.5*Vpi; % [V] peak-to-peak of v_drive(t)
biasV = 2; % [V] differential bias added to v_drive
% Analytic
v_ = linspace(-1,2, 2001);
% Field transfer function (amplitude)
Field_mzm_analytic = cos((pi/2)*v_);
% Power transfer function (intensity)
P_mzm_analytic = Field_mzm_analytic.^2;
% Imbalance factor in YOUR notation:
rho = 1; % rho=1 -> ideal balanced MZM (collapses to ideal TF)
% Time axis + differential drive voltage v_drive(t)
T = Nper/f0;
t = (0:1/fs:T-1/fs).';
v_drive = biasV + (Vpp/2)*sin(2*pi*f0*t); % v_drive(t) (peak = Vpp/2)
% Push-pull branch voltages (consistent with v_drive = v1 - v2)
v1 = +0.5*v_drive; % arm 1
v2 = -0.5*v_drive; % arm 2
% Phases phi1, phi2
phi1 = pi * v1 / Vpi;
phi2 = pi * v2 / Vpi;
% Fields: E_in and E_out (exactly your Eq. (mzm_e_field))
E_in = E0 .* exp(1i*omega0*t);
common_phase = exp(-1i * (omega0*L*n_eff/c0)); % exp(-j*omega0*L*n_eff/c0)
E_out = E0 .* exp(1i*omega0*t) .* common_phase .* 0.5 .* ...
( exp(-1i*phi1) + rho .* exp(-1i*phi2) );
% Transfer function (numerical): E_out/E_in
H_num = E_out ./ E_in;
% Power (normalized)
Pnorm_num = abs(H_num).^2; % since |E_out/E_in|^2
% Ideal TF (analytic) for comparison (rho=1, push-pull)
H_ideal = common_phase .* cos( (pi/2) * (v_drive./Vpi) );
Pnorm_ideal = abs(H_ideal).^2;
Pnorm_math = cos( (pi/2) * (v_drive./Vpi) ).^2;
set(groot, 'defaultLegendInterpreter', 'tex');
set(groot, 'defaultAxesTickLabelInterpreter', 'tex');
set(groot, 'defaultTextInterpreter', 'tex');
% Normalized voltage axis (multiples of Vpi)
v_norm = v_drive./Vpi;
colfield = [0,0,0]; %is black
colpow = linspecer(2);
colpow = colpow(1,:);
colvdrive = linspecer(2);
colvdrive = colvdrive(2,:);
%% SIGNAL IN
figure(1); clf
plot(v_norm,t*1e9, 'LineWidth', 1.0,'Color',colvdrive); grid on;
ylabel('t [ns]'); xlabel('v_{drive}(t)/V_\pi');
title('Drive voltage (normalized)');
xlim([min(v_) max(v_)]);
%% IN/OUT (static transfer) — normalized x-axis + analytic curve
figure(2); clf
plot(v_, Field_mzm_analytic, 'LineWidth', 1.2,'LineStyle','--','Color',colfield); hold on;% analytic power TF
plot(v_, P_mzm_analytic, 'LineWidth', 1.2, 'Color',colpow); hold on;% analytic power TF
% show input time signal
plot(v_norm,-1+t*1e9, 'LineWidth', 1.0,'Color',colvdrive); grid on;
% show output time signal
plot(2+t*1e9, Pnorm_num, 'LineWidth', 1.0,'DisplayName','Intensity', 'Color',colvdrive); hold on;
plot(2+t*1e9, real(H_ideal), '--', 'LineWidth', 1.0,'DisplayName','Field','Color',colfield); hold on;
scatter(v_norm, Pnorm_num, 12, '.', 'LineWidth', 1,'MarkerEdgeColor',colvdrive);
scatter(biasV./Vpi,(cos((pi/2)*biasV./Vpi)^2),10,'Marker','o');
line([min(v_drive), min(v_drive)]./Vpi,[(cos((pi/2)*min(v_drive)./Vpi)^2), -2],'linewidth',0.5,'color','black','linestyle','--');
line([max(v_drive) max(v_drive)]./Vpi,[(cos((pi/2)*max(v_drive)./Vpi)^2), -2],'linewidth',0.5,'color','black','linestyle','--');
xline([min(v_norm) max(v_norm)])
grid on;
xlabel('v_{drive}(t)/V_\pi'); ylabel('|E_{out}/E_{in}|^2');
% legend
xlim([min(v_) max(v_)+1]);
ylim([-1 1]);
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mzm.tex');
%%
% % FIELD TF (only field here; do not mix power into this figure)
figure(3); clf
% plot(t*1e9, real(H_num), 'LineWidth', 1.0); hold on;
% plot(t*1e9, real(H_ideal), '--', 'LineWidth', 1.0,'DisplayName','Field','Color',colfield); hold on;
plot(t*1e9, Pnorm_num, 'LineWidth', 1.0,'DisplayName','Intensity', 'Color',colpow); hold on;
grid on;
xlabel('t [ns]'); ylabel('Re\{E_{out}/E_{in}\}');
legend
mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mzm_out.tex');

23
Functions/mat2tikz_improved.m Normal file
View File

@@ -0,0 +1,23 @@
function mat2tikz_improved(filename)
arguments
% Default to the path in your example if no argument is provided
filename (1,1) string = 'C:\Users\Silas\Documents\Dissertation\00_Examples\tikz\textfig.tikz';
end
cleanfigure;
matlab2tikz(char(filename), ...
'width','\fwidth', ...
'height','\fheight', ...
'showInfo',false, ...
'extraAxisOptions',{ ...
'legend style={font=\footnotesize}', ...
'xlabel style={font=\color{white!15!black},font=\small},',...
'ylabel style={font=\color{white!15!black},font=\small},',...
'legend columns=1', ...
'every axis/.append style={font=\scriptsize}',...
'legend columns=1',...
'legend style={at={(0.02,0.98)},font=\footnotesize,draw=black!60,rounded corners=2pt,inner sep=1pt,fill=white,column sep=6pt,anchor= north west}',...
'legend style={at={(0.02,0.98)},draw=white!0!white,font=\scriptsize,inner sep=0.1pt,fill=white,column sep=1pt,anchor= north west}',...
'every axis/.append style={font=\scriptsize}',...
});
end

