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6 Commits
3cafb06c4f
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8c4edf2490
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f421348e5b | ||
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ef0a74cb7f | ||
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a6cf742121 | ||
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aa210e5352 | ||
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753bc582b9 |
@@ -24,9 +24,9 @@ classdef Signal
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obj.signal = signal;
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obj.signal = signal;
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obj.signal = obj.signal;
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obj.signal = obj.signal;
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obj.fs = options.fs;
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obj.fs = options.fs;
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%
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[~,obj.gitSHA] = system('git rev-parse HEAD');
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% [~,obj.gitSHA] = system('git rev-parse HEAD');
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[~,obj.gitStatus] = system('git status --porcelain');
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% [~,obj.gitStatus] = system('git status --porcelain');
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% [~,obj.gitPatch] = system('git diff');
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% [~,obj.gitPatch] = system('git diff');
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%%% Stuff for Logbook %%%
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%%% Stuff for Logbook %%%
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File diff suppressed because it is too large
Load Diff
492
Classes/04_DSP/Equalizer/FFE_plain.m
Normal file
492
Classes/04_DSP/Equalizer/FFE_plain.m
Normal file
@@ -0,0 +1,492 @@
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classdef FFE_plain < handle
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% Plain feed-forward equalizer with training, DD mode, and BER mu tuning.
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%
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% This class intentionally contains only the core FFE tap adaptation logic.
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% MPI-specific DC tracking, A1/A2 suppression, and delayed update buffers
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% stay in FFE and the MPI_reduction classes.
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properties
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sps
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order
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e
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e_tr
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error
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len_tr
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mu_tr
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epochs_tr
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adaption_technique
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dd_mode
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mu_dd
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epochs_dd
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dd_len_fraction
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P
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constellation
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decide
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save_debug = 0
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debug_struct
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optmize_mus = 0
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mu_optimization
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mu_optimization_iter = 0
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mu_optimization_len
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plot_mu_optimization = 0
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mu_optimization_fignum = 3010
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end
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methods
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function obj = FFE_plain(options)
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arguments(Input)
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options.sps = 2
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options.order = 15
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options.len_tr = 4096
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options.mu_tr = 0
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options.epochs_tr = 5
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options.adaption_technique adaption_method = adaption_method.lms
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options.dd_mode = 1
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options.mu_dd = 1e-5
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options.epochs_dd = 5
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options.dd_len_fraction = 1
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options.decide = false
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options.save_debug = 0
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options.optmize_mus = 0
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options.mu_optimization_len = 2^15
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options.plot_mu_optimization = 0
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options.mu_optimization_fignum = 3010
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end
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fn = fieldnames(options);
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for n = 1:numel(fn)
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obj.(fn{n}) = options.(fn{n});
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end
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obj.sps = floor(obj.sps);
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obj.order = floor(obj.order);
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obj.dd_len_fraction = min(max(obj.dd_len_fraction,0),1);
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obj.resetState();
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end
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function [X,Noi] = process(obj, X, D)
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X = X.normalize("mode","rms");
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obj.constellation = unique(D.signal);
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obj.resetState();
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if obj.optmize_mus
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obj.optimizeMus(X.signal,D.signal);
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obj.resetState();
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end
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training = true;
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showviz = false;
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obj.equalize(X.signal,D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
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obj.e_tr = obj.e;
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training = false;
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n = X.length;
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if obj.dd_mode
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[signal,decision] = obj.equalize(X.signal,D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
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else
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[signal,decision] = obj.equalize(X.signal,D.signal,0,1,n,training,showviz);
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end
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if obj.decide
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X.signal = decision;
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else
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X.signal = signal;
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end
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X.fs = D.fs;
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X = X.logbookentry([num2str(obj.order),' tap FFE']);
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Noi = X - D;
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end
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function [y,d_hat] = equalize(obj,x,d,mu,epochs,N,training,showviz)
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arguments
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obj
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x
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d
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mu
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epochs
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N
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training
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showviz
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end
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unused_showviz = showviz; %#ok<NASGU>
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x = x(:);
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d = d(:);
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N = obj.validSampleLength(N,x,d);
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n_symbols = N / obj.sps;
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y = zeros(n_symbols,1);
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d_hat = zeros(n_symbols,1);
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if n_symbols == 0
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return
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end
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if isempty(obj.constellation)
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obj.constellation = unique(d);
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end
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decision_constellation = obj.constellation(:);
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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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lambda = mu;
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mask = ones(obj.order,1);
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maincursor_pos = ceil(length(obj.e)/2);
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adaption_code = obj.adaptionCode();
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if mu == 0 || (~obj.dd_mode && ~training)
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epochs = 1;
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end
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debug_enabled = obj.save_debug;
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if debug_enabled
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obj.initializeDebug(n_symbols,training);
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end
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for epoch = 1:epochs
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symbol = 0;
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for sample = 1:obj.sps:N
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symbol = symbol + 1;
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grad = zeros(obj.order,1);
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update = zeros(obj.order,1);
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weight = 0;
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U = x(obj.order+sample-1:-1:sample);
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y(symbol,1) = (obj.e.*mask).' * U;
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if training
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d_hat(symbol,1) = d(symbol);
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else
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[~,symbol_idx] = min(abs(y(symbol) - decision_constellation));
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d_hat(symbol,1) = decision_constellation(symbol_idx);
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end
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err = d_hat(symbol) - y(symbol);
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if mu ~= 0 && (training || obj.dd_mode)
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switch adaption_code
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case 1
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weight = mu / ((U.'*U) + eps);
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grad = err * U;
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update = grad * weight;
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obj.e = obj.e + update;
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case 2
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weight = mu;
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grad = err * U;
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update = grad * weight;
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obj.e = obj.e + update;
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case 3
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denom = lambda + U.' * obj.P * U;
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k = (obj.P * U) / denom;
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update = k * err;
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obj.e = obj.e + update;
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obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
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end
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end
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if debug_enabled && epoch == 1
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obj.debug_struct.error_first_epoch(1,symbol) = err * err';
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end
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if debug_enabled && epoch == epochs
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error_power = err * err';
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update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
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obj.debug_struct.error(1,symbol) = error_power;
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obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
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obj.debug_struct.mu_nlms(1,symbol) = weight;
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obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
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if training
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obj.debug_struct.error_tr(1,symbol) = error_power;
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obj.debug_struct.update_tr(1,symbol) = update_power;
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else
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obj.debug_struct.error_dd(1,symbol) = error_power;
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obj.debug_struct.update(1,symbol) = update_power;
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end
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end
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end
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end
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end
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function optimizeMus(obj,x,d)
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[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d);
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switch obj.adaption_technique
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case adaption_method.lms
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mu_range = [1e-5, 1e-2];
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case adaption_method.nlms
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mu_range = [1e-3, 5e-1];
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case adaption_method.rls
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mu_range = [0.98, 0.99999];
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otherwise
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builtin("error","FFE_plain:InvalidAdaptionTechnique", ...
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"Unsupported FFE adaption technique.");
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end
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vars = optimizableVariable("mu_tr",mu_range,"Transform","log");
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if obj.dd_mode
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vars = [vars, optimizableVariable("mu_dd",mu_range,"Transform","log")];
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end
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obj.mu_optimization_iter = 0;
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fprintf("FFE_plain mu opt uses %d samples / %d symbols\n",N_opt,numel(d_opt));
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obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x_opt,d_opt),vars, ...
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"MaxObjectiveEvaluations",20, ...
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"AcquisitionFunctionName","expected-improvement-plus", ...
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"IsObjectiveDeterministic",false, ...
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"Verbose",0, ...
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"PlotFcn",[]);
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obj.mu_tr = obj.mu_optimization.XAtMinObjective.mu_tr;
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if obj.dd_mode
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obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
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fprintf("\nFFE_plain mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e\n", ...
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obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective);
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else
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fprintf("\nFFE_plain mu opt done: mu_tr=%9.3e, BER=%9.3e\n", ...
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obj.mu_tr,obj.mu_optimization.MinObjective);
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end
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if obj.plot_mu_optimization
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obj.plotMuOptimization();
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end
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end
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function objective = muObjective(obj,params,x,d)
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old_state = obj.captureObjectiveState();
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cleanup = onCleanup(@()obj.restoreObjectiveState(old_state));
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obj.save_debug = 0;
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obj.resetState();
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N_tr = min(obj.len_tr,numel(x));
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obj.equalize(x,d,params.mu_tr,obj.epochs_tr,N_tr,true,false);
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if obj.dd_mode
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[signal,~] = obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),false,false);
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else
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[signal,~] = obj.equalize(x,d,0,1,numel(x),false,false);
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end
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[ber,errors] = obj.berObjective(signal,d);
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objective = ber;
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if ~isfinite(objective)
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objective = inf;
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end
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obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
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if obj.dd_mode
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fprintf("\rFFE_plain mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,params.mu_tr,params.mu_dd,ber,errors);
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else
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fprintf("\rFFE_plain mu opt %02d: mu_tr=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,params.mu_tr,ber,errors);
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end
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||||||
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clear cleanup
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end
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function [x_opt,d_opt,N_opt] = optimizationSignals(obj,x,d,opt_len)
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if nargin < 4
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opt_len = obj.mu_optimization_len;
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|
end
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||||||
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||||||
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N_available = min(numel(x),numel(d) * obj.sps);
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if isempty(opt_len) || opt_len <= 0 || isinf(opt_len)
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N_opt = N_available;
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|
else
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N_opt = min(N_available,max(obj.len_tr,opt_len));
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||||||
|
end
|
||||||
|
|
||||||
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N_opt = obj.sps * floor(N_opt / obj.sps);
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N_opt = max(obj.sps,N_opt);
|
||||||
|
|
||||||
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n_symbols = N_opt / obj.sps;
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x_opt = x(1:N_opt);
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d_opt = d(1:n_symbols);
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|
end
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||||||
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|
||||||
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function [ber,errors] = berObjective(~,signal,d)
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M = numel(unique(d));
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mapper = PAMmapper(M,0);
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||||||
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eq_signal_sd = Signal(signal);
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eq_signal_hd = mapper.quantize(eq_signal_sd);
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tx_symbols = Signal(d);
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rx_bits = mapper.demap(eq_signal_hd);
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tx_bits = mapper.demap(tx_symbols);
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|
[~,errors,ber,~] = calc_ber(rx_bits.signal,tx_bits.signal, ...
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||||||
|
"skip_front",1000, ...
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"skip_end",0, ...
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||||||
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"returnErrorLocation",1);
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||||||
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end
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||||||
|
|
||||||
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function plotMuOptimization(obj)
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||||||
|
if isempty(obj.mu_optimization)
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||||||
|
return
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||||||
|
end
|
||||||
|
|
||||||
|
X = obj.mu_optimization.XTrace;
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objective = obj.mu_optimization.ObjectiveTrace;
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objective = objective(:);
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||||||
|
valid = isfinite(objective);
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||||||
|
|
||||||
|
if isempty(X) || ~any(valid)
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||||||
|
return
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||||||
|
end
|
||||||
|
|
||||||
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var_names = X.Properties.VariableNames;
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||||||
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n_vars = numel(var_names);
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eval_idx = (1:numel(objective)).';
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objective_plot = obj.positiveObjectiveForLogPlot(objective);
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||||||
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best_plot = obj.positiveObjectiveForLogPlot(cummin(objective));
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||||||
|
|
||||||
|
figure(obj.mu_optimization_fignum);
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||||||
|
clf;
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||||||
|
t = tiledlayout(2,2,"TileSpacing","compact","Padding","compact");
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||||||
|
title(t,"FFE_plain Bayesian mu optimization");
|
||||||
|
|
||||||
|
nexttile;
|
||||||
|
h_candidate = semilogy(eval_idx,objective_plot,"o-","DisplayName","candidate");
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||||||
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obj.addOptimizationDataTips(h_candidate,X,objective,objective_plot,eval_idx,var_names);
|
||||||
|
hold on;
|
||||||
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h_best_trace = semilogy(eval_idx,best_plot,"k-","LineWidth",1.2,"DisplayName","best so far");
|
||||||
|
h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
|
||||||
|
h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Best BER",best_plot);
|
||||||
|
grid on;
|
||||||
|
xlabel("Evaluation");
|
||||||
|
ylabel("BER");
|
||||||
|
legend("Location","best");
|
||||||
|
|
||||||
|
if n_vars < 2
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
pairs = nchoosek(1:n_vars,2);
|
||||||
|
n_pair_plots = min(size(pairs,1),3);
|
||||||
|
[~,best_idx] = min(objective);
|
||||||
|
|
||||||
|
for pair_idx = 1:n_pair_plots
|
||||||
|
nexttile;
|
||||||
|
x_name = var_names{pairs(pair_idx,1)};
|
||||||
|
y_name = var_names{pairs(pair_idx,2)};
|
||||||
|
x_data = X.(x_name);
|
||||||
|
y_data = X.(y_name);
|
||||||
|
c_data = log10(objective_plot);
|
||||||
|
|
||||||
|
h_scatter = scatter(log10(x_data),log10(y_data),35,c_data,"filled");
|
||||||
|
obj.addOptimizationDataTips(h_scatter,X,objective,objective_plot,eval_idx,var_names);
|
||||||
|
hold on;
|
||||||
|
h_best = plot(log10(x_data(best_idx)),log10(y_data(best_idx)),"kp", ...
|
||||||
|
"MarkerSize",12, ...
|
||||||
|
"MarkerFaceColor","y", ...
|
||||||
|
"DisplayName","best");
|
||||||
|
obj.addOptimizationDataTips(h_best,X(best_idx,:),objective(best_idx),objective_plot(best_idx),eval_idx(best_idx),var_names);
|
||||||
|
grid on;
|
||||||
|
xlabel("log10(" + string(x_name) + ")");
|
||||||
|
ylabel("log10(" + string(y_name) + ")");
|
||||||
|
cb = colorbar;
|
||||||
|
cb.Label.String = "log10(BER)";
|
||||||
|
title(string(x_name) + " vs " + string(y_name));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function addOptimizationDataTips(~,plot_handle,X,objective,objective_plot,eval_idx,var_names)
|
||||||
|
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
|
||||||
|
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER",objective);
|
||||||
|
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER shown",objective_plot);
|
||||||
|
|
||||||
|
for var_idx = 1:numel(var_names)
|
||||||
|
var_name = var_names{var_idx};
|
||||||
|
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow(var_name,X.(var_name));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function objective_plot = positiveObjectiveForLogPlot(~,objective)
|
||||||
|
objective_plot = objective;
|
||||||
|
positive_values = objective(isfinite(objective) & objective > 0);
|
||||||
|
|
||||||
|
if isempty(positive_values)
|
||||||
|
floor_value = 1e-12;
|
||||||
|
else
|
||||||
|
floor_value = min(positive_values) / 10;
|
||||||
|
end
|
||||||
|
|
||||||
|
objective_plot(~isfinite(objective_plot) | objective_plot <= 0) = floor_value;
|
||||||
|
end
|
||||||
|
|
||||||
|
function state = captureObjectiveState(obj)
|
||||||
|
state.e = obj.e;
|
||||||
|
state.e_tr = obj.e_tr;
|
||||||
|
state.error = obj.error;
|
||||||
|
state.P = obj.P;
|
||||||
|
state.save_debug = obj.save_debug;
|
||||||
|
state.debug_struct = obj.debug_struct;
|
||||||
|
end
|
||||||
|
|
||||||
|
function restoreObjectiveState(obj,state)
|
||||||
|
obj.e = state.e;
|
||||||
|
obj.e_tr = state.e_tr;
|
||||||
|
obj.error = state.error;
|
||||||
|
obj.P = state.P;
|
||||||
|
obj.save_debug = state.save_debug;
|
||||||
|
obj.debug_struct = state.debug_struct;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
methods (Access = private)
|
||||||
|
function resetState(obj)
|
||||||
|
obj.e = zeros(obj.order,1);
|
||||||
|
obj.e_tr = zeros(obj.order,1);
|
||||||
|
obj.error = 0;
|
||||||
|
obj.P = (1/0.05) * eye(obj.order);
|
||||||
|
obj.debug_struct = struct();
|
||||||
|
end
|
||||||
|
|
||||||
|
function N = validSampleLength(obj,N,x,d)
|
||||||
|
N_available = min(numel(x),numel(d) * obj.sps);
|
||||||
|
N = min(N,N_available);
|
||||||
|
N = obj.sps * floor(N / obj.sps);
|
||||||
|
N = max(0,N);
|
||||||
|
end
|
||||||
|
|
||||||
|
function adaption_code = adaptionCode(obj)
|
||||||
|
if obj.adaption_technique == adaption_method.nlms
|
||||||
|
adaption_code = 1;
|
||||||
|
elseif obj.adaption_technique == adaption_method.lms
|
||||||
|
adaption_code = 2;
|
||||||
|
elseif obj.adaption_technique == adaption_method.rls
|
||||||
|
adaption_code = 3;
|
||||||
|
else
|
||||||
|
builtin("error","FFE_plain:InvalidAdaptionTechnique", ...
|
||||||
|
"Unsupported FFE adaption technique.");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function initializeDebug(obj,n_symbols,training)
|
||||||
|
obj.debug_struct.error = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.error_first_epoch = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.main_cursor = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.mu_nlms = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_gradient = NaN(1,n_symbols);
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_tr = NaN(1,n_symbols);
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update = NaN(1,n_symbols);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
311
Classes/04_DSP/Equalizer/MPI_reduction/FFE_A1.m
Normal file
311
Classes/04_DSP/Equalizer/MPI_reduction/FFE_A1.m
Normal file
@@ -0,0 +1,311 @@
|
|||||||
|
classdef FFE_A1 < handle
|
||||||
|
%FFE_A1 Feed-forward equalizer with A1 moving-average input suppression.
|
||||||
|
%
|
||||||
|
% This class is intentionally narrow: it contains the normal adaptive FFE
|
||||||
|
% flow plus the A1 block-rate moving-average input correction. A2,
|
||||||
|
% DC-tracking, and optimizer logic are kept out of this file.
|
||||||
|
|
||||||
|
properties
|
||||||
|
sps
|
||||||
|
order
|
||||||
|
e
|
||||||
|
e_tr
|
||||||
|
error
|
||||||
|
|
||||||
|
len_tr
|
||||||
|
mu_tr
|
||||||
|
epochs_tr
|
||||||
|
|
||||||
|
adaption_technique
|
||||||
|
dd_mode
|
||||||
|
mu_dd
|
||||||
|
epochs_dd
|
||||||
|
dd_len_fraction
|
||||||
|
|
||||||
|
% A1 moving-average input suppression
|
||||||
|
dc_avg_bufferlength_a1
|
||||||
|
dc_avg_update_blocklength_a1
|
||||||
|
dc_smoothing_a1
|
||||||
|
|
||||||
|
P
|
||||||
|
constellation
|
||||||
|
|
||||||
|
decide
|
||||||
|
|
||||||
|
save_debug = 0
|
||||||
|
debug_struct
|
||||||
|
end
|
||||||
|
|
||||||
|
methods
|
||||||
|
function obj = FFE_A1(options)
|
||||||
|
arguments(Input)
|
||||||
|
options.sps = 2
|
||||||
|
options.order = 15
|
||||||
|
|
||||||
|
options.len_tr = 4096
|
||||||
|
options.mu_tr = 0
|
||||||
|
options.epochs_tr = 5
|
||||||
|
|
||||||
|
options.adaption_technique adaption_method = adaption_method.lms
|
||||||
|
options.dd_mode = 1
|
||||||
|
options.mu_dd = 1e-5
|
||||||
|
options.epochs_dd = 5
|
||||||
|
options.dd_len_fraction = 1
|
||||||
|
|
||||||
|
options.dc_avg_bufferlength_a1 = 0
|
||||||
|
options.dc_avg_update_blocklength_a1 = 0
|
||||||
|
options.dc_smoothing_a1 = 0
|
||||||
|
|
||||||
|
options.decide = false
|
||||||
|
options.save_debug = 0
|
||||||
|
end
|
||||||
|
|
||||||
|
fn = fieldnames(options);
|
||||||
|
for n = 1:numel(fn)
|
||||||
|
obj.(fn{n}) = options.(fn{n});
|
||||||
|
end
|
||||||
|
|
||||||
|
assert(obj.dc_avg_bufferlength_a1 >= 0);
|
||||||
|
assert(obj.dc_avg_update_blocklength_a1 >= 0);
|
||||||
|
|
||||||
|
obj.e = zeros(obj.order,1);
|
||||||
|
obj.error = 0;
|
||||||
|
obj.dc_avg_bufferlength_a1 = floor(obj.dc_avg_bufferlength_a1);
|
||||||
|
obj.dc_avg_update_blocklength_a1 = floor(obj.dc_avg_update_blocklength_a1);
|
||||||
|
obj.dc_smoothing_a1 = min(max(obj.dc_smoothing_a1,0),1);
|
||||||
|
end
|
||||||
|
|
||||||
|
function [X,Noi] = process(obj,X,D)
|
||||||
|
X = X.normalize("mode","rms");
|
||||||
|
|
||||||
|
obj.constellation = unique(D.signal);
|
||||||
|
|
||||||
|
delta = 0.05;
|
||||||
|
obj.P = (1/delta) * eye(obj.order);
|
||||||
|
|
||||||
|
training = true;
|
||||||
|
showviz = false;
|
||||||
|
obj.equalize(X.signal,D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
|
||||||
|
obj.e_tr = obj.e;
|
||||||
|
|
||||||
|
n = X.length;
|
||||||
|
training = false;
|
||||||
|
if obj.dd_mode
|
||||||
|
[signal,decision] = obj.equalize(X.signal,D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
|
||||||
|
else
|
||||||
|
[signal,decision] = obj.equalize(X.signal,D.signal,0,1,n,training,showviz);
|
||||||
|
end
|
||||||
|
|
||||||
|
if obj.decide
|
||||||
|
X.signal = decision;
|
||||||
|
else
|
||||||
|
X.signal = signal;
|
||||||
|
end
|
||||||
|
|
||||||
|
X.fs = D.fs;
|
||||||
|
X = X.logbookentry([num2str(obj.order),' tap FFE A1']);
|
||||||
|
|
||||||
|
Noi = X - D;
|
||||||
|
end
|
||||||
|
|
||||||
|
function [y,d_hat] = equalize(obj,x,d,mu,epochs,N,training,showviz)
|
||||||
|
arguments
|
||||||
|
obj
|
||||||
|
x
|
||||||
|
d
|
||||||
|
mu
|
||||||
|
epochs
|
||||||
|
N
|
||||||
|
training
|
||||||
|
showviz %#ok<INUSA>
|
||||||
|
end
|
||||||
|
|
||||||
|
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
|
||||||
|
lambda = mu;
|
||||||
|
n_symbols = ceil(N / obj.sps);
|
||||||
|
y = zeros(n_symbols,1);
|
||||||
|
d_hat = zeros(n_symbols,1);
|
||||||
|
err = zeros(n_symbols,1);
|
||||||
|
constellation = obj.constellation;
|
||||||
|
|
||||||
|
adaption_code = obj.adaptionCode();
|
||||||
|
adaption_is_rls = adaption_code == 3;
|
||||||
|
|
||||||
|
mask = ones(obj.order,1);
|
||||||
|
maincursor_pos = ceil(length(obj.e)/2);
|
||||||
|
grad = 0;
|
||||||
|
weight = 0;
|
||||||
|
update = 0;
|
||||||
|
|
||||||
|
if mu == 0 || (~obj.dd_mode && ~training)
|
||||||
|
epochs = 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
dc_avg_enabled = obj.dc_avg_bufferlength_a1 > 1;
|
||||||
|
if dc_avg_enabled
|
||||||
|
dc_avg_buffer = NaN(obj.dc_avg_bufferlength_a1,1);
|
||||||
|
dc_avg_buffer_pos = 0;
|
||||||
|
dc_avg_sum = 0;
|
||||||
|
dc_avg_valid_count = 0;
|
||||||
|
dc_avg_est = 0;
|
||||||
|
dc_avg_update_blocklength = obj.dc_avg_update_blocklength_a1;
|
||||||
|
if dc_avg_update_blocklength <= 0
|
||||||
|
dc_avg_update_blocklength = obj.dc_avg_bufferlength_a1;
|
||||||
|
end
|
||||||
|
dc_avg_update_blocklength = max(1,floor(dc_avg_update_blocklength));
|
||||||
|
dc_avg_input_offset = floor(obj.order/2);
|
||||||
|
% Hardware-like A1 is causal; dc_smoothing_a1 is reserved for offline variants.
|
||||||
|
end
|
||||||
|
|
||||||
|
debug_enabled = obj.save_debug;
|
||||||
|
if debug_enabled
|
||||||
|
obj.debug_struct.error = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.error_first_epoch = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.main_cursor = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.mu_nlms = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_gradient = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_avg_offset = NaN(1,n_symbols);
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_tr = NaN(1,n_symbols);
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update = NaN(1,n_symbols);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
for epoch = 1:epochs
|
||||||
|
symbol = 0;
|
||||||
|
for sample = 1:obj.sps:N
|
||||||
|
symbol = symbol + 1;
|
||||||
|
dc_avg_offset = 0;
|
||||||
|
|
||||||
|
U = x(obj.order+sample-1:-1:sample);
|
||||||
|
if dc_avg_enabled
|
||||||
|
dc_avg_offset = dc_avg_est;
|
||||||
|
U = U - dc_avg_offset;
|
||||||
|
end
|
||||||
|
|
||||||
|
y(symbol,1) = (obj.e.*mask).' * U;
|
||||||
|
|
||||||
|
if dc_avg_enabled
|
||||||
|
raw_sample_idx = dc_avg_input_offset + sample;
|
||||||
|
[dc_avg_buffer,dc_avg_buffer_pos,dc_avg_sum,dc_avg_valid_count,dc_avg_est] = ...
|
||||||
|
obj.updateA1Buffer(dc_avg_buffer,dc_avg_buffer_pos,dc_avg_sum, ...
|
||||||
|
dc_avg_valid_count,dc_avg_est,x(raw_sample_idx),symbol, ...
|
||||||
|
dc_avg_update_blocklength);
|
||||||
|
end
|
||||||
|
|
||||||
|
if training
|
||||||
|
d_hat(symbol,1) = d(symbol);
|
||||||
|
else
|
||||||
|
[~,symbol_idx] = min(abs(y(symbol) - constellation));
|
||||||
|
d_hat(symbol,1) = constellation(symbol_idx);
|
||||||
|
end
|
||||||
|
|
||||||
|
err(symbol) = d_hat(symbol) - y(symbol);
|
||||||
|
|
||||||
|
if training || obj.dd_mode
|
||||||
|
switch adaption_code
|
||||||
|
case 1
|
||||||
|
normU = (U.'*U) + eps;
|
||||||
|
weight = mu / normU;
|
||||||
|
grad = err(symbol) * U;
|
||||||
|
update = grad * weight;
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
|
||||||
|
case 2
|
||||||
|
weight = mu;
|
||||||
|
grad = err(symbol) * U;
|
||||||
|
update = grad * weight;
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
|
||||||
|
case 3
|
||||||
|
denom = lambda + U.' * obj.P * U;
|
||||||
|
k = (obj.P * U) / denom;
|
||||||
|
update = k * err(symbol);
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if debug_enabled && epoch == 1
|
||||||
|
obj.debug_struct.error_first_epoch(1,symbol) = err(symbol) * err(symbol)';
|
||||||
|
end
|
||||||
|
|
||||||
|
if debug_enabled && epoch == epochs
|
||||||
|
error_power = err(symbol) * err(symbol)';
|
||||||
|
update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
|
||||||
|
obj.debug_struct.error(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
|
||||||
|
obj.debug_struct.mu_nlms(1,symbol) = weight;
|
||||||
|
obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
|
||||||
|
obj.debug_struct.dc_avg_offset(1,symbol) = dc_avg_offset;
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.update_tr(1,symbol) = update_power;
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.update(1,symbol) = update_power;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if ~adaption_is_rls
|
||||||
|
obj.P = [];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
methods (Access = private)
|
||||||
|
function adaption_code = adaptionCode(obj)
|
||||||
|
if obj.adaption_technique == adaption_method.nlms
|
||||||
|
adaption_code = 1;
|
||||||
|
elseif obj.adaption_technique == adaption_method.lms
|
||||||
|
adaption_code = 2;
|
||||||
|
elseif obj.adaption_technique == adaption_method.rls
|
||||||
|
adaption_code = 3;
|
||||||
|
else
|
||||||
|
builtin("error","FFE_A1:InvalidAdaptionTechnique", ...
|
||||||
|
"Unsupported FFE adaption technique.");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
methods (Static, Access = private)
|
||||||
|
function [buffer,buffer_pos,buffer_sum,valid_count,estimate] = ...
|
||||||
|
updateA1Buffer(buffer,buffer_pos,buffer_sum,valid_count,estimate, ...
|
||||||
|
new_value,symbol,update_blocklength)
|
||||||
|
|
||||||
|
buffer_pos = buffer_pos + 1;
|
||||||
|
if buffer_pos > numel(buffer)
|
||||||
|
buffer_pos = 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
old_value = buffer(buffer_pos);
|
||||||
|
if isfinite(old_value)
|
||||||
|
buffer_sum = buffer_sum - old_value;
|
||||||
|
valid_count = valid_count - 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
if isfinite(new_value)
|
||||||
|
buffer(buffer_pos) = new_value;
|
||||||
|
buffer_sum = buffer_sum + new_value;
|
||||||
|
valid_count = valid_count + 1;
|
||||||
|
else
|
||||||
|
buffer(buffer_pos) = NaN;
|
||||||
|
end
|
||||||
|
|
||||||
|
if mod(symbol,update_blocklength) == 0
|
||||||
|
if valid_count == 0
|
||||||
|
estimate = 0;
|
||||||
|
else
|
||||||
|
estimate = buffer_sum / valid_count;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
347
Classes/04_DSP/Equalizer/MPI_reduction/FFE_A2Residual.m
Normal file
347
Classes/04_DSP/Equalizer/MPI_reduction/FFE_A2Residual.m
Normal file
@@ -0,0 +1,347 @@
|
|||||||
|
classdef FFE_A2Residual < FFE_plain
|
||||||
|
%FFE_A2Residual Plain FFE with A2 residual MPI suppression.
|
||||||
|
%
|
||||||
|
% This class reuses FFE_plain for process flow and BER-based mu
|
||||||
|
% optimization. Only equalize is specialized to add the paper-style A2
|
||||||
|
% residual correction: average y_raw - d_hat per PAM level, subtract the
|
||||||
|
% level-weighted residual estimate, then redo the DD decision.
|
||||||
|
|
||||||
|
properties
|
||||||
|
dc_level_avg_bufferlength_a2
|
||||||
|
dc_level_update_blocklength_a2
|
||||||
|
dc_smoothing_a2
|
||||||
|
dc_level_weights_a2
|
||||||
|
end
|
||||||
|
|
||||||
|
methods
|
||||||
|
function obj = FFE_A2Residual(options)
|
||||||
|
arguments(Input)
|
||||||
|
options.sps = 2
|
||||||
|
options.order = 15
|
||||||
|
|
||||||
|
options.len_tr = 4096
|
||||||
|
options.mu_tr = 0
|
||||||
|
options.epochs_tr = 5
|
||||||
|
|
||||||
|
options.adaption_technique adaption_method = adaption_method.lms
|
||||||
|
options.dd_mode = 1
|
||||||
|
options.mu_dd = 1e-5
|
||||||
|
options.epochs_dd = 5
|
||||||
|
options.dd_len_fraction = 1
|
||||||
|
|
||||||
|
options.dc_level_avg_bufferlength_a2 = 0
|
||||||
|
options.dc_level_update_blocklength_a2 = 0
|
||||||
|
options.dc_smoothing_a2 = 0
|
||||||
|
options.dc_level_weights_a2 = 0
|
||||||
|
|
||||||
|
options.decide = false
|
||||||
|
|
||||||
|
options.save_debug = 0
|
||||||
|
options.optmize_mus = 0
|
||||||
|
options.mu_optimization_len = 2^15
|
||||||
|
options.plot_mu_optimization = 0
|
||||||
|
options.mu_optimization_fignum = 3010
|
||||||
|
end
|
||||||
|
|
||||||
|
obj@FFE_plain( ...
|
||||||
|
"sps",options.sps, ...
|
||||||
|
"order",options.order, ...
|
||||||
|
"len_tr",options.len_tr, ...
|
||||||
|
"mu_tr",options.mu_tr, ...
|
||||||
|
"epochs_tr",options.epochs_tr, ...
|
||||||
|
"adaption_technique",options.adaption_technique, ...
|
||||||
|
"dd_mode",options.dd_mode, ...
|
||||||
|
"mu_dd",options.mu_dd, ...
|
||||||
|
"epochs_dd",options.epochs_dd, ...
|
||||||
|
"dd_len_fraction",options.dd_len_fraction, ...
|
||||||
|
"decide",options.decide, ...
|
||||||
|
"save_debug",options.save_debug, ...
|
||||||
|
"optmize_mus",options.optmize_mus, ...
|
||||||
|
"mu_optimization_len",options.mu_optimization_len, ...
|
||||||
|
"plot_mu_optimization",options.plot_mu_optimization, ...
|
||||||
|
"mu_optimization_fignum",options.mu_optimization_fignum);
|
||||||
|
|
||||||
|
obj.dc_level_avg_bufferlength_a2 = floor(options.dc_level_avg_bufferlength_a2);
|
||||||
|
obj.dc_level_update_blocklength_a2 = floor(options.dc_level_update_blocklength_a2);
|
||||||
|
obj.dc_smoothing_a2 = min(max(options.dc_smoothing_a2,0),1);
|
||||||
|
obj.dc_level_weights_a2 = options.dc_level_weights_a2;
|
||||||
|
|
||||||
|
assert(obj.dc_level_avg_bufferlength_a2 >= 0);
|
||||||
|
assert(obj.dc_level_update_blocklength_a2 >= 0);
|
||||||
|
end
|
||||||
|
|
||||||
|
function [y,d_hat] = equalize(obj,x,d,mu,epochs,N,training,showviz)
|
||||||
|
arguments
|
||||||
|
obj
|
||||||
|
x
|
||||||
|
d
|
||||||
|
mu
|
||||||
|
epochs
|
||||||
|
N
|
||||||
|
training
|
||||||
|
showviz
|
||||||
|
end
|
||||||
|
|
||||||
|
unused_showviz = showviz; %#ok<NASGU>
|
||||||
|
x = x(:);
|
||||||
|
d = d(:);
|
||||||
|
N = obj.validSampleLength(N,x,d);
|
||||||
|
n_symbols = N / obj.sps;
|
||||||
|
y = zeros(n_symbols,1);
|
||||||
|
d_hat = zeros(n_symbols,1);
|
||||||
|
|
||||||
|
if n_symbols == 0
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if isempty(obj.constellation)
|
||||||
|
obj.constellation = unique(d);
|
||||||
|
end
|
||||||
|
decision_constellation = obj.constellation(:);
|
||||||
|
|
||||||
|
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
|
||||||
|
lambda = mu;
|
||||||
|
mask = ones(obj.order,1);
|
||||||
|
maincursor_pos = ceil(length(obj.e)/2);
|
||||||
|
adaption_code = obj.adaptionCode();
|
||||||
|
|
||||||
|
if mu == 0 || (~obj.dd_mode && ~training)
|
||||||
|
epochs = 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
dc_level_enabled = obj.dc_level_avg_bufferlength_a2 > 1 && any(obj.dc_level_weights_a2(:) ~= 0);
|
||||||
|
if dc_level_enabled
|
||||||
|
[dc_level_weight_by_level,n_levels] = obj.expandedLevelWeights(decision_constellation);
|
||||||
|
dc_level_buffer_len = obj.dc_level_avg_bufferlength_a2;
|
||||||
|
dc_level_err_buffer = NaN(n_levels,dc_level_buffer_len);
|
||||||
|
dc_level_err_buffer_pos_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_err_sum_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_buffer_valid_count_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_mpi_est_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_valid_count_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_update_blocklength = obj.dc_level_update_blocklength_a2;
|
||||||
|
if dc_level_update_blocklength <= 0
|
||||||
|
dc_level_update_blocklength = dc_level_buffer_len;
|
||||||
|
end
|
||||||
|
dc_level_update_blocklength = max(1,floor(dc_level_update_blocklength));
|
||||||
|
dc_level_window_future_fraction = obj.dc_smoothing_a2; %#ok<NASGU>
|
||||||
|
end
|
||||||
|
|
||||||
|
debug_enabled = obj.save_debug;
|
||||||
|
if debug_enabled
|
||||||
|
obj.initializeDebug(n_symbols,training);
|
||||||
|
end
|
||||||
|
|
||||||
|
for epoch = 1:epochs
|
||||||
|
symbol = 0;
|
||||||
|
for sample = 1:obj.sps:N
|
||||||
|
symbol = symbol + 1;
|
||||||
|
grad = zeros(obj.order,1);
|
||||||
|
update = zeros(obj.order,1);
|
||||||
|
weight = 0;
|
||||||
|
dc_level_mpi_est = 0;
|
||||||
|
dc_level_weight = 0;
|
||||||
|
dc_level_valid_count = 0;
|
||||||
|
|
||||||
|
U = x(obj.order+sample-1:-1:sample);
|
||||||
|
y(symbol,1) = (obj.e.*mask).' * U;
|
||||||
|
y_raw = y(symbol);
|
||||||
|
|
||||||
|
if training
|
||||||
|
d_hat(symbol,1) = d(symbol);
|
||||||
|
[~,symbol_idx] = min(abs(d_hat(symbol) - decision_constellation));
|
||||||
|
dc_level_decision_level = decision_constellation(symbol_idx);
|
||||||
|
else
|
||||||
|
[~,symbol_idx] = min(abs(y_raw - decision_constellation));
|
||||||
|
d_hat(symbol,1) = decision_constellation(symbol_idx);
|
||||||
|
dc_level_decision_level = decision_constellation(symbol_idx);
|
||||||
|
end
|
||||||
|
|
||||||
|
dc_level_symbol_idx = symbol_idx;
|
||||||
|
if dc_level_enabled
|
||||||
|
dc_level_mpi_est = dc_level_mpi_est_by_level(dc_level_symbol_idx);
|
||||||
|
dc_level_valid_count = dc_level_valid_count_by_level(dc_level_symbol_idx);
|
||||||
|
dc_level_weight = dc_level_weight_by_level(dc_level_symbol_idx) * ...
|
||||||
|
min(dc_level_valid_count / dc_level_buffer_len,1);
|
||||||
|
|
||||||
|
mpi_err = y_raw - d_hat(symbol);
|
||||||
|
y(symbol,1) = y_raw - dc_level_weight * dc_level_mpi_est;
|
||||||
|
|
||||||
|
if training
|
||||||
|
d_hat(symbol,1) = d(symbol);
|
||||||
|
else
|
||||||
|
[~,symbol_idx] = min(abs(y(symbol) - decision_constellation));
|
||||||
|
d_hat(symbol,1) = decision_constellation(symbol_idx);
|
||||||
|
dc_level_decision_level = decision_constellation(symbol_idx);
|
||||||
|
end
|
||||||
|
|
||||||
|
[dc_level_err_buffer,dc_level_err_buffer_pos_by_level, ...