0
Libs/Violinplot-Matlab-master/violinplot.m → Libs/Violinplot-Matlab-master/violinplot_community.m
View File

127
Libs/wesanderson_colors/WesPalette.m
View File

@@ -1,10 +1,13 @@
classdef WesPalette
% WESPALETTE Wes Anderson color palettes with auto-completion
% Usage:
% cmap = WesPalette.Zissou1.rgb()
% cmap = WesPalette.Zissou1.rgb(3)
% https://github.com/karthik/wesanderson?tab=readme-ov-file
% cmap = WesPalette.Zissou1.rgb() % full palette
% cmap = WesPalette.Zissou1.rgb(3) % 3 colors (discrete default)
% cmap = WesPalette.Zissou1.rgb(12,"discrete") % any n, no interpolation
% cmap = WesPalette.Zissou1.rgb(256,"continuous") % smooth colormap (Lab interpolation)
%
% Requires:
% - colorspace.m (Pascal Getreuer) on MATLAB path for "continuous" mode
enumeration
BottleRocket1
@@ -34,21 +37,33 @@ classdef WesPalette
end
methods
function cmap = rgb(obj, n)
function cmap = rgb(obj, n, mode)
% Return palette as Nx3 RGB colormap [0–1]
%
% n : number of requested colors (optional)
% mode : "discrete" (default) or "continuous"
hex = obj.hex();
base_hex = obj.hex();
base_rgb = WesPalette.hex2rgb(base_hex);
rgb = hex2rgb(hex);
if nargin == 2
if n > size(rgb,1)
error('Requested %d colors, but only %d available.', ...
n, size(rgb,1))
if nargin < 2 || isempty(n)
cmap = base_rgb;
return;
end
cmap = rgb(1:n,:);
if nargin < 3 || isempty(mode)
mode = "discrete";
end
mode = lower(string(mode));
validateattributes(n, {'numeric'}, {'scalar','integer','positive'}, mfilename, 'n');
if mode ~= "discrete" && mode ~= "continuous"
error('mode must be "discrete" or "continuous".');
end
if mode == "discrete"
cmap = WesPalette.sample_discrete(base_rgb, n);
else
cmap = rgb;
cmap = WesPalette.interpolate_continuous_lab(base_rgb, n);
end
end
end
@@ -56,78 +71,116 @@ classdef WesPalette
methods (Access = private)
function hex = hex(obj)
% Internal HEX storage
switch obj
case WesPalette.BottleRocket1
hex = {'#A42820','#5F5647','#9B110E','#3F5151','#4E2A1E','#550307','#0C1707'};
case WesPalette.BottleRocket2
hex = {'#FAD510','#CB2314','#273046','#354823','#1E1E1E'};
case {WesPalette.Rushmore1, WesPalette.Rushmore}
hex = {'#E1BD6D','#EABE94','#0B775E','#35274A','#F2300F'};
case WesPalette.Royal1
hex = {'#899DA4','#C93312','#FAEFD1','#DC863B'};
case WesPalette.Royal2
hex = {'#9A8822','#F5CDB4','#F8AFA8','#FDDDA0','#74A089'};
case WesPalette.Zissou1
hex = {'#3B9AB2','#78B7C5','#EBCC2A','#E1AF00','#F21A00'};
case WesPalette.Zissou1Continuous
hex = {'#3A9AB2','#6FB2C1','#91BAB6','#A5C2A3','#BDC881', ...
'#DCCB4E','#E3B710','#E79805','#EC7A05','#EF5703','#F11B00'};
case WesPalette.Darjeeling1
hex = {'#FF0000','#00A08A','#F2AD00','#F98400','#5BBCD6'};
case WesPalette.Darjeeling2
hex = {'#ECCBAE','#046C9A','#D69C4E','#ABDDDE','#000000'};
case WesPalette.Chevalier1
hex = {'#446455','#FDD262','#D3DDDC','#C7B19C'};
case WesPalette.FantasticFox1
hex = {'#DD8D29','#E2D200','#46ACC8','#E58601','#B40F20'};
case WesPalette.Moonrise1
hex = {'#F3DF6C','#CEAB07','#D5D5D3','#24281A'};
case WesPalette.Moonrise2
hex = {'#798E87','#C27D38','#CCC591','#29211F'};
case WesPalette.Moonrise3
hex = {'#85D4E3','#F4B5BD','#9C964A','#CDC08C','#FAD77B'};
case WesPalette.Cavalcanti1
hex = {'#D8B70A','#02401B','#A2A475','#81A88D','#972D15'};
case WesPalette.GrandBudapest1
hex = {'#F1BB7B','#FD6467','#5B1A18','#D67236'};
case WesPalette.GrandBudapest2
hex = {'#E6A0C4','#C6CDF7','#D8A499','#7294D4'};
case WesPalette.IsleofDogs1
hex = {'#9986A5','#79402E','#CCBA72','#0F0D0E','#D9D0D3','#8D8680'};
case WesPalette.IsleofDogs2
hex = {'#EAD3BF','#AA9486','#B6854D','#39312F','#1C1718'};
case WesPalette.FrenchDispatch
hex = {'#90D4CC','#BD3027','#B0AFA2','#7FC0C6','#9D9C85'};
case WesPalette.AsteroidCity1
hex = {'#0A9F9D','#CEB175','#E54E21','#6C8645','#C18748'};
case WesPalette.AsteroidCity2
hex = {'#C52E19','#AC9765','#54D8B1','#B67C3B','#175149','#AF4E24'};
case WesPalette.AsteroidCity3
hex = {'#FBA72A','#D3D4D8','#CB7A5C','#5785C1'};
end
end
end
methods (Static, Access = private)
function rgb = hex2rgb(hex)
% hex: cellstr like {'#RRGGBB', ...}
if isstring(hex), hex = cellstr(hex); end
n = numel(hex);
rgb = zeros(n,3);
for i = 1:n
h = char(hex{i});
if startsWith(h,'#'), h = h(2:end); end
if numel(h) ~= 6
error('Invalid HEX color: %s', hex{i});
end
rgb(i,1) = hex2dec(h(1:2))/255;
rgb(i,2) = hex2dec(h(3:4))/255;
rgb(i,3) = hex2dec(h(5:6))/255;
end
end
function cmap = sample_discrete(base_rgb, n)
% No interpolation; allow any n by sampling/repeating.
k = size(base_rgb,1);
if n <= k
idx = round(linspace(1, k, n)); % spread across palette
idx = max(1, min(k, idx));
cmap = base_rgb(idx,:);
else
reps = floor(n / k);
rmd = mod(n, k);
cmap = [repmat(base_rgb, reps, 1); base_rgb(1:rmd,:)];
end
end
function cmap = interpolate_continuous_lab(base_rgb, n)
% Smooth interpolation in Lab using colorspace().
% Requires colorspace.m by Pascal Getreuer on MATLAB path.
k = size(base_rgb,1);
if k == 1
cmap = repmat(base_rgb, n, 1);
return;
end
% Convert to Lab, interpolate each channel, convert back
lab = colorspace('Lab<-RGB', base_rgb);
t_base = linspace(0, 1, k);
t_new = linspace(0, 1, n);
lab_new = zeros(n,3);
for c = 1:3
lab_new(:,c) = interp1(t_base, lab(:,c), t_new, 'linear');
end
rgb_new = colorspace('RGB<-Lab', lab_new);
% Clamp to displayable gamut
cmap = min(max(rgb_new, 0), 1);
end
end
end

4
Libs/wesanderson_colors/minimal_example_wespalette.m
View File

@@ -5,8 +5,8 @@ y2 = 1e0 ./ (1 + exp(-0.4*(x-12))); % NLPN
y3 = 1e-6 * 10.^(0.45*x); % RP on gamma
y4 = 1e-2 * 10.^(0.18*(x-8)); % RP on beta2
cmap = WesPalette.AsteroidCity1.rgb(4);
cmap = linspecer(4);
cmap = WesPalette.AsteroidCity1;
% cmap = linspecer(4);
figure1=figure(202998);clf;hold on
lw = 0.8; ms = 4;
plot(x,y1,'LineWidth',lw,'Color',cmap(1,:),'Marker','o','MarkerEdgeColor',cmap(1,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms);