|
||||||
|
dc_level_err_sum_by_level,dc_level_buffer_valid_count_by_level, ...
|
||||||
|
dc_level_mpi_est_by_level,dc_level_valid_count_by_level] = ...
|
||||||
|
obj.updateResidualBuffer( ...
|
||||||
|
dc_level_err_buffer,dc_level_err_buffer_pos_by_level, ...
|
||||||
|
dc_level_err_sum_by_level,dc_level_buffer_valid_count_by_level, ...
|
||||||
|
dc_level_mpi_est_by_level,dc_level_valid_count_by_level, ...
|
||||||
|
mpi_err,dc_level_symbol_idx,symbol,dc_level_update_blocklength);
|
||||||
|
end
|
||||||
|
|
||||||
|
err = d_hat(symbol) - y(symbol);
|
||||||
|
|
||||||
|
if mu ~= 0 && (training || obj.dd_mode)
|
||||||
|
switch adaption_code
|
||||||
|
case 1
|
||||||
|
weight = mu / ((U.'*U) + eps);
|
||||||
|
grad = err * U;
|
||||||
|
update = grad * weight;
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
|
||||||
|
case 2
|
||||||
|
weight = mu;
|
||||||
|
grad = err * U;
|
||||||
|
update = grad * weight;
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
|
||||||
|
case 3
|
||||||
|
denom = lambda + U.' * obj.P * U;
|
||||||
|
k = (obj.P * U) / denom;
|
||||||
|
update = k * err;
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if debug_enabled && epoch == 1
|
||||||
|
obj.debug_struct.error_first_epoch(1,symbol) = err * err';
|
||||||
|
end
|
||||||
|
|
||||||
|
if debug_enabled && epoch == epochs
|
||||||
|
error_power = err * err';
|
||||||
|
update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
|
||||||
|
obj.debug_struct.error(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
|
||||||
|
obj.debug_struct.mu_nlms(1,symbol) = weight;
|
||||||
|
obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
|
||||||
|
obj.debug_struct.dc_level_mpi_est(1,symbol) = dc_level_mpi_est;
|
||||||
|
obj.debug_struct.dc_level_weight(1,symbol) = dc_level_weight;
|
||||||
|
obj.debug_struct.dc_level_valid_count(1,symbol) = dc_level_valid_count;
|
||||||
|
obj.debug_struct.dc_level_symbol_idx(1,symbol) = dc_level_symbol_idx;
|
||||||
|
obj.debug_struct.dc_level_decision_level(1,symbol) = dc_level_decision_level;
|
||||||
|
obj.debug_struct.dc_level_y_raw(1,symbol) = y_raw;
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.update_tr(1,symbol) = update_power;
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.update(1,symbol) = update_power;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
methods (Access = private)
|
||||||
|
function N = validSampleLength(obj,N,x,d)
|
||||||
|
N_available = min(numel(x),numel(d) * obj.sps);
|
||||||
|
N = min(N,N_available);
|
||||||
|
N = obj.sps * floor(N / obj.sps);
|
||||||
|
N = max(0,N);
|
||||||
|
end
|
||||||
|
|
||||||
|
function adaption_code = adaptionCode(obj)
|
||||||
|
if obj.adaption_technique == adaption_method.nlms
|
||||||
|
adaption_code = 1;
|
||||||
|
elseif obj.adaption_technique == adaption_method.lms
|
||||||
|
adaption_code = 2;
|
||||||
|
elseif obj.adaption_technique == adaption_method.rls
|
||||||
|
adaption_code = 3;
|
||||||
|
else
|
||||||
|
builtin("error","FFE_A2Residual:InvalidAdaptionTechnique", ...
|
||||||
|
"Unsupported FFE adaption technique.");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function [weights,n_levels] = expandedLevelWeights(obj,decision_constellation)
|
||||||
|
n_levels = numel(decision_constellation);
|
||||||
|
if isscalar(obj.dc_level_weights_a2)
|
||||||
|
weights = repmat(obj.dc_level_weights_a2,n_levels,1);
|
||||||
|
elseif numel(obj.dc_level_weights_a2) == n_levels
|
||||||
|
weights = obj.dc_level_weights_a2(:);
|
||||||
|
else
|
||||||
|
builtin("error","FFE_A2Residual:InvalidDCLevelWeights", ...
|
||||||
|
"dc_level_weights_a2 must be scalar or have one entry per constellation level.");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function initializeDebug(obj,n_symbols,training)
|
||||||
|
obj.debug_struct.error = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.error_first_epoch = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.main_cursor = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.mu_nlms = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_gradient = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_mpi_est = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_weight = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_valid_count = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_symbol_idx = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_decision_level = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_y_raw = NaN(1,n_symbols);
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_tr = NaN(1,n_symbols);
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update = NaN(1,n_symbols);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
methods (Static, Access = private)
|
||||||
|
function [buffer,buffer_pos_by_level,err_sum_by_level,buffer_valid_count_by_level, ...
|
||||||
|
mpi_est_by_level,valid_count_by_level] = updateResidualBuffer( ...
|
||||||
|
buffer,buffer_pos_by_level,err_sum_by_level,buffer_valid_count_by_level, ...
|
||||||
|
mpi_est_by_level,valid_count_by_level,new_value,symbol_idx,symbol, ...
|
||||||
|
update_blocklength)
|
||||||
|
|
||||||
|
buffer_len = size(buffer,2);
|
||||||
|
buffer_pos = buffer_pos_by_level(symbol_idx) + 1;
|
||||||
|
if buffer_pos > buffer_len
|
||||||
|
buffer_pos = 1;
|
||||||
|
end
|
||||||
|
buffer_pos_by_level(symbol_idx) = buffer_pos;
|
||||||
|
|
||||||
|
old_value = buffer(symbol_idx,buffer_pos);
|
||||||
|
if isfinite(old_value)
|
||||||
|
err_sum_by_level(symbol_idx) = err_sum_by_level(symbol_idx) - old_value;
|
||||||
|
buffer_valid_count_by_level(symbol_idx) = buffer_valid_count_by_level(symbol_idx) - 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
if isfinite(new_value)
|
||||||
|
buffer(symbol_idx,buffer_pos) = new_value;
|
||||||
|
err_sum_by_level(symbol_idx) = err_sum_by_level(symbol_idx) + new_value;
|
||||||
|
buffer_valid_count_by_level(symbol_idx) = buffer_valid_count_by_level(symbol_idx) + 1;
|
||||||
|
else
|
||||||
|
buffer(symbol_idx,buffer_pos) = NaN;
|
||||||
|
end
|
||||||
|
|
||||||
|
if mod(symbol,update_blocklength) == 0
|
||||||
|
if update_blocklength == 1
|
||||||
|
valid_count_by_level(symbol_idx) = buffer_valid_count_by_level(symbol_idx);
|
||||||
|
if valid_count_by_level(symbol_idx) == 0
|
||||||
|
mpi_est_by_level(symbol_idx) = 0;
|
||||||
|
else
|
||||||
|
mpi_est_by_level(symbol_idx) = ...
|
||||||
|
err_sum_by_level(symbol_idx) / valid_count_by_level(symbol_idx);
|
||||||
|
end
|
||||||
|
else
|
||||||
|
valid_count_by_level = buffer_valid_count_by_level;
|
||||||
|
has_valid = valid_count_by_level > 0;
|
||||||
|
mpi_est_by_level(:) = 0;
|
||||||
|
mpi_est_by_level(has_valid) = ...
|
||||||
|
err_sum_by_level(has_valid) ./ valid_count_by_level(has_valid);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
361
Classes/04_DSP/Equalizer/MPI_reduction/FFE_A2TrackedLevels.m
Normal file
361
Classes/04_DSP/Equalizer/MPI_reduction/FFE_A2TrackedLevels.m
Normal file
@@ -0,0 +1,361 @@
|
|||||||
|
classdef FFE_A2TrackedLevels < handle
|
||||||
|
%FFE_A2TrackedLevels FFE with A2 tracked-level decision thresholds.
|
||||||
|
%
|
||||||
|
% This class is intentionally narrow: it contains the normal adaptive FFE
|
||||||
|
% flow plus the A2 tracked-level decision logic. The output sample y stays
|
||||||
|
% unchanged; only the decision constellation is moved by the tracked
|
||||||
|
% level-wise offsets.
|
||||||
|
|
||||||
|
properties
|
||||||
|
sps
|
||||||
|
order
|
||||||
|
e
|
||||||
|
e_tr
|
||||||
|
error
|
||||||
|
|
||||||
|
len_tr
|
||||||
|
mu_tr
|
||||||
|
epochs_tr
|
||||||
|
|
||||||
|
adaption_technique
|
||||||
|
dd_mode
|
||||||
|
mu_dd
|
||||||
|
epochs_dd
|
||||||
|
dd_len_fraction
|
||||||
|
|
||||||
|
% A2 tracked-level decision
|
||||||
|
dc_level_avg_bufferlength_a2
|
||||||
|
dc_level_update_blocklength_a2
|
||||||
|
dc_smoothing_a2
|
||||||
|
dc_level_weights_a2
|
||||||
|
|
||||||
|
P
|
||||||
|
constellation
|
||||||
|
|
||||||
|
decide
|
||||||
|
|
||||||
|
save_debug = 0
|
||||||
|
debug_struct
|
||||||
|
end
|
||||||
|
|
||||||
|
methods
|
||||||
|
function obj = FFE_A2TrackedLevels(options)
|
||||||
|
arguments(Input)
|
||||||
|
options.sps = 2
|
||||||
|
options.order = 15
|
||||||
|
|
||||||
|
options.len_tr = 4096
|
||||||
|
options.mu_tr = 0
|
||||||
|
options.epochs_tr = 5
|
||||||
|
|
||||||
|
options.adaption_technique adaption_method = adaption_method.lms
|
||||||
|
options.dd_mode = 1
|
||||||
|
options.mu_dd = 1e-5
|
||||||
|
options.epochs_dd = 5
|
||||||
|
options.dd_len_fraction = 1
|
||||||
|
|
||||||
|
options.dc_level_avg_bufferlength_a2 = 0
|
||||||
|
options.dc_level_update_blocklength_a2 = 0
|
||||||
|
options.dc_smoothing_a2 = 0
|
||||||
|
options.dc_level_weights_a2 = 0
|
||||||
|
|
||||||
|
options.decide = false
|
||||||
|
options.save_debug = 0
|
||||||
|
end
|
||||||
|
|
||||||
|
fn = fieldnames(options);
|
||||||
|
for n = 1:numel(fn)
|
||||||
|
obj.(fn{n}) = options.(fn{n});
|
||||||
|
end
|
||||||
|
|
||||||
|
assert(obj.dc_level_avg_bufferlength_a2 >= 0);
|
||||||
|
assert(obj.dc_level_update_blocklength_a2 >= 0);
|
||||||
|
|
||||||
|
obj.e = zeros(obj.order,1);
|
||||||
|
obj.error = 0;
|
||||||
|
obj.dc_level_avg_bufferlength_a2 = floor(obj.dc_level_avg_bufferlength_a2);
|
||||||
|
obj.dc_level_update_blocklength_a2 = floor(obj.dc_level_update_blocklength_a2);
|
||||||
|
obj.dc_smoothing_a2 = min(max(obj.dc_smoothing_a2,0),1);
|
||||||
|
end
|
||||||
|
|
||||||
|
function [X,Noi] = process(obj,X,D)
|
||||||
|
X = X.normalize("mode","rms");
|
||||||
|
|
||||||
|
obj.constellation = unique(D.signal);
|
||||||
|
|
||||||
|
delta = 0.05;
|
||||||
|
obj.P = (1/delta) * eye(obj.order);
|
||||||
|
|
||||||
|
training = true;
|
||||||
|
showviz = false;
|
||||||
|
obj.equalize(X.signal,D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
|
||||||
|
obj.e_tr = obj.e;
|
||||||
|
|
||||||
|
n = X.length;
|
||||||
|
training = false;
|
||||||
|
if obj.dd_mode
|
||||||
|
[signal,decision] = obj.equalize(X.signal,D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
|
||||||
|
else
|
||||||
|
[signal,decision] = obj.equalize(X.signal,D.signal,0,1,n,training,showviz);
|
||||||
|
end
|
||||||
|
|
||||||
|
if obj.decide
|
||||||
|
X.signal = decision;
|
||||||
|
else
|
||||||
|
X.signal = signal;
|
||||||
|
end
|
||||||
|
|
||||||
|
X.fs = D.fs;
|
||||||
|
X = X.logbookentry([num2str(obj.order),' tap FFE A2 tracked levels']);
|
||||||
|
|
||||||
|
Noi = X - D;
|
||||||
|
end
|
||||||
|
|
||||||
|
function [y,d_hat] = equalize(obj,x,d,mu,epochs,N,training,showviz)
|
||||||
|
arguments
|
||||||
|
obj
|
||||||
|
x
|
||||||
|
d
|
||||||
|
mu
|
||||||
|
epochs
|
||||||
|
N
|
||||||
|
training
|
||||||
|
showviz %#ok<INUSA>
|
||||||
|
end
|
||||||
|
|
||||||
|
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
|
||||||
|
lambda = mu;
|
||||||
|
n_symbols = ceil(N / obj.sps);
|
||||||
|
y = zeros(n_symbols,1);
|
||||||
|
d_hat = zeros(n_symbols,1);
|
||||||
|
err = zeros(n_symbols,1);
|
||||||
|
constellation = obj.constellation;
|
||||||
|
|
||||||
|
adaption_code = obj.adaptionCode();
|
||||||
|
adaption_is_rls = adaption_code == 3;
|
||||||
|
|
||||||
|
mask = ones(obj.order,1);
|
||||||
|
maincursor_pos = ceil(length(obj.e)/2);
|
||||||
|
grad = 0;
|
||||||
|
weight = 0;
|
||||||
|
update = 0;
|
||||||
|
|
||||||
|
if mu == 0 || (~obj.dd_mode && ~training)
|
||||||
|
epochs = 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
dc_level_enabled = obj.dc_level_avg_bufferlength_a2 > 1 && any(obj.dc_level_weights_a2(:) ~= 0);
|
||||||
|
if dc_level_enabled
|
||||||
|
if isempty(constellation)
|
||||||
|
builtin("error","FFE_A2TrackedLevels:MissingConstellation", ...
|
||||||
|
"A2 tracked-level decision requires obj.constellation to be set.");
|
||||||
|
end
|
||||||
|
|
||||||
|
n_levels = numel(constellation);
|
||||||
|
if isscalar(obj.dc_level_weights_a2)
|
||||||
|
dc_level_weight_by_level = repmat(obj.dc_level_weights_a2,n_levels,1);
|
||||||
|
elseif numel(obj.dc_level_weights_a2) == n_levels
|
||||||
|
dc_level_weight_by_level = obj.dc_level_weights_a2(:);
|
||||||
|
else
|
||||||
|
builtin("error","FFE_A2TrackedLevels:InvalidDCLevelWeights", ...
|
||||||
|
"dc_level_weights_a2 must be scalar or have one entry per constellation level.");
|
||||||
|
end
|
||||||
|
|
||||||
|
dc_level_buffer_len = obj.dc_level_avg_bufferlength_a2;
|
||||||
|
dc_level_buffer = NaN(n_levels,dc_level_buffer_len);
|
||||||
|
dc_level_buffer_pos_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_sum_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_buffer_valid_count_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_offset_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_valid_count_by_level = zeros(n_levels,1);
|
||||||
|
dc_level_update_blocklength = obj.dc_level_update_blocklength_a2;
|
||||||
|
if dc_level_update_blocklength <= 0
|
||||||
|
dc_level_update_blocklength = dc_level_buffer_len;
|
||||||
|
end
|
||||||
|
dc_level_update_blocklength = max(1,floor(dc_level_update_blocklength));
|
||||||
|
dc_level_window_future_fraction = obj.dc_smoothing_a2; %#ok<NASGU>
|
||||||
|
end
|
||||||
|
|
||||||
|
debug_enabled = obj.save_debug;
|
||||||
|
if debug_enabled
|
||||||
|
obj.debug_struct.error = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.error_first_epoch = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.main_cursor = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.mu_nlms = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_gradient = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_mpi_est = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_weight = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_valid_count = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_symbol_idx = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_decision_level = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_level_y_raw = NaN(1,n_symbols);
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_tr = NaN(1,n_symbols);
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update = NaN(1,n_symbols);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
for epoch = 1:epochs
|
||||||
|
symbol = 0;
|
||||||
|
for sample = 1:obj.sps:N
|
||||||
|
symbol = symbol + 1;
|
||||||
|
dc_level_offset = 0;
|
||||||
|
dc_level_weight = 0;
|
||||||
|
dc_level_valid_count = 0;
|
||||||
|
|
||||||
|
U = x(obj.order+sample-1:-1:sample);
|
||||||
|
y(symbol,1) = (obj.e.*mask).' * U;
|
||||||
|
y_raw = y(symbol);
|
||||||
|
|
||||||
|
if training
|
||||||
|
d_hat(symbol,1) = d(symbol);
|
||||||
|
[~,symbol_idx] = min(abs(d_hat(symbol) - constellation));
|
||||||
|
dc_level_decision_level = constellation(symbol_idx);
|
||||||
|
else
|
||||||
|
if dc_level_enabled
|
||||||
|
dc_level_ramp_by_level = min(dc_level_valid_count_by_level / dc_level_buffer_len,1);
|
||||||
|
dc_level_decision_constellation = constellation + ...
|
||||||
|
dc_level_weight_by_level .* dc_level_ramp_by_level .* dc_level_offset_by_level;
|
||||||
|
[~,symbol_idx] = min(abs(y_raw - dc_level_decision_constellation));
|
||||||
|
dc_level_decision_level = dc_level_decision_constellation(symbol_idx);
|
||||||
|
else
|
||||||
|
[~,symbol_idx] = min(abs(y_raw - constellation));
|
||||||
|
dc_level_decision_level = constellation(symbol_idx);
|
||||||
|
end
|
||||||
|
d_hat(symbol,1) = constellation(symbol_idx);
|
||||||
|
end
|
||||||
|
|
||||||
|
dc_level_symbol_idx = symbol_idx;
|
||||||
|
if dc_level_enabled
|
||||||
|
dc_level_offset = dc_level_offset_by_level(symbol_idx);
|
||||||
|
dc_level_valid_count = dc_level_valid_count_by_level(symbol_idx);
|
||||||
|
dc_level_weight = dc_level_weight_by_level(symbol_idx) * ...
|
||||||
|
min(dc_level_valid_count / dc_level_buffer_len,1);
|
||||||
|
|
||||||
|
[dc_level_buffer,dc_level_buffer_pos_by_level,dc_level_sum_by_level, ...
|
||||||
|
dc_level_buffer_valid_count_by_level,dc_level_offset_by_level, ...
|
||||||
|
dc_level_valid_count_by_level] = obj.updateTrackedLevelBuffer( ...
|
||||||
|
dc_level_buffer,dc_level_buffer_pos_by_level,dc_level_sum_by_level, ...
|
||||||
|
dc_level_buffer_valid_count_by_level,dc_level_offset_by_level, ...
|
||||||
|
dc_level_valid_count_by_level,y_raw,symbol_idx,symbol, ...