205
projects/FSO_transmission/first_analysis.m
View File

@@ -1,15 +1,15 @@
base = "C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC";
base = "C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC";
mode = 0; %0 oder 1
M = 2;
all_files = dir(fullfile(base, "**/*.mat"));
if M == 2
tx_data = load("C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\14G_PAM2\tx_info\tx_info_PAM2_14Gbd0.75RRC.mat");
tx_data = load("C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\14G_PAM2\tx_info\tx_info_PAM2_14Gbd0.75RRC.mat");
filename = fullfile(base, "14G_PAM2\M=2_Rs=1.4e10_Fs=8e10_I=265mA_RoP=46.3mW_L=31m_PS=RRC_rolloff=0.75_Mode=Rise.mat");
elseif M == 4
tx_data = load("C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\6G_PAM4\tx_info\tx_info_PAM4_6Gbd0.6RRC.mat");
tx_data = load("C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\6G_PAM4\tx_info\tx_info_PAM4_6Gbd0.6RRC.mat");
filename = fullfile(base, "6G_PAM4\M=4_Rs=6e9_Fs=8e10_I=255mA_RoP=42.3mW_L=31m_PS=RRC_rolloff=0.6_Mode=Rise.mat");
end
@@ -53,10 +53,14 @@ Bits_ = PM.demap(Symbols);
assert(ber == 0);
%% For comparison, apply pulsef on Tx Symbols
Pform = Pulseformer("fsym",fsym,"fdac",fs,"pulse","rc","pulselength",16,"alpha",rolloff);
Digi_sig_compare = Pform.process(Symbols);
MF = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
Rx_sig_compare = MF.process(Digi_sig_compare);
Pform = Pulseformer("fsym",fsym,"fdac",fs,"pulse","rrc","pulselength",16,"alpha",rolloff);
Digi_sig_tx_compare = Pform.process(Symbols);
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
Digi_sync = Pform.process(Symbols);
MF = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
Rx_sig_compare = MF.process(Digi_sig_tx_compare);
%%
@@ -68,53 +72,59 @@ scoperead_volts = (traceData.RawData - traceData.YReference) * traceData.YIncrem
demystified = isequal(traceData.YData,scoperead_volts);
assert(demystified);
timesig_compare = [0:1:datas.tr.lastData(1).trace.ch3.Points-1] ./ fs;
timesig = datas.tr.lastData(1).trace.ch3.XData;
assert(isequal(traceData.YData,scoperead_volts));
Scope_sig = Electricalsignal(traceData.YData,"fs",fs);
Scope_sig.plot("displayname",'raw','fignum',100);
Scope_sig.spectrum("displayname",'raw','fignum',101)
%%
% 1) matched filter
% pulse is symmetric, hence we can use pulsef firectly as matched filter.
% pulse is symmetric, hence we can use pulsef directly as matched filter.
% It feels off (bit I think correct) that the fsym is now the output freq.!!
% -> output 2 sps to omit timing recovery!?
Pform = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1);
apply_matched_filter = 1;
k = 1;
if apply_matched_filter
Pform = Pulseformer("fsym",fsym,"fdac",k*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1);
Rx_matched = Pform.process(Scope_sig);
Rx_matched.spectrum("displayname",'Signal after matched filter','fignum',1);
else
Rx_matched = Filter('filtdegree',4,"f_cutoff",fsym*0.5,"fs",Scope_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scope_sig);
Rx_matched = Rx_matched.resample("fs_out",k*fsym);
end
Rx_matched.spectrum();
%%
sys = comm.SymbolSynchronizer('TimingErrorDetector', 'Gardner (non-data-aided)', ...
'SamplesPerSymbol', 2, ...
'DampingFactor', 0.7, ...
'NormalizedLoopBandwidth', 0.01);
Rx_symbolsync = Rx_matched;
[Rx_symbolsync.signal, timing_error] = sys(Rx_matched.signal);
coefficients = arburg(Rx_matched.signal,25);
plot(timing_error); % If this is a ramp, you have drift!
figure()
[h,w] = freqz(1,coefficients,Rx_matched.length,"whole",Rx_matched.fs);
h = h/max(abs(h));
hold on
w_ = (w - Rx_matched.fs/2);
plot(w_.*1e-9,20*log10(fftshift(abs(h))),'DisplayName',['Burg Coeffs: ', num2str(round(coefficients,2)), ' '],'LineWidth',2);
%% timing sync -> at this point we still have no symbol timing recovery, we
% % try to do this with 2sps EQ!
[~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_symbolsync.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 1);
%% not working..
%% Timing Rec
apply_timing_rec = 1;
if apply_timing_rec
[Rx_symbolsync, timing_error] = Timing_Recovery("timing_error_detector",'Gardner (non-data-aided)','sps',k,'damping_factor',0.1,'normalized_loop_bandwidth',0.1,'detector_gain',2.7).process(Rx_matched);
figure();plot(timing_error);
Rx_symbolsync.fs = fsym;
% Tsynch
[~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_symbolsync.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
Rx_synced = Rx_synced_cell{1};
len_tr = 4096*2;
mu_ffe1 = 0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dc = 0.005;
mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
mu_dfe = 0.0004;
duob_mode = db_mode.no_db;
Rx_synced.plot("displayname",'RX: Matched+Sync+2sps','fignum',2);
Digi_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped','fignum',1,'normalizeTo0dB',0);
Rx_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped + matched filtered ','fignum',1,'normalizeTo0dB',0);
Rx_synced.normalize("mode","rms").spectrum("displayname",'RX: matched filtered + synced','fignum',1,'normalizeTo0dB',0);
sps = 1;
else
% Tsynch
[~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_matched.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
Rx_synced = Rx_synced_cell{1};
Rx_synced = Rx_synced.resample("fs_out",2*fsym);
sps = 2;
end
if M == 2
ber_in_paper = 10^(-2.6); %fig 3a) 4 Gb/s MWIR FSO Transmission using Directly Modulated QCL and an Uncooled UTC-PD at Room-Temperature
@@ -122,50 +132,121 @@ elseif M == 4
ber_in_paper = 10^(-2.5);
end
%% -------------------- FFE --------------------
%%
Rx_synced = Rx_synced_cell{1};
% -------------------- FFE --------------------
% requires some more digging what is going on :-)
eq_ffe = EQ("Ne",[50, 5, 5],"Nb",[2,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
eq_ffe = EQ("Ne",[50, 1, 1],"Nb",[2,0,0], ...
"training_length",512,"training_loops",5,"dd_loops",5, ...
"K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
ffe_results = ffe(eq_ffe,M,Rx_synced,Symbols,Bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
vars = logspace(-4,-3,36);
parfor i = 1:numel(vars)
len_tr = 4096;
mu_ffe1 = 0.01;% mus(i);%0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dc = 0.005;
mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
mu_dfe = vars(i);
duob_mode = db_mode.no_db;
% requires some more digging what is going on :-)
eq_ffe_1 = EQ("Ne",[150, 1, 0],"Nb",[50,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
eq_ffe_2 = FFE("epochs_tr",1,"epochs_dd",vars(i),"len_tr",4096,"mu_dd",vars(i),"mu_tr",vars(i),"order",999,"sps",1,"decide",0, "adaption",adaption_method.nlms,"dd_mode",0);
% eq_ffe_2 = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"ffe_mu_dd",1e-5,"dfe_mu_dd",mus(i),"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",50,"dfe_order",10,"sps",1,"decide",1);
ffe_results = ffe(eq_ffe_1,M,Rx_synced,Symbols,Bits, ...
"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
"eth_style_symbol_mapping",mapping_style);
% ffe_results.metrics.print
fprintf('My EQ: %.1e \n',ffe_results.metrics.BER);
ffe_results.metrics.BER
bers(i) = ffe_results.metrics.BER;
end
figure();
plot(vars,bers);
yline(ber_in_paper)
beautifyBERplot();
%
fprintf('Paper: %.1e \n \n',ber_in_paper);
ffe_results.metrics.print("description",'FFE');
fprintf('FFE: %.1e \n',ffe_results.metrics.BER);
%% -------------------- VNLE + MLSE --------------------
len_tr = 4096;
mu_ffe1 = 0.0001;% mus(i);%0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dc = 0.005;
mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
mu_dfe = 0.0004;
duob_mode = db_mode.no_db;
pf_ncoeffs = 1;
eq_v = EQ("Ne",[100, 5, 5],"Nb",[0, 0, 0], ...
pf_ncoeffs = 4;
vars = 1:7;
bers = zeros(size(vars));
parfor i = 1:numel(vars)
eqv = EQ("Ne",[200, 1, 0],"Nb",[2, 0, 0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
pf_ncoeffs = vars(i);
pf = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0,"eth_style",mapping_style).levels);
[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Rx_synced, Symbols, Bits, ...
[vnle_results, mlse_results] = vnle_postfilter_mlse(eqv, pf, mlse_, M, Rx_synced, Symbols, Bits, ...
"precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
mlse_results.metrics.print("description",'MLSE')
fprintf('My EQ: %.1e \n',mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
vnle_results.metrics.print("description",'VNLE');
mlse_results.metrics.print("description",'MLSE');
bers(i) = mlse_results.metrics.BER;
end
%%
figure();hold on
plot(vars,bers_ffe,'DisplayName','FFE [200,0,0] + PF + MLSE');
plot(vars,bers_vnle,'DisplayName','VNLE [200,1,0] + PF + MLSE');
plot(vars,bers_vnledfe,'DisplayName','VNLE [200,1,0] + DFE [2] + PF + MLSE');
plot(vars,bers_vnledfe_ideal,'DisplayName','VNLE [200,1,0] + ideal DFE [2] + PF + MLSE');
yline(ber_in_paper);
yline([2e-2, 4.85e-3, 3.8e-3, 2,2e-4],'LineWidth',2,'Color',[0.8,0.8,0.8],'LineStyle',':','HandleVisibility','off');
ylim([1e-5,0.1]);
beautifyBERplot();
%% -------------------- DB target --------------------
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);
eq_ = EQ("Ne",[50, 5, 5],"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
"K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
dbt_results = duobinary_target(eq_,mlse_db_, M, Rx_synced, Symbols, Bits, ...
"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", [],"eth_style_symbol_mapping",mapping_style);
dbt_results.metrics.print("description",'Duobinary');
mlse_results.metrics.print
fprintf('My EQ: %.1e \n',dbt_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
dbt_results.metrics.print("description",'Duobinary');
%%
%ML-based MLSE (L=2)
mu_ml = 0.01; training_epochs = 100;
ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
"len_tr",len_tr,"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",1, ...
"traceback_depth",128,"L",3,"delta",4,"adaptive_mu",0);
[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode);
ml_mlse_results.metrics.print("description",'ML ');

26
projects/HighSpeedExperiment_2024/Auswertung_JLT/final/Copy_of_FIGURE_WAVELENGTH.m
View File

@@ -6,7 +6,7 @@ db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
pam_levels = [4, 6, 8]; % three tiles
bitrate_set = 360e9;
fiberL = 10;
fiberL = 2;
fields = [
db.getTableFieldNames('power_state_info');
@@ -96,7 +96,7 @@ end
%% ============================================================
% PLOT — 1×3 (PAM-4, PAM-6, PAM-8)
% ============================================================
fig = figure(9110); clf;
fig = figure(9112); clf;
tiledlayout(1,3,'TileSpacing','compact','Padding','compact');
lw = 1.8;
@@ -223,19 +223,19 @@ ylabel('');
end
pos = 1e3.*[2.7770 1.2017 1.4000 0.3200];
set(fig, 'Position', pos);
% pos = 1e3.*[2.7770 1.2017 1.4000 0.3200];
% set(fig, 'Position', pos);
%% === EXPORT ===
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\wavelength_analysis.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\wavelength_analysis.tikz';
% matlab2tikz(outfile, ...
% 'width','\fwidth', ...
% 'height','\fheight', ...
% 'showInfo',false, ...
% 'extraAxisOptions',{ ...
% 'legend style={font=\footnotesize}', ...
% 'legend columns=1' ...
% });

36
projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_EYES.m
View File

@@ -2,7 +2,7 @@ dsp_options.storage_path = 'Z:\2024\sioe_labor\';
dsp_options.max_occurences = 1;
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
rate = [300e9];
cols = cbrewer2('BuPu',25);
cols = [cols(end-10:2:end,:)];
cols = cbrewer2('Set1',6);
@@ -12,10 +12,10 @@ fig=figure(fignum);clf;
dbmode = 0;
% 1 - PAM 4 with preemphasis
fp = QueryFilter();
M = 6;
M = 8;
rate = [360e9];
fp.where('Runs', 'pam_level','EQUALS', M);
fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
fp.where('Runs', 'fiber_length','EQUALS', 2);
@@ -23,7 +23,7 @@ fp.where('Runs', 'wavelength','EQUALS', 1310);
fp.where('Runs', 'db_mode','EQUALS', dbmode);
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
[dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
dataTable = queryRunid(dataTable.run_id, database);
fsym = dataTable.symbolrate;
@@ -33,14 +33,36 @@ duob_mode = db_mode(strrep(dataTable.db_mode,'"',''));
% Load and Sync signal data from DB
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(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');
%%%%%% SNR CHEAT - Avges the measured signal occurences found after correlation in "tsynch" %%%%%%
average_signals = 1;
if average_signals
Scpe_sig_avg = Scpe_sig_syncd;
scope_mean = zeros(size(Scpe_cell{1}.signal));
for n=1:numel(Scpe_cell)
scope_mean = scope_mean + Scpe_cell{n}.signal;
end
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 = Scpe_sig_avg.*1.25;
Scpe_sig_avg.eye(fsym,M,"fignum",rate.*1e-9*M,"displayname",' Eye of AVG Signal');
end
% Preprocess signal
Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
Scpe_sig = preprocessSignal(Scpe_sig_avg, Symbols, fsym);
Scpe_sig.eye(fsym,M,"fignum",M*10);
%% === EXPORT TO TIKZ ===
% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\eye_pam_',num2str(M),'.tikz'];
% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\vnle_optimization.tikz'];
% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G_submission\media\matlab2tikz\eye_pam_',num2str(M),'-2.tikz'];
% % outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\vnle_optimization.tikz'];
% matlab2tikz(outfile, ...
% 'width','\fwidth', ...
% 'height','\fheight', ...