|
||||||
|
dc_level_update_blocklength,constellation);
|
||||||
|
end
|
||||||
|
|
||||||
|
err(symbol) = d_hat(symbol) - y(symbol);
|
||||||
|
|
||||||
|
if training || obj.dd_mode
|
||||||
|
switch adaption_code
|
||||||
|
case 1
|
||||||
|
normU = (U.'*U) + eps;
|
||||||
|
weight = mu / normU;
|
||||||
|
grad = err(symbol) * U;
|
||||||
|
update = grad * weight;
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
|
||||||
|
case 2
|
||||||
|
weight = mu;
|
||||||
|
grad = err(symbol) * U;
|
||||||
|
update = grad * weight;
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
|
||||||
|
case 3
|
||||||
|
denom = lambda + U.' * obj.P * U;
|
||||||
|
k = (obj.P * U) / denom;
|
||||||
|
update = k * err(symbol);
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if debug_enabled && epoch == 1
|
||||||
|
obj.debug_struct.error_first_epoch(1,symbol) = err(symbol) * err(symbol)';
|
||||||
|
end
|
||||||
|
|
||||||
|
if debug_enabled && epoch == epochs
|
||||||
|
error_power = err(symbol) * err(symbol)';
|
||||||
|
update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
|
||||||
|
obj.debug_struct.error(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
|
||||||
|
obj.debug_struct.mu_nlms(1,symbol) = weight;
|
||||||
|
obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
|
||||||
|
obj.debug_struct.dc_level_mpi_est(1,symbol) = dc_level_offset;
|
||||||
|
obj.debug_struct.dc_level_weight(1,symbol) = dc_level_weight;
|
||||||
|
obj.debug_struct.dc_level_valid_count(1,symbol) = dc_level_valid_count;
|
||||||
|
obj.debug_struct.dc_level_symbol_idx(1,symbol) = dc_level_symbol_idx;
|
||||||
|
obj.debug_struct.dc_level_decision_level(1,symbol) = dc_level_decision_level;
|
||||||
|
obj.debug_struct.dc_level_y_raw(1,symbol) = y_raw;
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.update_tr(1,symbol) = update_power;
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.update(1,symbol) = update_power;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if ~adaption_is_rls
|
||||||
|
obj.P = [];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
methods (Access = private)
|
||||||
|
function adaption_code = adaptionCode(obj)
|
||||||
|
if obj.adaption_technique == adaption_method.nlms
|
||||||
|
adaption_code = 1;
|
||||||
|
elseif obj.adaption_technique == adaption_method.lms
|
||||||
|
adaption_code = 2;
|
||||||
|
elseif obj.adaption_technique == adaption_method.rls
|
||||||
|
adaption_code = 3;
|
||||||
|
else
|
||||||
|
builtin("error","FFE_A2TrackedLevels:InvalidAdaptionTechnique", ...
|
||||||
|
"Unsupported FFE adaption technique.");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
methods (Static, Access = private)
|
||||||
|
function [buffer,buffer_pos_by_level,level_sum_by_level,buffer_valid_count_by_level, ...
|
||||||
|
offset_by_level,valid_count_by_level] = updateTrackedLevelBuffer( ...
|
||||||
|
buffer,buffer_pos_by_level,level_sum_by_level,buffer_valid_count_by_level, ...
|
||||||
|
offset_by_level,valid_count_by_level,new_value,symbol_idx,symbol, ...
|
||||||
|
update_blocklength,constellation)
|
||||||
|
|
||||||
|
buffer_pos = buffer_pos_by_level(symbol_idx) + 1;
|
||||||
|
if buffer_pos > size(buffer,2)
|
||||||
|
buffer_pos = 1;
|
||||||
|
end
|
||||||
|
buffer_pos_by_level(symbol_idx) = buffer_pos;
|
||||||
|
|
||||||
|
old_value = buffer(symbol_idx,buffer_pos);
|
||||||
|
if isfinite(old_value)
|
||||||
|
level_sum_by_level(symbol_idx) = level_sum_by_level(symbol_idx) - old_value;
|
||||||
|
buffer_valid_count_by_level(symbol_idx) = buffer_valid_count_by_level(symbol_idx) - 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
if isfinite(new_value)
|
||||||
|
buffer(symbol_idx,buffer_pos) = new_value;
|
||||||
|
level_sum_by_level(symbol_idx) = level_sum_by_level(symbol_idx) + new_value;
|
||||||
|
buffer_valid_count_by_level(symbol_idx) = buffer_valid_count_by_level(symbol_idx) + 1;
|
||||||
|
else
|
||||||
|
buffer(symbol_idx,buffer_pos) = NaN;
|
||||||
|
end
|
||||||
|
|
||||||
|
if mod(symbol,update_blocklength) == 0
|
||||||
|
valid_count_by_level = buffer_valid_count_by_level;
|
||||||
|
has_valid = valid_count_by_level > 0;
|
||||||
|
offset_by_level(:) = 0;
|
||||||
|
offset_by_level(has_valid) = ...
|
||||||
|
level_sum_by_level(has_valid) ./ valid_count_by_level(has_valid) - ...
|
||||||
|
constellation(has_valid);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
752
Classes/04_DSP/Equalizer/MPI_reduction/FFE_DCTracking.m
Normal file
752
Classes/04_DSP/Equalizer/MPI_reduction/FFE_DCTracking.m
Normal file
@@ -0,0 +1,752 @@
|
|||||||
|
classdef FFE_DCTracking < FFE_plain
|
||||||
|
%FFE_DCTracking Plain FFE with the DC-tracking offset loop.
|
||||||
|
%
|
||||||
|
% This class reuses the plain FFE process idea and BER mu optimization,
|
||||||
|
% but keeps only the DC-tracking path from the larger FFE class. A1, A2,
|
||||||
|
% and delayed FFE tap-update buffers are intentionally excluded.
|
||||||
|
|
||||||
|
properties
|
||||||
|
dc_tracking_mu
|
||||||
|
dc_tracking_adaptive_enabled
|
||||||
|
dc_tracking_persistence_gain
|
||||||
|
dc_tracking_power_exponent
|
||||||
|
dc_tracking_buffer_len
|
||||||
|
dc_tracking_mu_eff_max
|
||||||
|
e_dc
|
||||||
|
|
||||||
|
optimize_dc_tracking_params = 0
|
||||||
|
dc_tracking_optimization
|
||||||
|
dc_tracking_optimization_iter = 0
|
||||||
|
dc_tracking_optimization_len
|
||||||
|
dc_tracking_optimization_max_evals
|
||||||
|
dc_tracking_optimization_delay_weight
|
||||||
|
dc_tracking_optimization_smoothing_len
|
||||||
|
end
|
||||||
|
|
||||||
|
properties (Access = private)
|
||||||
|
dc_tracking_mu_min = 1e-6
|
||||||
|
dc_tracking_mu_max = 3e-1
|
||||||
|
dc_tracking_mu_eff_min = 0
|
||||||
|
end
|
||||||
|
|
||||||
|
methods
|
||||||
|
function obj = FFE_DCTracking(options)
|
||||||
|
arguments(Input)
|
||||||
|
options.sps = 2
|
||||||
|
options.order = 15
|
||||||
|
|
||||||
|
options.len_tr = 4096
|
||||||
|
options.mu_tr = 0
|
||||||
|
options.epochs_tr = 5
|
||||||
|
|
||||||
|
options.adaption_technique adaption_method = adaption_method.lms
|
||||||
|
options.dd_mode = 1
|
||||||
|
options.mu_dd = 1e-5
|
||||||
|
options.epochs_dd = 5
|
||||||
|
options.dd_len_fraction = 1
|
||||||
|
|
||||||
|
options.dc_tracking_mu = 0
|
||||||
|
options.dc_tracking_adaptive_enabled = false
|
||||||
|
options.dc_tracking_persistence_gain = 0
|
||||||
|
options.dc_tracking_power_exponent = 2
|
||||||
|
options.dc_tracking_buffer_len = 1
|
||||||
|
options.dc_tracking_mu_eff_max = inf
|
||||||
|
|
||||||
|
options.decide = false
|
||||||
|
|
||||||
|
options.save_debug = 0
|
||||||
|
options.optmize_mus = 0
|
||||||
|
options.mu_optimization_len = 2^15
|
||||||
|
options.plot_mu_optimization = 0
|
||||||
|
options.mu_optimization_fignum = 3010
|
||||||
|
options.optimize_dc_tracking_params = 0
|
||||||
|
options.dc_tracking_optimization_len = 2^15
|
||||||
|
options.dc_tracking_optimization_max_evals = 20
|
||||||
|
options.dc_tracking_optimization_delay_weight = 1e-3
|
||||||
|
options.dc_tracking_optimization_smoothing_len = 501
|
||||||
|
end
|
||||||
|
|
||||||
|
obj@FFE_plain( ...
|
||||||
|
"sps",options.sps, ...
|
||||||
|
"order",options.order, ...
|
||||||
|
"len_tr",options.len_tr, ...
|
||||||
|
"mu_tr",options.mu_tr, ...
|
||||||
|
"epochs_tr",options.epochs_tr, ...
|
||||||
|
"adaption_technique",options.adaption_technique, ...
|
||||||
|
"dd_mode",options.dd_mode, ...
|
||||||
|
"mu_dd",options.mu_dd, ...
|
||||||
|
"epochs_dd",options.epochs_dd, ...
|
||||||
|
"dd_len_fraction",options.dd_len_fraction, ...
|
||||||
|
"decide",options.decide, ...
|
||||||
|
"save_debug",options.save_debug, ...
|
||||||
|
"optmize_mus",options.optmize_mus, ...
|
||||||
|
"mu_optimization_len",options.mu_optimization_len, ...
|
||||||
|
"plot_mu_optimization",options.plot_mu_optimization, ...
|
||||||
|
"mu_optimization_fignum",options.mu_optimization_fignum);
|
||||||
|
|
||||||
|
obj.dc_tracking_mu = options.dc_tracking_mu;
|
||||||
|
obj.dc_tracking_adaptive_enabled = options.dc_tracking_adaptive_enabled;
|
||||||
|
obj.dc_tracking_persistence_gain = options.dc_tracking_persistence_gain;
|
||||||
|
obj.dc_tracking_power_exponent = options.dc_tracking_power_exponent;
|
||||||
|
obj.dc_tracking_buffer_len = floor(options.dc_tracking_buffer_len);
|
||||||
|
obj.dc_tracking_mu_eff_max = options.dc_tracking_mu_eff_max;
|
||||||
|
obj.e_dc = 0;
|
||||||
|
|
||||||
|
obj.optimize_dc_tracking_params = options.optimize_dc_tracking_params;
|
||||||
|
obj.dc_tracking_optimization_len = options.dc_tracking_optimization_len;
|
||||||
|
obj.dc_tracking_optimization_max_evals = max(1, ...
|
||||||
|
floor(options.dc_tracking_optimization_max_evals));
|
||||||
|
obj.dc_tracking_optimization_delay_weight = ...
|
||||||
|
max(0,options.dc_tracking_optimization_delay_weight);
|
||||||
|
obj.dc_tracking_optimization_smoothing_len = max(1, ...
|
||||||
|
floor(options.dc_tracking_optimization_smoothing_len));
|
||||||
|
|
||||||
|
assert(obj.dc_tracking_buffer_len >= 0);
|
||||||
|
end
|
||||||
|
|
||||||
|
function [X,Noi] = process(obj,X,D)
|
||||||
|
X = X.normalize("mode","rms");
|
||||||
|
obj.constellation = unique(D.signal);
|
||||||
|
obj.resetTrackingState();
|
||||||
|
|
||||||
|
if obj.optmize_mus
|
||||||
|
obj.optimizeMus(X.signal,D.signal);
|
||||||
|
obj.resetTrackingState();
|
||||||
|
end
|
||||||
|
|
||||||
|
if obj.optimize_dc_tracking_params
|
||||||
|
obj.optimizeDcTrackingParams(X.signal,D.signal);
|
||||||
|
obj.resetTrackingState();
|
||||||
|
end
|
||||||
|
|
||||||
|
training = true;
|
||||||
|
showviz = false;
|
||||||
|
obj.equalize(X.signal,D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
|
||||||
|
obj.e_tr = obj.e;
|
||||||
|
|
||||||
|
n = X.length;
|
||||||
|
training = false;
|
||||||
|
if obj.dd_mode
|
||||||
|
[signal,decision] = obj.equalize(X.signal,D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
|
||||||
|
else
|
||||||
|
[signal,decision] = obj.equalize(X.signal,D.signal,0,1,n,training,showviz);
|
||||||
|
end
|
||||||
|
|
||||||
|
if obj.decide
|
||||||
|
X.signal = decision;
|
||||||
|
else
|
||||||
|
X.signal = signal;
|
||||||
|
end
|
||||||
|
|
||||||
|
X.fs = D.fs;
|
||||||
|
X = X.logbookentry([num2str(obj.order),' tap FFE DC tracking']);
|
||||||
|
|
||||||
|
Noi = X - D;
|
||||||
|
end
|
||||||
|
|
||||||
|
function optimizeMus(obj,x,d)
|
||||||
|
[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d);
|
||||||
|
|
||||||
|
switch obj.adaption_technique
|
||||||
|
case adaption_method.lms
|
||||||
|
mu_range = [1e-5, 1e-2];
|
||||||
|
case adaption_method.nlms
|
||||||
|
mu_range = [1e-3, 5e-1];
|
||||||
|
case adaption_method.rls
|
||||||
|
mu_range = [0.98, 0.99999];
|
||||||
|
otherwise
|
||||||
|
builtin("error","FFE_DCTracking:InvalidAdaptionTechnique", ...
|
||||||
|
"Unsupported FFE adaption technique.");
|
||||||
|
end
|
||||||
|
|
||||||
|
vars = optimizableVariable("mu_tr",mu_range,"Transform","log");
|
||||||
|
if obj.dd_mode
|
||||||
|
vars = [vars, optimizableVariable("mu_dd",mu_range,"Transform","log")];
|
||||||
|
end
|
||||||
|
optimize_dc_tracking_mu = obj.dc_tracking_mu ~= 0 && ...
|
||||||
|
~obj.optimize_dc_tracking_params;
|
||||||
|
if optimize_dc_tracking_mu
|
||||||
|
vars = [vars, optimizableVariable("dc_tracking_mu",[1e-5,1e-1],"Transform","log")];
|
||||||
|
end
|
||||||
|
|
||||||
|
obj.mu_optimization_iter = 0;
|
||||||
|
fprintf("FFE_DCTracking mu opt uses %d samples / %d symbols\n",N_opt,numel(d_opt));
|
||||||
|
obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x_opt,d_opt),vars, ...
|
||||||
|
"MaxObjectiveEvaluations",20, ...
|
||||||
|
"AcquisitionFunctionName","expected-improvement-plus", ...
|
||||||
|
"IsObjectiveDeterministic",false, ...
|
||||||
|
"Verbose",0, ...
|
||||||
|
"PlotFcn",[]);
|
||||||
|
|
||||||
|
obj.mu_tr = obj.mu_optimization.XAtMinObjective.mu_tr;
|
||||||
|
if obj.dd_mode
|
||||||
|
obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
|
||||||
|
end
|
||||||
|
if optimize_dc_tracking_mu
|
||||||
|
obj.dc_tracking_mu = obj.mu_optimization.XAtMinObjective.dc_tracking_mu;
|
||||||
|
end
|
||||||
|
|
||||||
|
if obj.dd_mode && optimize_dc_tracking_mu
|
||||||
|
fprintf("\nFFE_DCTracking mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e\n", ...
|
||||||
|
obj.mu_tr,obj.mu_dd,obj.dc_tracking_mu,obj.mu_optimization.MinObjective);
|
||||||
|
elseif obj.dd_mode
|
||||||
|
fprintf("\nFFE_DCTracking mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e\n", ...
|
||||||
|
obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective);
|
||||||
|
elseif optimize_dc_tracking_mu
|
||||||
|
fprintf("\nFFE_DCTracking mu opt done: mu_tr=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e\n", ...
|
||||||
|
obj.mu_tr,obj.dc_tracking_mu,obj.mu_optimization.MinObjective);
|
||||||
|
else
|
||||||
|
fprintf("\nFFE_DCTracking mu opt done: mu_tr=%9.3e, BER=%9.3e\n", ...
|
||||||
|
obj.mu_tr,obj.mu_optimization.MinObjective);
|
||||||
|
end
|
||||||
|
|
||||||
|
if obj.plot_mu_optimization
|
||||||
|
obj.plotMuOptimization();
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function optimizeDcTrackingParams(obj,x,d)
|
||||||
|
[x_opt,d_opt,N_opt] = obj.optimizationSignals( ...
|
||||||
|
x,d,obj.dc_tracking_optimization_len);
|
||||||
|
|
||||||
|
vars = optimizableVariable( ...
|
||||||
|
"dc_tracking_mu",[1e-5,1e-1],"Transform","log");
|
||||||
|
initial_values = min(max(obj.dc_tracking_mu,1e-5),1e-1);
|
||||||
|
initial_names = "dc_tracking_mu";
|
||||||
|
if obj.dc_tracking_adaptive_enabled
|
||||||
|
vars = [vars, ...
|
||||||
|
optimizableVariable("dc_tracking_persistence_gain",[0,2]), ...
|
||||||
|
optimizableVariable("dc_tracking_mu_eff_max",[1e-3,3e-1], ...
|
||||||
|
"Transform","log")];
|
||||||
|
initial_values = [initial_values, ...
|
||||||
|
min(max(obj.dc_tracking_persistence_gain,0),2), ...
|
||||||
|
min(max(obj.dc_tracking_mu_eff_max,1e-3),3e-1)];
|
||||||
|
initial_names = [initial_names, ...
|
||||||
|
"dc_tracking_persistence_gain", "dc_tracking_mu_eff_max"];
|
||||||
|
end
|
||||||
|
initial_x = array2table(initial_values, ...
|
||||||
|
"VariableNames",cellstr(initial_names));
|
||||||
|
|
||||||
|
obj.dc_tracking_optimization_iter = 0;
|
||||||
|
fprintf("FFE_DCTracking DC opt uses fixed mu_tr=%9.3e, mu_dd=%9.3e on %d samples / %d symbols\n", ...
|
||||||
|
obj.mu_tr,obj.mu_dd,N_opt,numel(d_opt));
|
||||||
|
|
||||||
|
old_rng = rng;
|
||||||
|
cleanup_rng = onCleanup(@()rng(old_rng));
|
||||||
|
rng(42,"twister");
|
||||||
|
obj.dc_tracking_optimization = bayesopt( ...
|
||||||
|
@(p)obj.dcTrackingObjective(p,x_opt,d_opt),vars, ...
|
||||||
|
"MaxObjectiveEvaluations",obj.dc_tracking_optimization_max_evals, ...
|
||||||
|
"InitialX",initial_x, ...
|
||||||
|
"AcquisitionFunctionName","expected-improvement-plus", ...
|
||||||
|
"IsObjectiveDeterministic",true, ...
|
||||||
|
"Verbose",0, ...
|
||||||
|
"PlotFcn",[]);
|
||||||
|
clear cleanup_rng
|
||||||
|
|
||||||
|
best = obj.dc_tracking_optimization.XAtMinObjective;
|
||||||
|
obj.dc_tracking_mu = best.dc_tracking_mu;
|
||||||
|
if obj.dc_tracking_adaptive_enabled
|
||||||
|
obj.dc_tracking_persistence_gain = ...
|
||||||
|
best.dc_tracking_persistence_gain;
|
||||||
|
obj.dc_tracking_mu_eff_max = best.dc_tracking_mu_eff_max;
|
||||||
|
fprintf("\nFFE_DCTracking DC opt done: dc_tracking_mu=%9.3e, persistence_gain=%6.3f, mu_eff_max=%9.3e, objective=%9.3e\n", ...
|
||||||
|
obj.dc_tracking_mu,obj.dc_tracking_persistence_gain, ...
|
||||||
|
obj.dc_tracking_mu_eff_max, ...
|
||||||
|
obj.dc_tracking_optimization.MinObjective);
|
||||||
|
else
|
||||||
|
fprintf("\nFFE_DCTracking DC opt done: dc_tracking_mu=%9.3e, objective=%9.3e\n", ...