146
projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_introduction.m
View File

@@ -1,83 +1,139 @@
tablename = 'C:\Users\Silas\Documents\latex\JLT_400G_submission\HighSpeedExperiments_oneandonly_csv.csv';
% Returns a Table
data = readtable(tablename,"Delimiter",';','DecimalSeparator',',');
%% ============================================================
% PLOT
% ============================================================
%% 1. DATA EXTRACTION & SETUP
%% 1. DATA EXTRACTION & SETUP
raw_M = data.M;
raw_baud = data.BaudRate;
raw_net = data.NetRate;
raw_codes = string(data.ZoteroCode);
raw_names = string(data.Name);
raw_band = string(data.Band);
% Filter Valid Data
target_M = [2, 4, 6, 8];
validIdx = ismember(raw_M, target_M) & ~isnan(raw_baud) & ~isnan(raw_net);
Mvals = raw_M(validIdx);
baud = raw_baud(validIdx);
netrate = raw_net(validIdx);
codes = raw_codes(validIdx);
names = raw_names(validIdx);
bands = raw_band(validIdx);
pam_list = target_M;
colors = flip(cbrewer2('SET1',4));
%% 2. PLOT (For Visual Check only)
figure; hold on;
ms = 32; % scatter size
lw = 0.8; % line width
for k = 1:4 % PAM-2/4/6/8
ms = 20;
lw = 0.5;
for k = 1:length(pam_list)
M = pam_list(k);
idxPam = (Mvals == M);
% Extract for this PAM
x = baud(idxPam);
y = netrate(idxPam);
b = bands(idxPam);
n = names(idxPam);
% Get color for this PAM format
col = colors(k,:);
% ----- LEGEND FLAG (only add one entry per PAM) -----
firstLegend = true;
% ---- PLOT ALL POINTS (marker based on publication) ----
for i = 1:sum(idxPam)
% marker selection by publication
pubIdx = find(pub_list == n(i), 1);
marker = markerlist{mod(pubIdx-1, nMarkers) + 1};
% Marker Logic
ms = 20;
if strcmpi(b(i), 'O')
marker = 'o';
elseif strcmpi(b(i), 'C')
marker = 'd';
else
marker = 's';
end
if strcmpi(n(i), 'THIS WORK')
marker = 'pentagram';
ms = 100;
end
% Plot Scatter
if firstLegend
h = scatter(x(i), y(i), ms, ...
'Marker', marker, ...
'MarkerEdgeColor', col, ...
'MarkerFaceColor', col, ...
scatter(x(i), y(i), ms, 'Marker', marker, ...
'MarkerEdgeColor', col, 'MarkerFaceColor', col, ...
'DisplayName', sprintf('PAM-%d', M));
firstLegend = false;
else
h = scatter(x(i), y(i), ms, ...
'Marker', marker, ...
'MarkerEdgeColor', col, ...
'MarkerFaceColor', col, ...
scatter(x(i), y(i), ms, 'Marker', marker, ...
'MarkerEdgeColor', col, 'MarkerFaceColor', col, ...
'HandleVisibility','off');
end
% ====== CUSTOM DATATIP CONTENT ======
dt = h.DataTipTemplate;
dt.DataTipRows(1).Label = 'Baud rate';
dt.DataTipRows(2).Label = 'Net rate';
% Add publication name
dt.DataTipRows(end+1) = dataTipTextRow('Publication', n(i));
end
% ---- Fit (PAM-specific) ----
valid = ~isnan(x) & ~isnan(y);
if sum(valid) >= 3
p = polyfit(x(valid), y(valid), 2);
xfit = linspace(min(x(valid)), max(x(valid)), 200);
yfit = polyval(p, xfit);
plot(xfit, yfit, ':', ...
'LineWidth', lw, ...
'Color', col, ...
'HandleVisibility', 'off'); % do NOT add to legend
% Fit lines
if length(x) >= 3
[p, S, mu] = polyfit(x, y, 2);
xfit = linspace(min(x), max(x), 200);
yfit = polyval(p, xfit, S, mu);
plot(xfit, yfit, '-', 'LineWidth', lw, 'Color', col, 'HandleVisibility', 'off');
end
end
grid on;
grid on; box on;
xlabel('Baud rate [GBd]');
ylabel('Net rate [Gb/s]');
% title('Check Command Window for TikZ Code');
% legend('Location','northwest');
legend('Location','northwest');
set(gca,'FontSize',11);
%% 3. GENERATE TIKZ ANNOTATION CODE
% This prints the manual \draw commands to the console
%% GENERATE TIKZ ANNOTATION CODE
% This prints the manual \draw commands to the console
%% GENERATE TIKZ ANNOTATION CODE (Colored Borders + Tiny Font)
%% GENERATE TIKZ ANNOTATION CODE (No Arrow, Close Text)
fprintf('\n\n%% ===========================================================\n');
fprintf('%% COPY THE FOLLOWING LINES INTO YOUR .TEX FILE \n');
fprintf('%% (Paste them just before \\end{axis})\n');
fprintf('%% ===========================================================\n\n');
for i = 1:length(baud)
bx = baud(i);
by = netrate(i);
key = codes(i);
M_val = Mvals(i);
% --- PLACEMENT LOGIC ---
if M_val == 8
% PAM-8: Place Top-Left
% 'south east' anchor means the text's bottom-right corner touches the coordinate
% shift moves it slightly up and left to clear the marker
anchorStr = 'south east';
shiftStr = 'shift={(-3pt, 3pt)}';
else
% Others: Place Bottom-Right
% 'north west' anchor means the text's top-left corner touches the coordinate
% shift moves it slightly down and right
anchorStr = 'north west';
shiftStr = 'shift={(3pt, -3pt)}';
end
% --- PRINT COMMAND ---
% Uses \node directly at the coordinate (axis cs:...)
fprintf('\\node[anchor=%s, %s, font=\\tiny, fill=white, inner sep=1pt] at (axis cs:%.2f, %.2f) {\\cite{%s}};\n', ...
anchorStr, shiftStr, bx, by, key);
end
fprintf('\n')
%% === EXPORT ===
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\highspeedresults.tikz';
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G_submission\media\matlab2tikz\highspeedresults_test.tikz';
matlab2tikz(outfile, ...
'width','\fwidth', ...
'height','\fheight', ...