|
||||||
|
obj.dc_tracking_mu,obj.dc_tracking_optimization.MinObjective);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function objective = muObjective(obj,params,x,d)
|
||||||
|
old_state = obj.captureTrackingObjectiveState();
|
||||||
|
cleanup = onCleanup(@()obj.restoreTrackingObjectiveState(old_state));
|
||||||
|
|
||||||
|
obj.save_debug = 0;
|
||||||
|
if any(strcmp(params.Properties.VariableNames,"dc_tracking_mu"))
|
||||||
|
obj.dc_tracking_mu = params.dc_tracking_mu;
|
||||||
|
end
|
||||||
|
obj.resetTrackingState();
|
||||||
|
|
||||||
|
N_tr = min(obj.len_tr,numel(x));
|
||||||
|
obj.equalize(x,d,params.mu_tr,obj.epochs_tr,N_tr,true,false);
|
||||||
|
if obj.dd_mode
|
||||||
|
[signal,~] = obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),false,false);
|
||||||
|
else
|
||||||
|
[signal,~] = obj.equalize(x,d,0,1,numel(x),false,false);
|
||||||
|
end
|
||||||
|
|
||||||
|
[ber,errors] = obj.berObjective(signal,d);
|
||||||
|
objective = ber;
|
||||||
|
if ~isfinite(objective)
|
||||||
|
objective = inf;
|
||||||
|
end
|
||||||
|
|
||||||
|
obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
|
||||||
|
has_dc_tracking_mu = any(strcmp(params.Properties.VariableNames,"dc_tracking_mu"));
|
||||||
|
if obj.dd_mode && has_dc_tracking_mu
|
||||||
|
fprintf("\rFFE_DCTracking mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e, errors=%d", ...
|
||||||
|
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,params.dc_tracking_mu,ber,errors);
|
||||||
|
elseif obj.dd_mode
|
||||||
|
fprintf("\rFFE_DCTracking mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e, errors=%d", ...
|
||||||
|
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,ber,errors);
|
||||||
|
elseif has_dc_tracking_mu
|
||||||
|
fprintf("\rFFE_DCTracking mu opt %02d: mu_tr=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e, errors=%d", ...
|
||||||
|
obj.mu_optimization_iter,params.mu_tr,params.dc_tracking_mu,ber,errors);
|
||||||
|
else
|
||||||
|
fprintf("\rFFE_DCTracking mu opt %02d: mu_tr=%9.3e, BER=%9.3e, errors=%d", ...
|
||||||
|
obj.mu_optimization_iter,params.mu_tr,ber,errors);
|
||||||
|
end
|
||||||
|
|
||||||
|
clear cleanup
|
||||||
|
end
|
||||||
|
|
||||||
|
function objective = dcTrackingObjective(obj,params,x,d)
|
||||||
|
old_state = obj.captureTrackingObjectiveState();
|
||||||
|
cleanup = onCleanup(@()obj.restoreTrackingObjectiveState(old_state));
|
||||||
|
|
||||||
|
obj.applyDcTrackingObjectiveParams(params);
|
||||||
|
obj.save_debug = 1;
|
||||||
|
obj.resetTrackingState();
|
||||||
|
|
||||||
|
N_tr = min(obj.len_tr,numel(x));
|
||||||
|
obj.equalize(x,d,obj.mu_tr,obj.epochs_tr,N_tr,true,false);
|
||||||
|
if obj.dd_mode
|
||||||
|
[signal,~] = obj.equalize( ...
|
||||||
|
x,d,obj.mu_dd,obj.epochs_dd,numel(x),false,false);
|
||||||
|
else
|
||||||
|
[signal,~] = obj.equalize(x,d,0,1,numel(x),false,false);
|
||||||
|
end
|
||||||
|
|
||||||
|
[ber,errors] = obj.berObjective(signal,d);
|
||||||
|
[delay_symbols,delay_corr] = obj.dcTrackingDelayObjective(signal,d);
|
||||||
|
delay_penalty = obj.dc_tracking_optimization_delay_weight * ...
|
||||||
|
abs(delay_symbols) / max(numel(d),1);
|
||||||
|
objective = ber + delay_penalty;
|
||||||
|
if ~isfinite(objective)
|
||||||
|
objective = inf;
|
||||||
|
end
|
||||||
|
|
||||||
|
obj.dc_tracking_optimization_iter = ...
|
||||||
|
obj.dc_tracking_optimization_iter + 1;
|
||||||
|
if obj.dc_tracking_adaptive_enabled
|
||||||
|
fprintf("\rFFE_DCTracking DC opt %02d: dc_tracking_mu=%9.3e, persistence_gain=%6.3f, mu_eff_max=%9.3e, BER=%9.3e, delay=%7.0f, corr=%6.3f, obj=%9.3e, errors=%d", ...
|
||||||
|
obj.dc_tracking_optimization_iter,params.dc_tracking_mu, ...
|
||||||
|
params.dc_tracking_persistence_gain, ...
|
||||||
|
params.dc_tracking_mu_eff_max,ber,delay_symbols,delay_corr, ...
|
||||||
|
objective,errors);
|
||||||
|
else
|
||||||
|
fprintf("\rFFE_DCTracking DC opt %02d: dc_tracking_mu=%9.3e, BER=%9.3e, delay=%7.0f, corr=%6.3f, obj=%9.3e, errors=%d", ...
|
||||||
|
obj.dc_tracking_optimization_iter,params.dc_tracking_mu, ...
|
||||||
|
ber,delay_symbols,delay_corr,objective,errors);
|
||||||
|
end
|
||||||
|
|
||||||
|
clear cleanup
|
||||||
|
end
|
||||||
|
|
||||||
|
function [y,d_hat] = equalize(obj,x,d,mu,epochs,N,training,showviz)
|
||||||
|
arguments
|
||||||
|
obj
|
||||||
|
x
|
||||||
|
d
|
||||||
|
mu
|
||||||
|
epochs
|
||||||
|
N
|
||||||
|
training
|
||||||
|
showviz
|
||||||
|
end
|
||||||
|
|
||||||
|
unused_showviz = showviz; %#ok<NASGU>
|
||||||
|
x = x(:);
|
||||||
|
d = d(:);
|
||||||
|
N = obj.validSampleLength(N,x,d);
|
||||||
|
n_symbols = N / obj.sps;
|
||||||
|
y = zeros(n_symbols,1);
|
||||||
|
d_hat = zeros(n_symbols,1);
|
||||||
|
|
||||||
|
if n_symbols == 0
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if isempty(obj.constellation)
|
||||||
|
obj.constellation = unique(d);
|
||||||
|
end
|
||||||
|
decision_constellation = obj.constellation(:);
|
||||||
|
|
||||||
|
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
|
||||||
|
lambda = mu;
|
||||||
|
mask = ones(obj.order,1);
|
||||||
|
maincursor_pos = ceil(length(obj.e)/2);
|
||||||
|
adaption_code = obj.adaptionCode();
|
||||||
|
adaption_is_rls = adaption_code == 3;
|
||||||
|
|
||||||
|
if mu == 0 || (~obj.dd_mode && ~training)
|
||||||
|
epochs = 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
dc_tracking_mu_eff_min = obj.dc_tracking_mu_eff_min;
|
||||||
|
dc_tracking_mu_eff_max = obj.dc_tracking_mu_eff_max;
|
||||||
|
dc_tracking_persistence_gain = 0;
|
||||||
|
if obj.dc_tracking_adaptive_enabled
|
||||||
|
dc_tracking_persistence_gain = obj.dc_tracking_persistence_gain;
|
||||||
|
end
|
||||||
|
dc_tracking_enabled = obj.dc_tracking_mu ~= 0;
|
||||||
|
if dc_tracking_enabled
|
||||||
|
obj.dc_tracking_mu = min(max(obj.dc_tracking_mu, ...
|
||||||
|
obj.dc_tracking_mu_min),obj.dc_tracking_mu_max);
|
||||||
|
end
|
||||||
|
dc_tracking_mu = obj.dc_tracking_mu;
|
||||||
|
dc_tracking_use_persistence = dc_tracking_persistence_gain > 0;
|
||||||
|
dc_tracking_base_mu_eff = min(max(dc_tracking_mu, ...
|
||||||
|
dc_tracking_mu_eff_min),dc_tracking_mu_eff_max);
|
||||||
|
dc_buffer_enabled = dc_tracking_enabled && obj.dc_tracking_buffer_len > 1;
|
||||||
|
if dc_buffer_enabled
|
||||||
|
dc_tracking_err_buffer = NaN(obj.dc_tracking_buffer_len,1);
|
||||||
|
dc_tracking_err_buffer_pos = 0;
|
||||||
|
dc_tracking_err_sum = 0;
|
||||||
|
dc_tracking_abs_err_sum = 0;
|
||||||
|
dc_tracking_valid_count = 0;
|
||||||
|
else
|
||||||
|
dc_tracking_err_buffer = [];
|
||||||
|
dc_tracking_err_buffer_pos = 0;
|
||||||
|
dc_tracking_err_sum = 0;
|
||||||
|
dc_tracking_abs_err_sum = 0;
|
||||||
|
dc_tracking_valid_count = 0;
|
||||||
|
end
|
||||||
|
|
||||||
|
debug_enabled = obj.save_debug;
|
||||||
|
if debug_enabled
|
||||||
|
obj.initializeTrackingDebug(n_symbols,training);
|
||||||
|
end
|
||||||
|
|
||||||
|
for epoch = 1:epochs
|
||||||
|
symbol = 0;
|
||||||
|
for sample = 1:obj.sps:N
|
||||||
|
symbol = symbol + 1;
|
||||||
|
grad = zeros(obj.order,1);
|
||||||
|
update = zeros(obj.order,1);
|
||||||
|
weight = 0;
|
||||||
|
dc_tracking_mu_eff = 0;
|
||||||
|
|
||||||
|
U = x(obj.order+sample-1:-1:sample);
|
||||||
|
y(symbol,1) = obj.e_dc + (obj.e.*mask).' * U;
|
||||||
|
|
||||||
|
if training
|
||||||
|
d_hat(symbol,1) = d(symbol);
|
||||||
|
else
|
||||||
|
[~,symbol_idx] = min(abs(y(symbol) - decision_constellation));
|
||||||
|
d_hat(symbol,1) = decision_constellation(symbol_idx);
|
||||||
|
end
|
||||||
|
|
||||||
|
err = d_hat(symbol) - y(symbol);
|
||||||
|
|
||||||
|
switch adaption_code
|
||||||
|
case 1
|
||||||
|
weight = mu / ((U.'*U) + eps);
|
||||||
|
grad = err * U;
|
||||||
|
update = grad * weight;
|
||||||
|
|
||||||
|
case 2
|
||||||
|
weight = mu;
|
||||||
|
grad = err * U;
|
||||||
|
update = grad * weight;
|
||||||
|
|
||||||
|
case 3
|
||||||
|
denom = lambda + U.' * obj.P * U;
|
||||||
|
k = (obj.P * U) / denom;
|
||||||
|
update = k * err;
|
||||||
|
end
|
||||||
|
|
||||||
|
obj.e = obj.e + update;
|
||||||
|
|
||||||
|
if adaption_is_rls
|
||||||
|
obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
|
||||||
|
end
|
||||||
|
|
||||||
|
if dc_tracking_enabled
|
||||||
|
[obj.e_dc,dc_tracking_mu_eff,dc_tracking_err_buffer, ...
|
||||||
|
dc_tracking_err_buffer_pos,dc_tracking_err_sum, ...
|
||||||
|
dc_tracking_abs_err_sum,dc_tracking_valid_count] = ...
|
||||||
|
obj.updateDcTracking( ...
|
||||||
|
err,dc_tracking_mu,dc_tracking_base_mu_eff, ...
|
||||||
|
dc_tracking_persistence_gain,dc_tracking_use_persistence, ...
|
||||||
|
dc_buffer_enabled,dc_tracking_mu_eff_min,dc_tracking_mu_eff_max, ...
|
||||||
|
symbol,dc_tracking_err_buffer,dc_tracking_err_buffer_pos, ...
|
||||||
|
dc_tracking_err_sum,dc_tracking_abs_err_sum, ...
|
||||||
|
dc_tracking_valid_count);
|
||||||
|
end
|
||||||
|
|
||||||
|
if debug_enabled && epoch == epochs
|
||||||
|
error_power = err * err';
|
||||||
|
update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
|
||||||
|
obj.debug_struct.error(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
|
||||||
|
obj.debug_struct.mu_nlms(1,symbol) = weight;
|
||||||
|
obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
|
||||||
|
obj.debug_struct.dc_tracking_mu_eff(1,symbol) = dc_tracking_mu_eff;
|
||||||
|
obj.debug_struct.dc_tracking_est(1,symbol) = obj.e_dc;
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.update_tr(1,symbol) = update_power;
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd(1,symbol) = error_power;
|
||||||
|
obj.debug_struct.update(1,symbol) = update_power;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
methods (Access = private)
|
||||||
|
function resetTrackingState(obj)
|
||||||
|
obj.e = zeros(obj.order,1);
|
||||||
|
obj.e_tr = zeros(obj.order,1);
|
||||||
|
obj.error = 0;
|
||||||
|
obj.e_dc = 0;
|
||||||
|
obj.P = (1/0.05) * eye(obj.order);
|
||||||
|
obj.debug_struct = struct();
|
||||||
|
end
|
||||||
|
|
||||||
|
function state = captureTrackingObjectiveState(obj)
|
||||||
|
state.e = obj.e;
|
||||||
|
state.e_tr = obj.e_tr;
|
||||||
|
state.error = obj.error;
|
||||||
|
state.e_dc = obj.e_dc;
|
||||||
|
state.P = obj.P;
|
||||||
|
state.save_debug = obj.save_debug;
|
||||||
|
state.debug_struct = obj.debug_struct;
|
||||||
|
state.dc_tracking_mu = obj.dc_tracking_mu;
|
||||||
|
state.dc_tracking_persistence_gain = ...
|
||||||
|
obj.dc_tracking_persistence_gain;
|
||||||
|
state.dc_tracking_mu_eff_max = obj.dc_tracking_mu_eff_max;
|
||||||
|
end
|
||||||
|
|
||||||
|
function restoreTrackingObjectiveState(obj,state)
|
||||||
|
obj.e = state.e;
|
||||||
|
obj.e_tr = state.e_tr;
|
||||||
|
obj.error = state.error;
|
||||||
|
obj.e_dc = state.e_dc;
|
||||||
|
obj.P = state.P;
|
||||||
|
obj.save_debug = state.save_debug;
|
||||||
|
obj.debug_struct = state.debug_struct;
|
||||||
|
obj.dc_tracking_mu = state.dc_tracking_mu;
|
||||||
|
obj.dc_tracking_persistence_gain = ...
|
||||||
|
state.dc_tracking_persistence_gain;
|
||||||
|
obj.dc_tracking_mu_eff_max = state.dc_tracking_mu_eff_max;
|
||||||
|
end
|
||||||
|
|
||||||
|
function applyDcTrackingObjectiveParams(obj,params)
|
||||||
|
var_names = string(params.Properties.VariableNames);
|
||||||
|
if any(var_names == "dc_tracking_mu")
|
||||||
|
obj.dc_tracking_mu = params.dc_tracking_mu;
|
||||||
|
end
|
||||||
|
if any(var_names == "dc_tracking_persistence_gain")
|
||||||
|
obj.dc_tracking_persistence_gain = ...
|
||||||
|
params.dc_tracking_persistence_gain;
|
||||||
|
end
|
||||||
|
if any(var_names == "dc_tracking_mu_eff_max")
|
||||||
|
obj.dc_tracking_mu_eff_max = params.dc_tracking_mu_eff_max;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function [delay_symbols,delay_corr] = ...
|
||||||
|
dcTrackingDelayObjective(obj,signal,d)
|
||||||
|
delay_symbols = 0;
|
||||||
|
delay_corr = 0;
|
||||||
|
if ~isfield(obj.debug_struct,"dc_tracking_est") || ...
|
||||||
|
isempty(obj.debug_struct.dc_tracking_est)
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
smooth_len = obj.dc_tracking_optimization_smoothing_len;
|
||||||
|
dc_tracking_est_s = movmean( ...
|
||||||
|
obj.debug_struct.dc_tracking_est(:),smooth_len,"omitnan");
|
||||||
|
avg_lvl_dc = obj.averageLevelTrace(signal,d,smooth_len);
|
||||||
|
|
||||||
|
xcorr_len = min(numel(dc_tracking_est_s),numel(avg_lvl_dc));
|
||||||
|
if xcorr_len < 2
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
inv_dc_xcorr = -dc_tracking_est_s(1:xcorr_len);
|
||||||
|
avg_lvl_xcorr = avg_lvl_dc(1:xcorr_len);
|
||||||
|
inv_dc_xcorr = fillmissing( ...
|
||||||
|
inv_dc_xcorr,"linear","EndValues","nearest");
|
||||||
|
avg_lvl_xcorr = fillmissing( ...
|
||||||
|
avg_lvl_xcorr,"linear","EndValues","nearest");
|
||||||
|
inv_dc_xcorr = inv_dc_xcorr - mean(inv_dc_xcorr,"omitnan");
|
||||||
|
avg_lvl_xcorr = avg_lvl_xcorr - mean(avg_lvl_xcorr,"omitnan");
|
||||||
|
|
||||||
|
if rms(inv_dc_xcorr) <= eps || rms(avg_lvl_xcorr) <= eps
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
[dc_level_xcorr,dc_level_lags] = xcorr( ...
|
||||||
|
inv_dc_xcorr,avg_lvl_xcorr,"coeff");
|
||||||
|
[delay_corr,delay_idx] = max(dc_level_xcorr);
|
||||||
|
delay_symbols = dc_level_lags(delay_idx);
|
||||||
|
if ~isfinite(delay_corr)
|
||||||
|
delay_corr = 0;
|
||||||
|
delay_symbols = 0;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function avg_lvl_dc = averageLevelTrace(obj,signal,d,smooth_len)
|
||||||
|
signal = signal(:);
|
||||||
|
d = d(:);
|
||||||
|
n_symbols = min(numel(signal),numel(d));
|
||||||
|
signal = signal(1:n_symbols);
|
||||||
|
d = d(1:n_symbols);
|
||||||
|
levels = unique(d);
|
||||||
|
avg_for_lvl = NaN(numel(levels),n_symbols);
|
||||||
|
|
||||||
|
for level_idx = 1:numel(levels)
|
||||||
|
level_mask = d == levels(level_idx);
|
||||||
|
level_samples = signal(level_mask);
|
||||||
|
if isempty(level_samples)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
smooth_window = min(smooth_len,numel(level_samples));
|
||||||
|
avg_for_lvl(level_idx,level_mask) = movmean( ...
|
||||||
|
level_samples,smooth_window,"omitnan","Endpoints","shrink");
|
||||||
|
avg_for_lvl(level_idx,:) = obj.interpolateMissingAverage( ...
|
||||||
|
avg_for_lvl(level_idx,:));
|
||||||
|
end
|
||||||
|
|
||||||
|
avg_lvl_dc = mean(avg_for_lvl,1,"omitnan").';
|
||||||
|
end
|
||||||
|
|
||||||
|
function level_average = interpolateMissingAverage(~,level_average)
|
||||||
|
valid_samples = isfinite(level_average);
|
||||||
|
if nnz(valid_samples) == 0
|
||||||
|
return
|
||||||
|
elseif nnz(valid_samples) == 1
|
||||||
|
level_average(:) = level_average(valid_samples);
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
t = 1:numel(level_average);
|
||||||
|
level_average(~valid_samples) = interp1( ...
|
||||||
|
t(valid_samples),level_average(valid_samples), ...