8
projects/HighSpeedExperiment_2024/Auswertung_JLT/run_dsp_from_db.m
View File

@@ -42,14 +42,14 @@ fp = QueryFilter();
% fp.where('Runs', 'run_id','EQUALS', 2776);
M = 6;
fp.where('Runs', 'pam_level','EQUALS', M);
% fp.where('Runs', 'bitrate','EQUALS', 390e9);%360,390
fp.where('Runs', 'bitrate','EQUALS', 360e9);%360,390
% fp.where('Runs', 'symbolrate','EQUALS', 195e9);
fp.where('Runs', 'fiber_length','EQUALS', 10);
fp.where('Runs', 'fiber_length','EQUALS', 2);
fp.where('Runs', 'is_mpi','EQUALS', 0);
% fp.where('Runs', 'interference_path_length','EQUALS', 1000);
% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
% fp.where('Runs', 'sir','EQUALS',18);
% fp.where('Runs', 'wavelength','EQUALS', 1310);
fp.where('Runs', 'wavelength','EQUALS', 1310);
fp.where('Runs', 'db_mode','EQUALS', 0);
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
% fp.where('Runs', 'power_pd_in','LESS_THAN', 7);
@@ -70,7 +70,7 @@ wh.addStorage("mlmlse_package");
%% === RUN IT ===
[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "parallel", 'wh', wh, 'waitbar', true);
[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "serial", 'wh', wh, 'waitbar', true);

67
projects/HighSpeedExperiment_2024/a_minimal_example.m
View File

@@ -4,29 +4,35 @@ dsp_options.storage_path = 'Z:\2024\sioe_labor\';
dsp_options.max_occurences = 1;
db = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
fp = QueryFilter();
% fp = QueryFilter();
%
% fp.where('Runs','fiber_length','EQUALS', 2);
% fp.where('Runs','wavelength','EQUALS', 1310);
% fp.where('Runs','bitrate','EQUALS', 300e9);
% 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,~] = db.queryDB(fp, fields);
%
fp.where('Runs','fiber_length','EQUALS', 2);
fp.where('Runs','wavelength','EQUALS', 1310);
fp.where('Runs','bitrate','EQUALS', 300e9);
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,~] = db.queryDB(fp, fields);
run_ids = [ 993 1205 1413 1623 1833 2043 2253 2628 2836 2958 3000 3042 3323 5098 5225 5267 5309];
for id = run_ids
fp = QueryFilter();
fp.where('Runs', 'run_id','EQUALS', id);
fields = db.getTableFieldNames('power_state_info');
fields = [fields; db.getTableFieldNames('Runs')];
fields = [fields; db.getTableFieldNames('dashboard_ungrouped_alltime')]; %dashboard_ungrouped_after_nov_2025 dashboard_ungrouped_aug_nov_2025
fields = unique(fields);
[dataTable,~] = db.queryDB(fp, fields);
fsym = dataTable(1,:).symbolrate;
M = double(dataTable(1,:).pam_level);
duob_mode = db_mode(strrep(dataTable(1,:).db_mode,'"',''));
% Load and Sync signal data from DB
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable(1,:), dsp_options);
@@ -36,24 +42,24 @@ Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
% Show spectrum
Scpe_sig.spectrum("fignum",1,"displayname",'Rx')
% meta = struct();
% meta.varnames = dataTable.Properties.VariableNames;
%
% for k = 1:numel(meta.varnames)
% v = meta.varnames{k};
% col = dataTable.(v);
%
% if isnumeric(col) || islogical(col)
% meta.(v) = col;
% elseif isstring(col)
% meta.(v) = cellstr(col);
% elseif iscellstr(col)
% meta.(v) = col;
% else
% error("Unsupported table column type: %s", class(col))
% end
% end
% exp_data.metadata = meta;
meta = struct();
meta.varnames = dataTable.Properties.VariableNames;
for k = 1:numel(meta.varnames)
v = meta.varnames{k};
col = dataTable.(v);
if isnumeric(col) || islogical(col)
meta.(v) = col;
elseif isstring(col)
meta.(v) = cellstr(col);
elseif iscellstr(col)
meta.(v) = col;
else
error("Unsupported table column type: %s", class(col))
end
end
exp_data.metadata = meta;
exp_data = struct();
exp_data.metadata = dataTable;
@@ -68,6 +74,7 @@ filename = filename + ext;
savepath = fullfile('F:\2024\sioe_labor\export_skuehl\',filename);
save(savepath,'exp_data','-v7.3');
end

2
projects/HighSpeedExperiment_2024/baudrate_evaluation.m
View File

@@ -1,5 +1,5 @@
filename = "C:\Users\sioe\Documents\High_Speed_Measurement_2024\baudrate_sweep_b2b\PAMX_b2b_baudrate20241024_210648_wh.mat";
filename = "F:\2024\sioe\High Speed Messungen Oktober\baudrate_sweep_b2b\PAMX_b2b_baudrate20241024_210648_wh_final.mat";
a = load(filename);
wh = a.obj;

50
projects/IMDD_base_system/minimal_example.m
View File

@@ -8,7 +8,7 @@ fdac = 256e9;
fadc = 256e9;
random_key = 1;
rcalpha = 0.05;
rcalpha = 0.6;
kover = 16;
duob_mode = db_mode.no_db;
@@ -25,7 +25,7 @@ tx_bw_nyquist = 0.8;
link_length = 1;
% RX
rop = -8;
rop = -9;
rx_bw_nyquist = 0.8;
vnle_order1 = 50;
@@ -51,7 +51,7 @@ mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
mu_dfe = 0.0004;
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
"fsym",fsym,"M",M,"order",18,"useprbs",0,...
@@ -65,7 +65,7 @@ Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",1
%%%%% AWG
El_sig = M8199A("kover",kover).process(Digi_sig);
% El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1).process(Digi_sig);
El_sig.spectrum("displayname",'Digi Spectrum','fignum',100,'normalizeTo0dB',0);
El_sig.spectrum("displayname",'Digi Spectrum','fignum',1,'normalizeTo0dB',1);
% El_sig = El_sig.setPower(0,"dBm");
%%%%% Electrical Driver Amplifier %%%%%%
@@ -101,32 +101,53 @@ Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);
%%
%%%%%% Sample to 2x fsym %%%%%%
Scpe_sig = Scpe_sig.resample("fs_out",2*fsym);
Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
% 1) matched filter
% pulse is symmetric, hence we can use pulsef firectly as matched filter.
% It feels off (bit I think correct) that the fsym is now the output freq.!!
% -> output 2 sps to omit timing recovery!?
Pform = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha,"matched",1);
Scpe_sig = Pform.process(Scpe_sig);
Scpe_sig.spectrum("displayname",'Signal after matched filter','fignum',1,'normalizeTo0dB',1);
%
% %%
% %%%%%% Sample to 2x fsym %%%%%%
% Scpe_sig = Scpe_sig.resample("fs_out",2*fsym);
% Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
%%
%%%%%% Sync Rx signal with reference %%%%%%
[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",1);
Scpe_sig.spectrum("displayname",'Opt Spectrum','fignum',11,'normalizeTo0dB',1);
Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.5,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
% Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.5,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
%%
% -------------------- FFE --------------------
ffe_order = [50, 0, 0];
eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0], ...
eq_ = EQ("Ne",ffe_order,"Nb",[2,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
output.ffe_results = ffe(eq_ffe,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
% eq_ = FFE("epochs_tr",4,"epochs_dd",5,"len_tr",4096,"mu_dd",0.01,"mu_tr",0.01,"order",50,"sps",2,"decide",0, "adaption",adaption_method.nlms,"dd_mode",1);
eq_ = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",512,"ffe_mu_dd",1e-4,"dfe_mu_dd",5e-4,"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",99,"dfe_order",99,"sps",2,"decide",0);
output.ffe_results = ffe(eq_,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
"eth_style_symbol_mapping",0);
output.ffe_results.metrics.print
%%
% -------------------- VNLE + MLSE --------------------
pf_ncoeffs = 1;
ffe_order3 = [50, 5, 5];
@@ -139,8 +160,11 @@ pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
[output.vnle_results, output.mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", [], "eth_style_symbol_mapping", 0);
"precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", 0);
output.mlse_results.metrics.print
%%
% -------------------- DB target --------------------
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);