|
||||||
|
t(~valid_samples),"linear","extrap");
|
||||||
|
end
|
||||||
|
|
||||||
|
function N = validSampleLength(obj,N,x,d)
|
||||||
|
N_available = min(numel(x),numel(d) * obj.sps);
|
||||||
|
N = min(N,N_available);
|
||||||
|
N = obj.sps * floor(N / obj.sps);
|
||||||
|
N = max(0,N);
|
||||||
|
end
|
||||||
|
|
||||||
|
function adaption_code = adaptionCode(obj)
|
||||||
|
if obj.adaption_technique == adaption_method.nlms
|
||||||
|
adaption_code = 1;
|
||||||
|
elseif obj.adaption_technique == adaption_method.lms
|
||||||
|
adaption_code = 2;
|
||||||
|
elseif obj.adaption_technique == adaption_method.rls
|
||||||
|
adaption_code = 3;
|
||||||
|
else
|
||||||
|
builtin("error","FFE_DCTracking:InvalidAdaptionTechnique", ...
|
||||||
|
"Unsupported FFE adaption technique.");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function initializeTrackingDebug(obj,n_symbols,training)
|
||||||
|
obj.debug_struct.error = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.error_first_epoch = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.main_cursor = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.mu_nlms = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_gradient = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_tracking_mu_eff = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.dc_tracking_est = NaN(1,n_symbols);
|
||||||
|
|
||||||
|
if training
|
||||||
|
obj.debug_struct.error_tr = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update_tr = NaN(1,n_symbols);
|
||||||
|
else
|
||||||
|
obj.debug_struct.error_dd = NaN(1,n_symbols);
|
||||||
|
obj.debug_struct.update = NaN(1,n_symbols);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function [e_dc,mu_eff,err_buffer,err_buffer_pos,err_sum,abs_err_sum,valid_count] = ...
|
||||||
|
updateDcTracking(obj,err_current,mu_dc,base_mu_eff,persistence_gain, ...
|
||||||
|
use_persistence,buffer_enabled,mu_eff_min,mu_eff_max,symbol, ...
|
||||||
|
err_buffer,err_buffer_pos,err_sum,abs_err_sum,valid_count)
|
||||||
|
|
||||||
|
mu_eff = 0;
|
||||||
|
e_dc = obj.e_dc;
|
||||||
|
|
||||||
|
if buffer_enabled
|
||||||
|
err_buffer_pos = err_buffer_pos + 1;
|
||||||
|
if err_buffer_pos > obj.dc_tracking_buffer_len
|
||||||
|
err_buffer_pos = 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
old_err = err_buffer(err_buffer_pos);
|
||||||
|
if isfinite(old_err)
|
||||||
|
err_sum = err_sum - old_err;
|
||||||
|
valid_count = valid_count - 1;
|
||||||
|
if use_persistence
|
||||||
|
abs_err_sum = abs_err_sum - abs(old_err);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if isfinite(err_current)
|
||||||
|
err_buffer(err_buffer_pos) = err_current;
|
||||||
|
err_sum = err_sum + err_current;
|
||||||
|
valid_count = valid_count + 1;
|
||||||
|
if use_persistence
|
||||||
|
abs_err_sum = abs_err_sum + abs(err_current);
|
||||||
|
end
|
||||||
|
else
|
||||||
|
err_buffer(err_buffer_pos) = NaN;
|
||||||
|
end
|
||||||
|
|
||||||
|
if mod(symbol,obj.dc_tracking_buffer_len) == 0
|
||||||
|
if valid_count == 0
|
||||||
|
err_mean = 0;
|
||||||
|
err_abs_mean = 0;
|
||||||
|
else
|
||||||
|
err_mean = err_sum / valid_count;
|
||||||
|
err_abs_mean = abs_err_sum / valid_count;
|
||||||
|
end
|
||||||
|
|
||||||
|
mu_eff = obj.effectiveDcMu(mu_dc,base_mu_eff,persistence_gain, ...
|
||||||
|
use_persistence,err_mean,err_abs_mean,mu_eff_min,mu_eff_max);
|
||||||
|
e_dc = e_dc + mu_eff * err_mean;
|
||||||
|
end
|
||||||
|
else
|
||||||
|
if isfinite(err_current)
|
||||||
|
err_mean = err_current;
|
||||||
|
err_abs_mean = abs(err_current);
|
||||||
|
else
|
||||||
|
err_mean = 0;
|
||||||
|
err_abs_mean = 0;
|
||||||
|
end
|
||||||
|
|
||||||
|
mu_eff = obj.effectiveDcMu(mu_dc,base_mu_eff,persistence_gain, ...
|
||||||
|
use_persistence,err_mean,err_abs_mean,mu_eff_min,mu_eff_max);
|
||||||
|
e_dc = e_dc + mu_eff * err_mean;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function mu_eff = effectiveDcMu(~,mu_dc,base_mu_eff,persistence_gain, ...
|
||||||
|
use_persistence,err_mean,err_abs_mean,mu_eff_min,mu_eff_max)
|
||||||
|
|
||||||
|
if use_persistence
|
||||||
|
persistence_scale = abs(err_mean) / (err_abs_mean + eps);
|
||||||
|
persistence_scale = min(max(persistence_scale,0),1);
|
||||||
|
mu_eff = mu_dc * (1 + persistence_gain * persistence_scale);
|
||||||
|
mu_eff = min(max(mu_eff,mu_eff_min),mu_eff_max);
|
||||||
|
else
|
||||||
|
mu_eff = base_mu_eff;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
@@ -1,4 +1,4 @@
|
|||||||
function [ffe_results] = ffe(eq_, M, rx_signal, tx_symbols, tx_bits, options)
|
function [ffe_results,eq_signal_sd] = ffe(eq_, M, rx_signal, tx_symbols, tx_bits, options)
|
||||||
% FFE Processes signals through FFE equalizer
|
% FFE Processes signals through FFE equalizer
|
||||||
%
|
%
|
||||||
% Inputs:
|
% Inputs:
|
||||||
@@ -74,16 +74,22 @@ end
|
|||||||
|
|
||||||
% Create FFE results structure
|
% Create FFE results structure
|
||||||
ffe_results = struct();
|
ffe_results = struct();
|
||||||
try
|
config_clear_props = { ...
|
||||||
eq_.e = [];
|
"e", ...
|
||||||
eq_.e2 = [];
|
"e2", ...
|
||||||
eq_.e3 = [];
|
"e3", ...
|
||||||
eq_.b = [];
|
"b", ...
|
||||||
eq_.b2 = [];
|
"b2", ...
|
||||||
eq_.b3 = [];
|
"b3", ...
|
||||||
|
"mu_optimization", ...
|
||||||
|
"dc_optimization", ...
|
||||||
|
"dc_tracking_optimization", ...
|
||||||
|
"a2_level_weight_optimization"};
|
||||||
|
for config_clear_idx = 1:numel(config_clear_props)
|
||||||
|
prop_name = config_clear_props{config_clear_idx};
|
||||||
|
if isprop(eq_,prop_name)
|
||||||
|
eq_.(prop_name) = [];
|
||||||
end
|
end
|
||||||
try
|
|
||||||
eq_.mu_optimization = [];
|
|
||||||
end
|
end
|
||||||
|
|
||||||
ffe_results.config = Equalizerstruct();
|
ffe_results.config = Equalizerstruct();
|
||||||
|
|||||||
@@ -21,7 +21,7 @@ function output = dsp_recipe_minimal(Scpe_sig_raw, Symbols, Tx_bits, options)
|
|||||||
"debug_plots", options.debug_plots);
|
"debug_plots", options.debug_plots);
|
||||||
|
|
||||||
eq_ffe = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-1,"mu_tr",0.4,"order",25,...
|
eq_ffe = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-1,"mu_tr",0.4,"order",25,...
|
||||||
"sps",2,"decide",0,"optmize_mus",0,"dd_mode",options.userParameters.dd_mode,"adaption_technique","nlms","mu_dc",1.021e-05);
|
"sps",2,"decide",0,"optmize_mus",0,"dd_mode",options.userParameters.dd_mode,"adaption_technique","nlms","dc_tracking_mu",1.021e-05);
|
||||||
|
|
||||||
ffe_results = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
ffe_results = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
||||||
"precode_mode", options.duob_mode, ...
|
"precode_mode", options.duob_mode, ...
|
||||||
|
|||||||
@@ -70,8 +70,8 @@ function output = dsp_scope_signal(Scpe_sig_raw, Symbols, Tx_bits, options)
|
|||||||
pf_ = Postfilter("ncoeff", pf_ncoeffs, "useBurg", 1); %#ok<NASGU>
|
pf_ = Postfilter("ncoeff", pf_ncoeffs, "useBurg", 1); %#ok<NASGU>
|
||||||
mlse_ = MLSE("duobinary_output", 0, 'M', M, 'trellis_states', PAMmapper(M,0).levels); %#ok<NASGU>
|
mlse_ = MLSE("duobinary_output", 0, 'M', M, 'trellis_states', PAMmapper(M,0).levels); %#ok<NASGU>
|
||||||
|
|
||||||
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0,"adaption_technique","lms","mu_dc",mu_dc); %#ok<NASGU>
|
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0,"adaption_technique","lms","dc_tracking_mu",mu_dc); %#ok<NASGU>
|
||||||
eq_post = FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption_technique",adaption_method(adaption),"dd_mode",use_dd_mode,"mu_dc",mu_dc); %#ok<NASGU>
|
eq_post = FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption_technique",adaption_method(adaption),"dd_mode",use_dd_mode,"dc_tracking_mu",mu_dc); %#ok<NASGU>
|
||||||
|
|
||||||
mlse_db_enc = MLSE("DIR", [1,1], "duobinary_output", 0, "M", M, "trellis_states", PAMmapper(M,0).levels); %#ok<NASGU>
|
mlse_db_enc = MLSE("DIR", [1,1], "duobinary_output", 0, "M", M, "trellis_states", PAMmapper(M,0).levels); %#ok<NASGU>
|
||||||
eq_db_enc = EQ("Ne", ffe_order_dbtgt, "Nb", dfe_order_dbtgt, "training_length", len_tr, ...
|
eq_db_enc = EQ("Ne", ffe_order_dbtgt, "Nb", dfe_order_dbtgt, "training_length", len_tr, ...
|
||||||
@@ -94,7 +94,7 @@ function output = dsp_scope_signal(Scpe_sig_raw, Symbols, Tx_bits, options)
|
|||||||
if use_ffe
|
if use_ffe
|
||||||
eq_ffe = EQ("Ne",ffe_order_ffe,"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",0);
|
eq_ffe = EQ("Ne",ffe_order_ffe,"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",0);
|
||||||
eq_ffe = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-1,"mu_tr",0.4,"order",ffe_order_ffe(1),...
|
eq_ffe = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-1,"mu_tr",0.4,"order",ffe_order_ffe(1),...
|
||||||
"sps",2,"decide",0,"optmize_mus",0,"dd_mode",1,"adaption_technique","nlms","mu_dc",1.021e-05);
|
"sps",2,"decide",0,"optmize_mus",0,"dd_mode",1,"adaption_technique","nlms","dc_tracking_mu",1.021e-05);
|
||||||
|
|
||||||
ffe_results = ffe(eq_ffe, M, Scpe_sig, Symbols, Tx_bits, ...
|
ffe_results = ffe(eq_ffe, M, Scpe_sig, Symbols, Tx_bits, ...
|
||||||
"precode_mode", duob_mode, ...
|
"precode_mode", duob_mode, ...
|
||||||
|
|||||||
404
Functions/EQ_recipes/mpi_recipe.m
Normal file
404
Functions/EQ_recipes/mpi_recipe.m
Normal file
@@ -0,0 +1,404 @@
|
|||||||
|
function output = mpi_recipe_dev(Scpe_sig_raw, Symbols, Tx_bits, options)
|
||||||
|
%mpi_recipe_dev Minimal example recipe for the DSP job framework.
|
||||||
|
% This recipe intentionally performs only light preprocessing and records
|
||||||
|
% summary values. It demonstrates the recipe interface without running a
|
||||||
|
% full equalizer chain.
|
||||||
|
|
||||||
|
arguments
|
||||||
|
Scpe_sig_raw
|
||||||
|
Symbols
|
||||||
|
Tx_bits
|
||||||
|
options.fsym
|
||||||
|
options.M
|
||||||
|
options.duob_mode
|
||||||
|
options.dataTable table
|
||||||
|
options.userParameters struct = struct()
|
||||||
|
options.debug_plots (1,1) logical = false
|
||||||
|
end
|
||||||
|
|
||||||
|
Scpe_sig = preprocessSignal(Scpe_sig_raw, Symbols, options.fsym, ...
|
||||||
|
"mode", "auto", ...
|
||||||
|
"debug_plots", options.debug_plots);
|
||||||
|
output = struct();
|
||||||
|
|
||||||
|
%% conventional FFE
|
||||||
|
eq_core_settings = { ...
|
||||||
|
"sps", 2, ...
|
||||||
|
"order", 25, ...
|
||||||
|
"decide", 0, ...
|
||||||
|
"adaption_technique", "nlms"};
|
||||||
|
|
||||||
|
eq_training_settings = { ...
|
||||||
|
"len_tr", 4096, ...
|
||||||
|
"epochs_tr", 5, ...
|
||||||
|
"mu_tr", 0.04};
|
||||||
|
|
||||||
|
eq_dd_settings = { ...
|
||||||
|
"dd_mode", 1, ...
|
||||||
|
"epochs_dd", 3, ...
|
||||||
|
"mu_dd", 0.012};
|
||||||
|
|
||||||
|
eq_a1_settings = { ...
|
||||||
|
"dc_smoothing_a1", 0, ...
|
||||||
|
"dc_avg_bufferlength_a1", 0, ...
|
||||||
|
"dc_avg_update_blocklength_a1", 1};
|
||||||
|
|
||||||
|
eq_a2_settings = { ...
|
||||||
|
"dc_smoothing_a2", 0, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 0, ...
|
||||||
|
"dc_level_update_blocklength_a2", 0, ...
|
||||||
|
"dc_level_weights_a2", [0.6 0.6 0.6 0.6]};
|
||||||
|
|
||||||
|
eq_dc_tracking_settings = { ...
|
||||||
|
"dc_tracking_mu", 0, ...
|
||||||
|
"dc_tracking_adaptive_enabled", 0, ...
|
||||||
|
"dc_tracking_persistence_gain", 0, ...
|
||||||
|
"dc_tracking_buffer_len", 0};
|
||||||
|
|
||||||
|
eq_ffe_update_settings = { ...
|
||||||
|
"ffe_update_buffer_len", 1};
|
||||||
|
|
||||||
|
eq_optimizer_settings = { ...
|
||||||
|
"optmize_mus", 0, ...
|
||||||
|
"plot_mu_optimization", options.debug_plots, ...
|
||||||
|
"optimize_dc_tracking_params", 0, ...
|
||||||
|
"optimize_a2_level_weights", 0, ...
|
||||||
|
"a2_level_weight_optimization_len", 2^15, ...
|
||||||
|
"a2_level_weight_optimization_max_evals", 30, ...
|
||||||
|
"a2_level_weight_max", 1};
|
||||||
|
|
||||||
|
eq_debug_settings = { ...
|
||||||
|
"save_debug", false};
|
||||||
|
|
||||||
|
eq_settings = [ ...
|
||||||
|
eq_core_settings, ...
|
||||||
|
eq_training_settings, ...
|
||||||
|
eq_dd_settings, ...
|
||||||
|
eq_a1_settings, ...
|
||||||
|
eq_a2_settings, ...
|
||||||
|
eq_dc_tracking_settings, ...
|
||||||
|
eq_ffe_update_settings, ...
|
||||||
|
eq_optimizer_settings, ...
|
||||||
|
eq_debug_settings];
|
||||||
|
|
||||||
|
eq_ffe = FFE(eq_settings{:});
|
||||||
|
|
||||||
|
if 1
|
||||||
|
|
||||||
|
[ffe_results,equalized_signal] = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
||||||
|
"precode_mode", options.duob_mode, ...
|
||||||
|
'showAnalysis', options.debug_plots, ...
|
||||||
|
"postFFE", [], ...
|
||||||
|
"eth_style_symbol_mapping", 0);
|
||||||
|
|
||||||
|
ffe_results.metrics.print("description",sprintf('Immediate A2; SIR %d dB',options.dataTable.sir));
|
||||||
|
output.conventional_ffe = ffe_results;
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
if options.debug_plots
|
||||||
|
showLevelScatter(Scpe_sig_raw, Symbols, ...
|
||||||
|
"fsym", options.fsym, ...
|
||||||
|
"fignum", 400, ...
|
||||||
|
"normalize", true);
|
||||||
|
|
||||||
|
[~, avg_for_lvl] = showLevelScatter(equalized_signal, Symbols, ...
|
||||||
|
"fsym", options.fsym, ...
|
||||||
|
"fignum", 401, ...
|
||||||
|
"normalize", true);
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
%% A2
|
||||||
|
eq_core_settings = { ...
|
||||||
|
"sps", 2, ...
|
||||||
|
"order", 25, ...
|
||||||
|
"decide", 0, ...
|
||||||
|
"adaption_technique", "nlms"};
|
||||||
|
|
||||||
|
eq_training_settings = { ...
|
||||||
|
"len_tr", 4096, ...
|
||||||
|
"epochs_tr", 5, ...
|
||||||
|
"mu_tr", 0.04};
|
||||||
|
|
||||||
|
eq_dd_settings = { ...
|
||||||
|
"dd_mode", 1, ...
|
||||||
|
"epochs_dd", 3, ...
|
||||||
|
"mu_dd", 0.012};
|
||||||
|
|
||||||
|
eq_a1_settings = { ...
|
||||||
|
"dc_smoothing_a1", 0, ...
|
||||||
|
"dc_avg_bufferlength_a1", 0, ...
|
||||||
|
"dc_avg_update_blocklength_a1", 1};
|
||||||
|
|
||||||
|
eq_a2_settings = { ...
|
||||||
|
"dc_smoothing_a2", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 256, ...
|
||||||
|
"dc_level_update_blocklength_a2", options.userParameters.block_update, ...
|
||||||
|
"dc_level_weights_a2", [0.6 0.6 0.6 0.6]};
|
||||||
|
|
||||||
|
eq_dc_tracking_settings = { ...
|
||||||
|
"dc_tracking_mu", 0, ...
|
||||||
|
"dc_tracking_adaptive_enabled", 0, ...
|
||||||
|
"dc_tracking_persistence_gain", 0, ...
|
||||||
|
"dc_tracking_buffer_len", 0};
|
||||||
|
|
||||||
|
eq_ffe_update_settings = { ...
|
||||||
|
"ffe_update_buffer_len", 1};
|
||||||
|
|
||||||
|
eq_optimizer_settings = { ...
|
||||||
|
"optmize_mus", 0, ...
|
||||||
|
"plot_mu_optimization", options.debug_plots, ...
|
||||||
|
"optimize_dc_tracking_params", 0, ...
|
||||||
|
"optimize_a2_level_weights", 1, ...
|
||||||
|
"a2_level_weight_optimization_len", 2^15, ...
|
||||||
|
"a2_level_weight_optimization_max_evals", 30, ...
|
||||||
|
"a2_level_weight_max", 1};
|
||||||
|
|
||||||
|
eq_debug_settings = { ...
|
||||||
|
"save_debug", false};
|
||||||
|
|
||||||
|
eq_settings = [ ...
|
||||||
|
eq_core_settings, ...
|
||||||
|
eq_training_settings, ...
|
||||||
|
eq_dd_settings, ...
|
||||||
|
eq_a1_settings, ...
|
||||||
|
eq_a2_settings, ...
|
||||||
|
eq_dc_tracking_settings, ...
|
||||||
|
eq_ffe_update_settings, ...
|
||||||
|
eq_optimizer_settings, ...