88
projects/Lab_analysis/data_analysis_and_plot_scripts/lambda_vs_spectrum.m Normal file
View File

@@ -0,0 +1,88 @@
wh_aeon = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Lab_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 = wh_thor.wh;
% Silas' custom "warehouse" datatype
wh_aeon.showInfo;
wh_thor.showInfo;
%% PLOT OSA SPECTRA
figure();hold on
cols = linspecer(4);
cols = cbrewer2('Paired',8);
subplot(1,2,1);hold on;
wh = wh_thor;
ccnt = 1;
pumps = wh.parameter.pump.values;
plasers = wh.parameter.laserpower.values;
scnt = 1;
for p = 1:numel(plasers)
for pmp = 1:numel(pumps)
subplot(numel(plasers),numel(pumps),scnt);hold on;
title(sprintf('Laser: %d dBm; Pump: %d %',plasers(p),pumps(pmp)),"Interpreter","tex")
for lambda = wh.parameter.lambda.values
spectrum_osa = wh_thor.getStoValue('spectrum_osa',plasers(p),lambda,pumps(pmp));
wavelength_osa = wh_thor.getStoValue('wavelength_osa',plasers(p),lambda,pumps(pmp));
plot(wavelength_osa,spectrum_osa,'DisplayName',sprintf('P_{in}: %d dB ',plasers(p)),'Color',cols(ccnt,:));
spectrum_osa = wh_aeon.getStoValue('spectrum_osa',plasers(p),lambda,pumps(pmp));
wavelength_osa = wh_aeon.getStoValue('wavelength_osa',plasers(p),lambda,pumps(pmp));
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
scnt = scnt +1;
end
ccnt = ccnt+2;
end
xlabel('Wavelength [nm]');
ylabel('OSNR [dB]')
%%
figure();hold on
cols = linspecer(4);
subplot(1,2,1);hold on;
wh = wh_thor;
ccnt = 1;
pumps = wh.parameter.pump.values;
plasers = wh.parameter.laserpower.values;
scnt = 1;
for p = 1:numel(plasers)
for pmp = 1:numel(pumps)
subplot(numel(plasers),numel(pumps),scnt);hold on;
ase_noise = wh_thor.getStoValue('pase_osa',plasers(p),wh.parameter.lambda.values,pumps(pmp));
signal = wh_thor.getStoValue('psig_osa',plasers(p),wh.parameter.lambda.values,pumps(pmp));
plot(wh.parameter.lambda.values,ase_noise,'DisplayName',sprintf('P_{in}: %d dB ',plasers(p)),'Color',cols(ccnt,:));
ase_noise = wh_aeon.getStoValue('pase_osa',plasers(p),wh.parameter.lambda.values,pumps(pmp));
signal = wh_aeon.getStoValue('psig_osa',plasers(p),wh.parameter.lambda.values,pumps(pmp));
plot(wh.parameter.lambda.values,ase_noise,'DisplayName',sprintf('P_{in}: %d dB ',plasers(p)),'Color',cols(ccnt,:),'LineStyle','-');
ylim([-60, 20]);
beautifyBERplot("logscale",false,"setmarkers",0,"setcolors",0);
scnt = scnt +1;
end
ccnt = ccnt+1;
end
xlabel('Wavelength [nm]');
ylabel('OSNR [dB]')

16
projects/ML_based_MLSE/analyze_filter_length.m
View File

@@ -5,9 +5,9 @@ M = 4;
randkey = 1;
% --- Parameter sweep
order_range = 2:3:11; % FFE order
delta_range = 0:2:4; % delta
SNR_dB = 20;
order_range = 5:5:50; % FFE order
delta_range = 0:5:20; % delta
SNR_dB = 30;
% --- Prepare bit sequence
order_bits = 19;
@@ -21,10 +21,14 @@ Symbols = PAMmapper(M,0).map(Bits);
Symbols.fs = 200e9;
% --- Channel (minimal ISI + AWGN)
h = [0.3 0.9 0.3]; h = h/norm(h);
h = abs([0.3 0.9 0.3]); h = h/norm(h);
% h = [1 -1.67085330039878 1.17918163282514 -0.805210559745616 0.571564213123367 -0.296337147529674 0.00649773445209780 0.0854177610195952 -0.0576009020965258 0.0520994427061551 -0.0624586034913656 0.0553280962699552 -0.00705582559925755 -0.0336399056707792 0.0706903719452810 -0.0334124287931977 0.0131699455037966 0.0587431373842994 -0.0515902976066452 0.00647904355473619 0.0137506750904990 -0.0547974515885928 0.00994735499340592 -0.0135513582534086 -0.00463322575007739 0.0277311946101940];
% h = h/norm(h);
symbols_filt = Symbols.filter(h,1);
symbols_noi = symbols_filt;
symbols_noi.signal = awgn(symbols_filt.signal,SNR_dB,'measured');
symbols_noi.spectrum();
% --- Generate all parameter pairs
[O,D] = ndgrid(order_range, delta_range);
@@ -38,7 +42,7 @@ ce_vec = nan(size(pairs,1),1);
ce_training = nan(size(pairs,1),training_len);
% --- Parallel loop over parameter pairs
parfor k = 1:size(pairs,1)
for k = 1:size(pairs,1)
order_k = pairs(k,1);
delta_k = pairs(k,2);
@@ -89,7 +93,7 @@ end
beautifyBERplot
ylabel('BER'); xlabel('Filter Order [N]');
title('BER vs. Filter order');
ylim([1e-4, 0.1]);
% ylim([1e-4, 0.1]);
yline(3.8e-3,'HandleVisibility','off');
yline(2.2e-4,'HandleVisibility','off');

186
projects/WDM/WDM_auswertung.m
View File

@@ -1,5 +1,5 @@
base = "C:\Users\Silas\Nextcloud\Cluster";
base = "C:\Users\Silas\Nextcloud4\Cluster";
all_files = dir(fullfile(base, "**/*.mat"));
schemes = ["co","pair","alt","seg"];
@@ -13,8 +13,8 @@ T = table('Size',[0 10], ...
rx = "^WDM_(?<date>\d{8})_(?<time>\d{6})_n(?<node>\d+)_(?<jobid>\d+)_" + ...
"(?<L>\d+)km_(?<Nch>\d+)ch_(?<df>\d+)ghz_(?<scheme>[a-z]+)_alpha(?<alpha>\d+(?:_\d+)?)\.mat$";
for k = 1:numel(all_files)
f = all_files(k);
for i = 1:numel(all_files)
f = all_files(i);
folder = string(f.folder);
file = string(f.name);
@@ -44,6 +44,8 @@ for k = 1:numel(all_files)
end
%%
idx = strcmp(T.scheme,"co") & ...
T.alpha == 0.4 & ...
T.date >= datetime(2026,1,9) & ...
@@ -51,13 +53,181 @@ idx = strcmp(T.scheme,"co") & ...
T_sel = T(idx,:);
i = 2;
res = load(fullfile(T_sel.folder(i),T_sel.file(i)),'res');
res = res.res;
% ---- Load all res ----
R = cell(numel(T_sel.file),1);
for i = 1:numel(T_sel.file)
S = load(fullfile(T_sel.folder(i),T_sel.file(i)),'res');
S.res = strip_config_from_res(S.res);
R{i} = S.res;
end
res_all = combine_res_list(R);
res_all = drop_empty_realizations(res_all);
%%
%%
%%
% Routine A: plot BER curves and compute crossings
S = plot_BER_vs_ROP(res, 'fec', 3.8e-3);
S = plot_BER_vs_ROP(res_all, 'fec', 3.8e-3);
%% Routine B: violin plot (independent)
plot_FEC_violin(res, 'tech','VNLE', 'fec',3.8e-3, 'ylim',[-10 0]);
plot_FEC_violin(res_all, 'tech','VNLE', 'fec',3.8e-3, 'ylim',[-10 0],'eval_ptr',5);
%%
function res = strip_config_from_res(res)
% Remove the "config" payload from every non-empty result cell
% Works whether entries are structs-with-field or objects-with-property.
techs = {'ffe','dfe','vnle','mlse','dbt'};
for t = 1:numel(techs)
fn = techs{t};
if ~isfield(res, fn) || isempty(res.(fn)), continue; end
C = res.(fn);
if ~iscell(C), continue; end
idx = find(~cellfun('isempty', C)); % fast builtin
for k = 1:numel(idx)
x = C{idx(k)};
% Case 1: struct entry with field "config"
if isstruct(x) && isfield(x,'config')
x = rmfield(x,'config');
% Case 2: object entry with property "config"
elseif isobject(x) && isprop(x,'config')
try
x.config = []; % lighter than keeping Equalizerstruct JSON
catch
% ignore if class forbids assignment
end
end
C{idx(k)} = x;
end
res.(fn) = C;
end
end
% ===================== Local helper functions =====================
function res_out = combine_res_list(R)
% Concatenate along realization dimension (dim=3) for all techniques.
% Requires consistent sizes in dims 1,2,4 and consistent eval_dist_km.
fieldsTech = ["ffe","dfe","vnle","mlse","dbt"];
% Start with first
res_out = R{1};
% --- Build common distance axis (union, stable) ---
dist_all = res_out.eval_dist_km(:).';
for k = 2:numel(R)
dist_all = unique([dist_all, R{k}.eval_dist_km(:).'], 'stable');
end
% --- If res_out not already on dist_all, expand it ---
if ~isequal(res_out.eval_dist_km(:).', dist_all)
res_out = expand_res_dist(res_out, dist_all);
end
% --- For each file: expand to dist_all, then merge realizations (your logic) ---
for k = 2:numel(R)
R{k} = expand_res_dist(R{k}, dist_all);
end
% Concatenate technique cell arrays along dim=3 (realizations)
for f = fieldsTech
A = res_out.(f);
for k = 2:numel(R)
B = R{k}.(f);
A = cat(3, A, B);
end
res_out.(f) = A;
end
% Update num_realiz in settings to match concatenated dim
dims = size(res_out.ffe); if numel(dims)<3, dims(3)=1; end
res_out.settings.num_realiz = dims(3);
end
function res = expand_res_dist(res, dist_all)
techs = {'ffe','dfe','vnle','mlse','dbt'};
old = res.eval_dist_km(:).';
new = dist_all(:).';
[~,loc] = ismember(old, new); % mapping old -> new positions
if any(loc==0)
error('expand_res_dist: internal: old distances not found in union.');
end
% sizes from an existing field (prefer ffe)
Cref = res.ffe;
sz = size(Cref); sz(end+1:4) = 1; % ensure 4 dims
Nch=sz(1); Nrop=sz(2); Nreal=sz(3); Nnew=numel(new);
for t = 1:numel(techs)
fn = techs{t};
if ~isfield(res,fn) || isempty(res.(fn)), continue; end
Cold = res.(fn);
sz2 = size(Cold); sz2(end+1:4) = 1;
Cnew = cell(sz2(1), sz2(2), sz2(3), Nnew);
Cnew(:,:,:,loc) = Cold; % place old distances into new axis
res.(fn) = Cnew;
end
res.eval_dist_km = new;
end
function res_out = drop_empty_realizations(res_in)
% Removes realizations where ALL entries are empty across ALL techniques
% (across channels, rop, distances).
res_out = res_in;
fieldsTech = ["ffe","dfe","vnle","mlse","dbt"];
% Determine sizes from one field
dims = size(res_in.ffe);
if numel(dims) < 4, dims(end+1:4) = 1; end
Nreal = dims(3);
% For each realization, check if there is at least one non-empty result anywhere
keep = false(1, Nreal);
for r = 1:Nreal
hasAny = false;
for f = fieldsTech
C = res_in.(f); % cell array
% Slice all ch,rop,dist for this realization
slice = C(:,:,r,:); % still a cell array
hasAny = any(~cellfun(@isempty, slice(:)));
if hasAny, break; end
end
keep(r) = hasAny;
end
fprintf('drop_empty_realizations: keeping %d/%d realizations (dropping %d)\n', ...
sum(keep), Nreal, sum(~keep));
% Apply keep mask
for f = fieldsTech
res_out.(f) = res_out.(f)(:,:,keep,:);
end
% Update settings
res_out.settings.num_realiz = sum(keep);
end

20
projects/WDM/WDM_model_old.m
View File

@@ -52,7 +52,7 @@ f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9);
margin = 25e12; % some THz left and right
f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
f_nyq = f_span/2;
kover = 8;
kover = 4;
upsample_required = f_nyq./(fdac*kover/2);
upsample_pow = 2^nextpow2(upsample_required);
upsample_ceil = ceil(upsample_required);
@@ -64,13 +64,23 @@ signal_cell = {};
Symbols = {};
Tx_bits = {};
s.rop = -6:0.75:-0.75;
s.rop = -12:0.75:-0.75;
output_ffe = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
output_vnle = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
output_mlse = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
output_dbt = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
s.p = "pair";
switch s.p
case "co"
pol_rot = 100.*ones(1,length(s.wavelengthplan));
case "pair"
pol_rot = repmat([100,100,0,0],1,length(s.wavelengthplan)/4);
case "alt"
pol_rot = repmat([100,0,100,0],1,length(s.wavelengthplan)/4);
end
for realiz = 1:s.num_realiz
@@ -99,7 +109,7 @@ for realiz = 1:s.num_realiz
%%%%% s.MODULATE E/O CONVERSION %%%%%
Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",s.wavelengthplan(l),"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",s.random_key+l+realiz).process(El_sig);
signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",100).process(Eml_out);
signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",pol_rot(l)).process(Eml_out);
end
Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover,...
@@ -107,7 +117,7 @@ for realiz = 1:s.num_realiz
Opt_sig_wdm = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",3+10*log10(N)).process(Opt_sig_wdm);
% Opt_sig_wdm.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
Opt_sig_wdm.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
% Opt_sig_wdm.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',1,'max_num_lines',2);
@@ -122,7 +132,7 @@ for realiz = 1:s.num_realiz
Dvec = getDispersionVector(nSegments, D_local, zdw, randomize_D, s.random_key+realiz);
for seg = 1:nSegments
Opt_sig_wdm_fib = DP_Fiber("L",segment_length,"D",Dvec(s),"Dpmd",pmd,"Ds",0.07,...
Opt_sig_wdm_fib = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07,...
"beat_len",10,"corr_len",100,"dz",1,"manakov",0,...
"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01,...
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1).process(Opt_sig_wdm_fib);