|
||||||
|
eq_debug_settings];
|
||||||
|
|
||||||
|
eq_ffe = FFE(eq_settings{:});
|
||||||
|
|
||||||
|
if 1
|
||||||
|
|
||||||
|
[ffe_results,equalized_signal] = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
||||||
|
"precode_mode", options.duob_mode, ...
|
||||||
|
'showAnalysis', options.debug_plots, ...
|
||||||
|
"postFFE", [], ...
|
||||||
|
"eth_style_symbol_mapping", 0);
|
||||||
|
|
||||||
|
ffe_results.metrics.print("description",sprintf('Immediate A2; SIR %d dB',options.dataTable.sir));
|
||||||
|
output.a2_immediate_updates = ffe_results;
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
if options.debug_plots
|
||||||
|
showLevelScatter(Scpe_sig_raw, Symbols, ...
|
||||||
|
"fsym", options.fsym, ...
|
||||||
|
"fignum", 400, ...
|
||||||
|
"normalize", true);
|
||||||
|
|
||||||
|
[~, avg_for_lvl] = showLevelScatter(equalized_signal, Symbols, ...
|
||||||
|
"fsym", options.fsym, ...
|
||||||
|
"fignum", 401, ...
|
||||||
|
"normalize", true);
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
%% A1
|
||||||
|
|
||||||
|
eq_a1_settings = { ...
|
||||||
|
"dc_smoothing_a1", 1 ...
|
||||||
|
"dc_avg_bufferlength_a1", 2048, ...
|
||||||
|
"dc_avg_update_blocklength_a1", options.userParameters.block_update};
|
||||||
|
|
||||||
|
eq_a2_settings = { ...
|
||||||
|
"dc_smoothing_a2", 0, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 0, ...
|
||||||
|
"dc_level_update_blocklength_a2", 0, ...
|
||||||
|
"dc_level_weights_a2", [0.6 0.6 0.6 0.6]};
|
||||||
|
|
||||||
|
eq_optimizer_settings = { ...
|
||||||
|
"optmize_mus", 0, ...
|
||||||
|
"plot_mu_optimization", options.debug_plots, ...
|
||||||
|
"optimize_dc_tracking_params", 0, ...
|
||||||
|
"optimize_a2_level_weights", 0, ...
|
||||||
|
"a2_level_weight_optimization_len", 2^15, ...
|
||||||
|
"a2_level_weight_optimization_max_evals", 30, ...
|
||||||
|
"a2_level_weight_max", 1};
|
||||||
|
|
||||||
|
eq_debug_settings = { ...
|
||||||
|
"save_debug", false};
|
||||||
|
|
||||||
|
eq_settings = [ ...
|
||||||
|
eq_core_settings, ...
|
||||||
|
eq_training_settings, ...
|
||||||
|
eq_dd_settings, ...
|
||||||
|
eq_a1_settings, ...
|
||||||
|
eq_a2_settings, ...
|
||||||
|
eq_dc_tracking_settings, ...
|
||||||
|
eq_ffe_update_settings, ...
|
||||||
|
eq_optimizer_settings, ...
|
||||||
|
eq_debug_settings];
|
||||||
|
|
||||||
|
eq_ffe = FFE(eq_settings{:});
|
||||||
|
|
||||||
|
if 1
|
||||||
|
|
||||||
|
[ffe_results,equalized_signal] = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
||||||
|
"precode_mode", options.duob_mode, ...
|
||||||
|
'showAnalysis', options.debug_plots, ...
|
||||||
|
"postFFE", [], ...
|
||||||
|
"eth_style_symbol_mapping", 0);
|
||||||
|
|
||||||
|
ffe_results.metrics.print("description",sprintf('Immediate A1; SIR %d dB',options.dataTable.sir));
|
||||||
|
output.a1_immediate_updates = ffe_results;
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
%% Tracking
|
||||||
|
|
||||||
|
eq_core_settings = { ...
|
||||||
|
"sps", 2, ...
|
||||||
|
"order", 25, ...
|
||||||
|
"decide", 0, ...
|
||||||
|
"adaption_technique", "nlms"};
|
||||||
|
|
||||||
|
eq_training_settings = { ...
|
||||||
|
"len_tr", 4096, ...
|
||||||
|
"epochs_tr", 5, ...
|
||||||
|
"mu_tr", 0.04};
|
||||||
|
|
||||||
|
eq_dd_settings = { ...
|
||||||
|
"dd_mode", 1, ...
|
||||||
|
"epochs_dd", 3, ...
|
||||||
|
"mu_dd", 0.012};
|
||||||
|
|
||||||
|
eq_a1_settings = { ...
|
||||||
|
"dc_smoothing_a1", 0, ...
|
||||||
|
"dc_avg_bufferlength_a1", 0, ...
|
||||||
|
"dc_avg_update_blocklength_a1", 1};
|
||||||
|
|
||||||
|
eq_a2_settings = { ...
|
||||||
|
"dc_smoothing_a2", 0, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 0, ...
|
||||||
|
"dc_level_update_blocklength_a2", 0, ...
|
||||||
|
"dc_level_weights_a2", [0.6 0.6 0.6 0.6]};
|
||||||
|
|
||||||
|
eq_dc_tracking_settings = { ...
|
||||||
|
"dc_tracking_mu", 0.002, ...
|
||||||
|
"dc_tracking_adaptive_enabled", 0, ...
|
||||||
|
"dc_tracking_persistence_gain", 0, ...
|
||||||
|
"dc_tracking_buffer_len", options.userParameters.block_update};
|
||||||
|
|
||||||
|
eq_ffe_update_settings = { ...
|
||||||
|
"ffe_update_buffer_len", 1};
|
||||||
|
|
||||||
|
eq_optimizer_settings = { ...
|
||||||
|
"optmize_mus", 0, ...
|
||||||
|
"plot_mu_optimization", options.debug_plots, ...
|
||||||
|
"optimize_dc_tracking_params", 1, ...
|
||||||
|
"optimize_a2_level_weights", 0, ...
|
||||||
|
"a2_level_weight_optimization_len", 2^15, ...
|
||||||
|
"a2_level_weight_optimization_max_evals", 30, ...
|
||||||
|
"a2_level_weight_max", 1};
|
||||||
|
|
||||||
|
eq_debug_settings = { ...
|
||||||
|
"save_debug", false};
|
||||||
|
|
||||||
|
eq_settings = [ ...
|
||||||
|
eq_core_settings, ...
|
||||||
|
eq_training_settings, ...
|
||||||
|
eq_dd_settings, ...
|
||||||
|
eq_a1_settings, ...
|
||||||
|
eq_a2_settings, ...
|
||||||
|
eq_dc_tracking_settings, ...
|
||||||
|
eq_ffe_update_settings, ...
|
||||||
|
eq_optimizer_settings, ...
|
||||||
|
eq_debug_settings];
|
||||||
|
|
||||||
|
eq_ffe = FFE(eq_settings{:});
|
||||||
|
|
||||||
|
if 1
|
||||||
|
|
||||||
|
[ffe_results,equalized_signal] = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
||||||
|
"precode_mode", options.duob_mode, ...
|
||||||
|
'showAnalysis', options.debug_plots, ...
|
||||||
|
"postFFE", [], ...
|
||||||
|
"eth_style_symbol_mapping", 0);
|
||||||
|
|
||||||
|
ffe_results.metrics.print("description",sprintf('Immediate A2; SIR %d dB',options.dataTable.sir));
|
||||||
|
output.tracking_fixmu_immediate_updates = ffe_results;
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
%% Tracking Adaptive
|
||||||
|
|
||||||
|
eq_core_settings = { ...
|
||||||
|
"sps", 2, ...
|
||||||
|
"order", 25, ...
|
||||||
|
"decide", 0, ...
|
||||||
|
"adaption_technique", "nlms"};
|
||||||
|
|
||||||
|
eq_training_settings = { ...
|
||||||
|
"len_tr", 4096, ...
|
||||||
|
"epochs_tr", 5, ...
|
||||||
|
"mu_tr", 0.04};
|
||||||
|
|
||||||
|
eq_dd_settings = { ...
|
||||||
|
"dd_mode", 1, ...
|
||||||
|
"epochs_dd", 3, ...
|
||||||
|
"mu_dd", 0.012};
|
||||||
|
|
||||||
|
eq_a1_settings = { ...
|
||||||
|
"dc_smoothing_a1", 0, ...
|
||||||
|
"dc_avg_bufferlength_a1", 0, ...
|
||||||
|
"dc_avg_update_blocklength_a1", 1};
|
||||||
|
|
||||||
|
eq_a2_settings = { ...
|
||||||
|
"dc_smoothing_a2", 0, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 0, ...
|
||||||
|
"dc_level_update_blocklength_a2", 0, ...
|
||||||
|
"dc_level_weights_a2", [0.6 0.6 0.6 0.6]};
|
||||||
|
|
||||||
|
eq_dc_tracking_settings = { ...
|
||||||
|
"dc_tracking_mu", 0.002, ...
|
||||||
|
"dc_tracking_adaptive_enabled", 1, ...
|
||||||
|
"dc_tracking_persistence_gain", 0, ...
|
||||||
|
"dc_tracking_buffer_len", options.userParameters.block_update};
|
||||||
|
|
||||||
|
eq_ffe_update_settings = { ...
|
||||||
|
"ffe_update_buffer_len", 1};
|
||||||
|
|
||||||
|
eq_optimizer_settings = { ...
|
||||||
|
"optmize_mus", 0, ...
|
||||||
|
"plot_mu_optimization", options.debug_plots, ...
|
||||||
|
"optimize_dc_tracking_params", 1, ...
|
||||||
|
"optimize_a2_level_weights", 0, ...
|
||||||
|
"a2_level_weight_optimization_len", 2^15, ...
|
||||||
|
"a2_level_weight_optimization_max_evals", 30, ...
|
||||||
|
"a2_level_weight_max", 1};
|
||||||
|
|
||||||
|
eq_debug_settings = { ...
|
||||||
|
"save_debug", false};
|
||||||
|
|
||||||
|
eq_settings = [ ...
|
||||||
|
eq_core_settings, ...
|
||||||
|
eq_training_settings, ...
|
||||||
|
eq_dd_settings, ...
|
||||||
|
eq_a1_settings, ...
|
||||||
|
eq_a2_settings, ...
|
||||||
|
eq_dc_tracking_settings, ...
|
||||||
|
eq_ffe_update_settings, ...
|
||||||
|
eq_optimizer_settings, ...
|
||||||
|
eq_debug_settings];
|
||||||
|
|
||||||
|
eq_ffe = FFE(eq_settings{:});
|
||||||
|
|
||||||
|
if 1
|
||||||
|
|
||||||
|
[ffe_results,equalized_signal] = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
||||||
|
"precode_mode", options.duob_mode, ...
|
||||||
|
'showAnalysis', options.debug_plots, ...
|
||||||
|
"postFFE", [], ...
|
||||||
|
"eth_style_symbol_mapping", 0);
|
||||||
|
|
||||||
|
ffe_results.metrics.print("description",sprintf('Immediate A2; SIR %d dB',options.dataTable.sir));
|
||||||
|
output.tracking_adaptive_immediate_updates = ffe_results;
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
@@ -1,8 +1,5 @@
|
|||||||
function output = mpi_recipe_dev(Scpe_sig_raw, Symbols, Tx_bits, options)
|
function output = mpi_recipe_dev(Scpe_sig_raw, Symbols, Tx_bits, options)
|
||||||
%mpi_recipe_dev Minimal example recipe for the DSP job framework.
|
%mpi_recipe_dev Minimal MPI equalizer comparison recipe.
|
||||||
% This recipe intentionally performs only light preprocessing and records
|
|
||||||
% summary values. It demonstrates the recipe interface without running a
|
|
||||||
% full equalizer chain.
|
|
||||||
|
|
||||||
arguments
|
arguments
|
||||||
Scpe_sig_raw
|
Scpe_sig_raw
|
||||||
@@ -16,77 +13,277 @@ function output = mpi_recipe_dev(Scpe_sig_raw, Symbols, Tx_bits, options)
|
|||||||
options.debug_plots (1,1) logical = false
|
options.debug_plots (1,1) logical = false
|
||||||
end
|
end
|
||||||
|
|
||||||
|
%% Execution toggles
|
||||||
|
|
||||||
|
run_conventional_ffe = 1;
|
||||||
|
run_a2_tracked_levels = 1;
|
||||||
|
run_a2_residual = 1;
|
||||||
|
run_a1 = 1;
|
||||||
|
run_tracking_adaptive = 1;
|
||||||
|
plot_output_signals = 0;
|
||||||
|
|
||||||
|
%% Shared fixed EQ settings
|
||||||
|
eq_sps = 2;
|
||||||
|
eq_order = 50;
|
||||||
|
eq_adaption = "nlms";
|
||||||
|
eq_len_tr = 4096;
|
||||||
|
eq_epochs_tr = 5;
|
||||||
|
eq_mu_tr = 0.04;
|
||||||
|
eq_epochs_dd = 3;
|
||||||
|
eq_save_debug = false;
|
||||||
|
|
||||||
|
block_update = 1;
|
||||||
|
if isfield(options.userParameters,"block_update")
|
||||||
|
block_update = options.userParameters.block_update;
|
||||||
|
end
|
||||||
|
|
||||||
Scpe_sig = preprocessSignal(Scpe_sig_raw, Symbols, options.fsym, ...
|
Scpe_sig = preprocessSignal(Scpe_sig_raw, Symbols, options.fsym, ...
|
||||||
"mode", "auto", ...
|
"mode", "auto", ...
|
||||||
"debug_plots", options.debug_plots);
|
"debug_plots", options.debug_plots);
|
||||||
|
output = struct();
|
||||||
|
|
||||||
|
%%
|
||||||
mu_dc = 0;%1e-5;
|
if plot_output_signals
|
||||||
|
showLevelScatter(Scpe_sig, Symbols, ...
|
||||||
eq_settings = { ...
|
|
||||||
"epochs_tr", 5, ...
|
|
||||||
"epochs_dd", 5, ...
|
|
||||||
"len_tr", 4096*2, ...
|
|
||||||
"mu_dd",0.02, ...
|
|
||||||
"mu_tr",0.2, ...
|
|
||||||
"order", 25, ...
|
|
||||||
"sps", 2, ...
|
|
||||||
"decide", 0, ...
|
|
||||||
"optmize_mus", 1, ...
|
|
||||||
"dd_mode", 1, ...
|
|
||||||
"adaption_technique", "nlms", ...
|
|
||||||
"mu_dc", mu_dc};
|
|
||||||
|
|
||||||
eq_ffe = FFE(eq_settings{:});
|
|
||||||
|
|
||||||
showLevelScatter(Scpe_sig_raw, Symbols, ...
|
|
||||||
"fsym", options.fsym, ...
|
"fsym", options.fsym, ...
|
||||||
"fignum", 400, ...
|
"fignum", 400, ...
|
||||||
"normalize", true);
|
"normalize", true);
|
||||||
|
end
|
||||||
|
%% Conventional FFE
|
||||||
|
if run_conventional_ffe
|
||||||
|
eq_ffe = FFE_plain( ...
|
||||||
|
"sps", eq_sps, ...
|
||||||
|
"order", eq_order, ...
|
||||||
|
"decide", false, ...
|
||||||
|
"adaption_technique", eq_adaption, ...
|
||||||
|
"len_tr", eq_len_tr, ...
|
||||||
|
"epochs_tr", eq_epochs_tr, ...
|
||||||
|
"mu_tr", eq_mu_tr, ...
|
||||||
|
"dd_mode", true, ...
|
||||||
|
"epochs_dd", eq_epochs_dd, ...
|
||||||
|
"mu_dd", 0.0012, ...
|
||||||
|
"optmize_mus", false, ...
|
||||||
|
"plot_mu_optimization", options.debug_plots, ...
|
||||||
|
"save_debug", eq_save_debug);
|
||||||
|
|
||||||
% Scpe_sig_raw.spectrum("normalizeTo0dB",1,"fft_length",4096*4,"fignum",401);
|
storageName = "conventional_ffe";
|
||||||
|
[ffe_results,equalized_signal] = runFfe(eq_ffe, "Conventional FFE", ...
|
||||||
|
Scpe_sig, Symbols, Tx_bits, options);
|
||||||
|
ffe_results = attachMpiReductionConfig(ffe_results, eq_ffe, storageName, ...
|
||||||
|
"plain_ffe", "baseline", block_update);
|
||||||
|
output.(char(storageName)) = ffe_results;
|
||||||
|
|
||||||
%options.dataTable.sir;
|
if plot_output_signals
|
||||||
|
plotEqSignals(equalized_signal,Symbols,options,400,-1);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
%% NORMAL FFE
|
%% A2 tracked-level decision
|
||||||
if 1
|
if run_a2_tracked_levels
|
||||||
|
eq_ffe = FFE_A2TrackedLevels( ...
|
||||||
|
"sps", eq_sps, ...
|
||||||
|
"order", eq_order, ...
|
||||||
|
"decide", true, ...
|
||||||
|
"adaption_technique", eq_adaption, ...
|
||||||
|
"len_tr", eq_len_tr, ...
|
||||||
|
"epochs_tr", eq_epochs_tr, ...
|
||||||
|
"mu_tr", eq_mu_tr, ...
|
||||||
|
"dd_mode", true, ...
|
||||||
|
"epochs_dd", eq_epochs_dd, ...
|
||||||
|
"mu_dd", 0.012, ...
|
||||||
|
"dc_smoothing_a2", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 112, ...
|
||||||
|
"dc_level_update_blocklength_a2", block_update, ...
|
||||||
|
"dc_level_weights_a2", [1], ...
|
||||||
|
"save_debug", eq_save_debug);
|
||||||
|
|
||||||
ffe_results = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
storageName = "a2_adaptive_levels";
|
||||||
|
[ffe_results,equalized_signal] = runFfe(eq_ffe, "A2 tracked levels", ...
|
||||||
|
Scpe_sig, Symbols, Tx_bits, options);
|
||||||
|
ffe_results = attachMpiReductionConfig(ffe_results, eq_ffe, storageName, ...
|
||||||
|
"a2_tracked_levels", "tracked_levels", block_update);
|
||||||
|
output.(char(storageName)) = ffe_results;
|
||||||
|
|
||||||
|
if plot_output_signals
|
||||||
|
plotEqSignals(equalized_signal,Symbols,options,410,-1);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
%% A2 residual correction
|
||||||
|
if run_a2_residual
|
||||||
|
eq_ffe = FFE_A2Residual( ...
|
||||||
|
"sps", eq_sps, ...
|
||||||
|
"order", eq_order, ...
|
||||||
|
"decide", false, ...
|
||||||
|
"adaption_technique", eq_adaption, ...
|
||||||
|
"len_tr", eq_len_tr, ...
|
||||||
|
"epochs_tr", eq_epochs_tr, ...
|
||||||
|
"mu_tr", eq_mu_tr, ...
|
||||||
|
"dd_mode", true, ...
|
||||||
|
"epochs_dd", eq_epochs_dd, ...
|
||||||
|
"mu_dd", 0.012, ...
|
||||||
|
"dc_smoothing_a2", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 112, ...
|
||||||
|
"dc_level_update_blocklength_a2", block_update, ...
|
||||||
|
"dc_level_weights_a2", [0.6], ...
|
||||||
|
"save_debug", eq_save_debug, "optmize_mus",0);
|
||||||
|
|
||||||
|
storageName = "a2_residual";
|
||||||
|
[ffe_results,equalized_signal] = runFfe(eq_ffe, "A2 residual", ...
|
||||||
|
Scpe_sig, Symbols, Tx_bits, options);
|
||||||
|
ffe_results = attachMpiReductionConfig(ffe_results, eq_ffe, storageName, ...
|
||||||
|
"a2_residual", "residual_correction", block_update);
|
||||||
|
output.(char(storageName)) = ffe_results;
|
||||||
|
|
||||||
|
if plot_output_signals
|
||||||
|
plotEqSignals(equalized_signal,Symbols,options,420,-1);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
%% A1 moving-average input suppression
|
||||||
|
if run_a1
|
||||||
|
eq_ffe = FFE_A1( ...
|
||||||
|
"sps", eq_sps, ...
|
||||||
|
"order", eq_order, ...
|
||||||
|
"decide", false, ...
|
||||||
|
"adaption_technique", eq_adaption, ...
|
||||||
|
"len_tr", eq_len_tr, ...
|
||||||
|
"epochs_tr", eq_epochs_tr, ...
|
||||||
|
"mu_tr", eq_mu_tr, ...
|
||||||
|
"dd_mode", false, ...
|
||||||
|
"epochs_dd", eq_epochs_dd, ...
|
||||||
|
"mu_dd", 0.012, ...
|
||||||
|
"dc_smoothing_a1", 1, ...
|
||||||
|
"dc_avg_bufferlength_a1", 2048, ...
|
||||||
|
"dc_avg_update_blocklength_a1", block_update, ...
|
||||||
|
"save_debug", eq_save_debug);
|
||||||
|
|
||||||
|
storageName = "a1_ff_dc_avg";
|
||||||
|
[ffe_results,equalized_signal] = runFfe(eq_ffe, "A1", ...
|
||||||
|
Scpe_sig, Symbols, Tx_bits, options);
|
||||||
|
ffe_results = attachMpiReductionConfig(ffe_results, eq_ffe, storageName, ...
|
||||||
|
"a1_moving_average", "ff_dc_avg", block_update);
|
||||||
|
output.(char(storageName)) = ffe_results;
|
||||||
|
|
||||||
|
if plot_output_signals
|
||||||
|
plotEqSignals(equalized_signal,Symbols,options,430,-1);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
%% DC tracking, adaptive/persistence path
|
||||||
|
if run_tracking_adaptive
|
||||||
|
eq_ffe = FFE_DCTracking( ...
|
||||||
|
"sps", eq_sps, ...
|
||||||
|
"order", eq_order, ...
|
||||||
|
"decide", false, ...
|
||||||
|
"adaption_technique", eq_adaption, ...
|
||||||
|
"len_tr", eq_len_tr, ...
|
||||||
|
"epochs_tr", eq_epochs_tr, ...
|
||||||
|
"mu_tr", eq_mu_tr, ...
|
||||||
|
"dd_mode", true, ...
|
||||||
|
"epochs_dd", eq_epochs_dd, ...
|
||||||
|
"mu_dd", 0.012, ...
|
||||||
|
"dc_tracking_mu", 0.002, ...
|
||||||
|
"dc_tracking_adaptive_enabled", true, ...
|
||||||
|
"dc_tracking_persistence_gain", 0, ...
|
||||||
|
"dc_tracking_buffer_len", block_update, ...
|
||||||
|
"optmize_mus", false, ...
|
||||||
|
"optimize_dc_tracking_params", true, ...
|
||||||
|
"dc_tracking_optimization_len", 2^15, ...
|
||||||
|
"dc_tracking_optimization_max_evals", 20, ...
|
||||||
|
"plot_mu_optimization", options.debug_plots, ...
|
||||||
|
"save_debug", eq_save_debug);
|
||||||
|
|
||||||
|
storageName = "dc_tracking";
|
||||||
|
[ffe_results,equalized_signal] = runFfe(eq_ffe, "DC tracking adaptive", ...
|
||||||
|
Scpe_sig, Symbols, Tx_bits, options);
|
||||||
|
ffe_results = attachMpiReductionConfig(ffe_results, eq_ffe, storageName, ...
|
||||||
|
"dc_tracking", "adaptive", block_update);
|
||||||
|
output.(char(storageName)) = ffe_results;
|
||||||
|
|
||||||
|
if plot_output_signals
|
||||||
|
plotEqSignals(equalized_signal,Symbols,options,450,-1);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
function [ffe_results,equalized_signal] = runFfe(eq_ffe,description,Scpe_sig,Symbols,Tx_bits,options)
|
||||||
|
[ffe_results,equalized_signal] = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
||||||
"precode_mode", options.duob_mode, ...
|
"precode_mode", options.duob_mode, ...
|
||||||
'showAnalysis', options.debug_plots, ...
|
"showAnalysis", options.debug_plots, ...
|
||||||
"postFFE", [], ...
|
"postFFE", [], ...
|
||||||
"eth_style_symbol_mapping", 0);
|
"eth_style_symbol_mapping", 0);
|
||||||
|
|
||||||
ffe_results.metrics.print("description",sprintf('Normal FFE; SIR %d dB',options.dataTable.sir));
|
ffe_results.metrics.print("description",resultDescription(description,options));
|
||||||
output.ffe_package = ffe_results;
|
|
||||||
|
|
||||||
end
|
end
|
||||||
|
|
||||||
%% MPI Reduction
|
function ffe_results = attachMpiReductionConfig(ffe_results, eq_ffe, storageName, ...
|
||||||
if 1
|
algorithm, algorithmVariant, block_update)
|
||||||
|
ffe_results.mpi_reduction_config = struct( ...
|
||||||
|
"storage_name", char(storageName), ...
|
||||||
|
"algorithm", char(algorithm), ...
|
||||||
|
"algorithm_variant", char(algorithmVariant), ...
|
||||||
|
"eq_class", class(eq_ffe), ...
|
||||||
|
"params", collectMpiReductionParams(eq_ffe, block_update));
|
||||||
|
end
|
||||||
|
|
||||||
% dc_buffer_len = 0;
|
function params = collectMpiReductionParams(eq_ffe, block_update)
|
||||||
% ffe_buffer_len = 0;
|
params = struct();
|
||||||
% smoothing_buffer_length = options.userParameters.smoothing_length;
|
params.block_update = block_update;
|
||||||
% smoothing_buffer_update = 1;
|
|
||||||
|
|
||||||
% eq_ffe_dcr = FFE_DCremoval_adaptive_mu(eq_settings{:}, ...
|
whitelistedProps = [ ...
|
||||||
% "dc_buffer_len",dc_buffer_len, ...
|
"sps", ...
|
||||||
% "ffe_buffer_len",ffe_buffer_len,...
|
"order", ...
|
||||||
% "smoothing_buffer_length",smoothing_buffer_length,...
|
"decide", ...
|
||||||
% "smoothing_buffer_update",smoothing_buffer_update);
|
"adaption_technique", ...
|
||||||
|
"len_tr", ...
|
||||||
|
"epochs_tr", ...
|
||||||
|
"mu_tr", ...
|
||||||
|
"dd_mode", ...
|
||||||
|
"epochs_dd", ...
|
||||||
|
"mu_dd", ...
|
||||||
|
"optmize_mus", ...
|
||||||
|
"plot_mu_optimization", ...
|
||||||
|
"save_debug", ...
|
||||||
|
"dc_smoothing_a1", ...
|
||||||
|
"dc_avg_bufferlength_a1", ...
|
||||||
|
"dc_avg_update_blocklength_a1", ...
|
||||||
|
"dc_smoothing_a2", ...
|
||||||
|
"dc_level_avg_bufferlength_a2", ...
|
||||||
|
"dc_level_update_blocklength_a2", ...
|
||||||
|
"dc_level_weights_a2", ...
|
||||||
|
"dc_tracking_mu", ...
|
||||||
|
"dc_tracking_adaptive_enabled", ...
|
||||||
|
"dc_tracking_persistence_gain", ...
|
||||||
|
"dc_tracking_buffer_len", ...
|
||||||
|
"dc_tracking_mu_eff_max", ...
|
||||||
|
"optimize_dc_tracking_params", ...
|
||||||
|
"dc_tracking_optimization_len", ...
|
||||||
|
"dc_tracking_optimization_max_evals", ...
|
||||||
|
"dc_tracking_optimization_delay_weight", ...
|
||||||
|
"dc_tracking_optimization_smoothing_len"];
|
||||||
|
|
||||||
eq_ = FFE_DCremoval("epochs_tr",5,"epochs_dd",3,"len_tr",4096*2,"mu_dd",...
|
for propIdx = 1:numel(whitelistedProps)
|
||||||
0.0002,"mu_tr",0,"order",25,"sps",2,"decide",0,...
|
propName = char(whitelistedProps(propIdx));
|
||||||
"mu_dc",0.005,"dc_buffer_len",1);
|
if isprop(eq_ffe, propName)
|
||||||
|
params.(propName) = eq_ffe.(propName);
|
||||||
ffe_results_dcr = ffe(eq_, options.M, Scpe_sig, Symbols, Tx_bits, ...
|
|
||||||
"precode_mode", options.duob_mode, ...
|
|
||||||
'showAnalysis', options.debug_plots, ...
|
|
||||||
"postFFE", [], ...
|
|
||||||
"eth_style_symbol_mapping", 0);
|
|
||||||
|
|
||||||
ffe_results_dcr.metrics.print("description",'FFE DCR');
|
|
||||||
output.ffe_dcr_package = ffe_results_dcr;
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function description = resultDescription(prefix,options)
|
||||||
|
sir = options.dataTable.sir;
|
||||||
|
if numel(sir) > 1
|
||||||
|
sir = sir(1);
|
||||||
|
end
|
||||||
|
description = sprintf('%s; SIR %g dB',prefix,sir);
|
||||||
|
end
|
||||||
|
|
||||||
|
function plotEqSignals(equalized_signal,Symbols,options,fignum,output_scale)
|
||||||
|
|
||||||
|
showLevelScatter(equalized_signal .* output_scale, Symbols, ...
|
||||||
|
"fsym", options.fsym, ...
|
||||||
|
"fignum", fignum + 1, ...
|
||||||
|
"normalize", true);
|
||||||
|
end
|
||||||
|
|||||||
320
Functions/EQ_visuals/exportLevelScatterTikz.m
Normal file
320
Functions/EQ_visuals/exportLevelScatterTikz.m
Normal file
@@ -0,0 +1,320 @@
|
|||||||
|
function exportLevelScatterTikz(sourceAx, pngFile, tikzFile, pngTikzPath, options)
|
||||||
|
%EXPORTLEVELSCATTERTIKZ Export showLevelScatter axes as PNG-backed TikZ.
|
||||||
|
% Rasterizes scatter objects into a PNG layer and writes average lines plus
|
||||||
|
% text annotations as PGFPlots objects.
|
||||||
|
|
||||||
|
arguments
|
||||||
|
sourceAx
|
||||||
|
pngFile (1,1) string
|
||||||
|
tikzFile (1,1) string
|
||||||
|
pngTikzPath (1,1) string
|
||||||
|
options.resolutionDpi (1,1) double {mustBePositive, mustBeInteger} = 150
|
||||||
|
options.xLabel (1,1) string = "Time in $\mu$s"
|
||||||
|
options.yLabel (1,1) string = "Normalized Amplitude"
|
||||||
|
options.generatedBy (1,1) string = "exportLevelScatterTikz.m"
|
||||||
|
end
|
||||||
|
|
||||||
|
outputDir = fileparts(pngFile);
|
||||||
|
if ~exist(outputDir, "dir")
|
||||||
|
mkdir(outputDir);
|
||||||
|
end
|
||||||
|
|
||||||
|
exportScatterLayerPng(sourceAx, pngFile, options.resolutionDpi);
|
||||||
|
|
||||||
|
linePlots = collectTikzLinePlots(sourceAx);
|
||||||
|
textAnnotations = collectTikzTextAnnotations(sourceAx);
|
||||||
|
writeTikzPngAxis(tikzFile, pngTikzPath, sourceAx, linePlots, textAnnotations, options);
|
||||||
|
end
|
||||||
|
|
||||||
|
function exportScatterLayerPng(sourceAx, pngFile, resolutionDpi)
|
||||||
|
sourceFig = ancestor(sourceAx, "figure");
|
||||||
|
xLimits = sourceAx.XLim;
|
||||||
|
yLimits = sourceAx.YLim;
|
||||||
|
scatterHandles = findGraphicsObjectsByType(sourceAx, "scatter");
|
||||||
|
|
||||||
|
exportFig = figure( ...
|
||||||
|
"Visible", "off", ...
|
||||||
|
"Color", "white", ...
|
||||||
|
"Units", sourceFig.Units, ...
|
||||||
|
"Position", sourceFig.Position);
|
||||||
|
cleanupFigure = onCleanup(@() close(exportFig));
|
||||||
|
|
||||||
|
exportAx = copyobj(sourceAx, exportFig);
|
||||||
|
exportAx.Units = "normalized";
|
||||||
|
exportAx.Position = [0 0 1 1];
|
||||||
|
exportAx.XLim = xLimits;
|
||||||
|
exportAx.YLim = yLimits;
|
||||||
|
exportAx.XLimMode = "manual";
|
||||||
|
exportAx.YLimMode = "manual";
|
||||||
|
stripAxesToScatterOnly(exportAx);
|
||||||
|
hideAxesInkForRasterExport(exportAx);
|
||||||
|
|
||||||
|
fprintf("Exporting scatter PNG with XLim=[%g %g], YLim=[%g %g], scatter objects=%d: %s\n", ...
|
||||||
|
xLimits(1), xLimits(2), yLimits(1), yLimits(2), numel(scatterHandles), pngFile);
|
||||||
|
drawnow;
|
||||||
|
exportFig.PaperPositionMode = "auto";
|
||||||
|
print(exportFig, char(pngFile), "-dpng", sprintf("-r%d", resolutionDpi));
|
||||||
|
end
|
||||||
|
|
||||||
|
function stripAxesToScatterOnly(ax)
|
||||||
|
plotObjects = findall(ax);
|
||||||
|
for objIdx = 1:numel(plotObjects)
|
||||||
|
obj = plotObjects(objIdx);
|
||||||
|
if obj == ax || ~isvalid(obj)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
objType = string(get(obj, "Type"));
|
||||||
|
if lower(objType) ~= "scatter"
|
||||||
|
delete(obj);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function hideAxesInkForRasterExport(ax)
|
||||||
|
ax.Visible = "on";
|
||||||
|
ax.Color = "white";
|
||||||
|
ax.Box = "off";
|
||||||
|
ax.XGrid = "off";
|
||||||
|
ax.YGrid = "off";
|
||||||
|
ax.XMinorGrid = "off";
|
||||||
|
ax.YMinorGrid = "off";
|
||||||
|
ax.XTick = [];
|
||||||
|
ax.YTick = [];
|
||||||
|
ax.XColor = "white";
|
||||||
|
ax.YColor = "white";
|
||||||
|
ax.Title.String = "";
|
||||||
|
ax.XLabel.String = "";
|
||||||
|
ax.YLabel.String = "";
|
||||||
|
end
|
||||||
|
|
||||||
|
function handles = findGraphicsObjectsByType(parentHandle, objectType)
|
||||||
|
allHandles = findall(parentHandle);
|
||||||
|
matches = false(size(allHandles));
|
||||||
|
for handleIdx = 1:numel(allHandles)
|
||||||
|
currentHandle = allHandles(handleIdx);
|
||||||
|
if ~isvalid(currentHandle)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
matches(handleIdx) = lower(string(get(currentHandle, "Type"))) == lower(string(objectType));
|
||||||
|
end
|
||||||
|
|
||||||
|
handles = allHandles(matches);
|
||||||
|
end
|
||||||
|
|
||||||
|
function linePlots = collectTikzLinePlots(sourceAx)
|
||||||
|
lineHandles = findGraphicsObjectsByType(sourceAx, "line");
|
||||||
|
lineHandles = flipud(lineHandles(:));
|
||||||
|
|
||||||
|
linePlots = repmat(struct( ...
|
||||||
|
"xData", [], ...
|
||||||
|
"yData", [], ...
|
||||||
|
"color", [], ...
|
||||||
|
"lineWidth", []), numel(lineHandles), 1);
|
||||||
|
validLineCount = 0;
|
||||||
|
|
||||||
|
for lineIdx = 1:numel(lineHandles)
|
||||||
|
lineHandle = lineHandles(lineIdx);
|
||||||
|
xData = lineHandle.XData(:);
|
||||||
|
yData = lineHandle.YData(:);
|
||||||
|
validSamples = isfinite(xData) & isfinite(yData);
|
||||||
|
|
||||||
|
if ~any(validSamples)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
validLineCount = validLineCount + 1;
|
||||||
|
linePlots(validLineCount).xData = xData(validSamples);
|
||||||
|
linePlots(validLineCount).yData = yData(validSamples);
|
||||||
|
linePlots(validLineCount).color = lineHandle.Color;
|
||||||
|
linePlots(validLineCount).lineWidth = lineHandle.LineWidth;
|
||||||
|
end
|
||||||
|
|
||||||
|
linePlots = linePlots(1:validLineCount);
|
||||||
|
end
|
||||||
|
|
||||||
|
function textAnnotations = collectTikzTextAnnotations(sourceAx)
|
||||||
|
textHandles = findGraphicsObjectsByType(sourceAx, "text");
|
||||||
|
textHandles = flipud(textHandles(:));
|
||||||
|
xLimits = sourceAx.XLim;
|
||||||
|
yLimits = sourceAx.YLim;
|
||||||
|
|
||||||
|
textAnnotations = repmat(struct( ...
|
||||||
|
"x", [], ...
|
||||||
|
"y", [], ...
|
||||||
|
"text", "", ...
|
||||||
|
"anchor", "center"), numel(textHandles), 1);
|
||||||
|
validTextCount = 0;
|
||||||
|
|
||||||
|
for textIdx = 1:numel(textHandles)
|
||||||
|
textHandle = textHandles(textIdx);
|
||||||
|
labelText = string(textHandle.String);
|
||||||
|
if strlength(strtrim(labelText)) == 0
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
textPosition = textHandle.Position;
|
||||||
|
if textPosition(1) < xLimits(1) || textPosition(1) > xLimits(2) || ...
|
||||||
|
textPosition(2) < yLimits(1) || textPosition(2) > yLimits(2)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
validTextCount = validTextCount + 1;
|
||||||
|
textAnnotations(validTextCount).x = textPosition(1);
|
||||||
|
textAnnotations(validTextCount).y = textPosition(2);
|
||||||
|
textAnnotations(validTextCount).text = labelText;
|
||||||
|
textAnnotations(validTextCount).anchor = matlabTextAlignmentToTikzAnchor( ...
|
||||||
|
textHandle.HorizontalAlignment, textHandle.VerticalAlignment);
|
||||||
|
end
|
||||||
|
|
||||||
|
textAnnotations = textAnnotations(1:validTextCount);
|
||||||
|
end
|
||||||
|
|
||||||
|
function writeTikzPngAxis(tikzFile, pngTikzPath, sourceAx, linePlots, textAnnotations, options)
|
||||||
|
outputDir = fileparts(tikzFile);
|
||||||
|
if ~exist(outputDir, "dir")
|
||||||
|
mkdir(outputDir);
|
||||||
|
end
|
||||||
|
|
||||||
|
xLimits = sourceAx.XLim;
|
||||||
|
yLimits = sourceAx.YLim;
|
||||||
|
fid = fopen(tikzFile, "w");
|
||||||
|
if fid < 0
|
||||||
|
error("exportLevelScatterTikz:TikzOpenFailed", ...
|
||||||
|
"Could not open TikZ export file: %s", tikzFile);
|
||||||
|
end
|
||||||
|
cleanupFile = onCleanup(@() fclose(fid));
|
||||||
|
|
||||||
|
axisOptions = {
|
||||||
|
" every axis/.append style={font=\scriptsize},"
|
||||||
|
"width=\fwidth,"
|
||||||
|
"height=\fheight,"
|
||||||
|
"at={(0\fwidth,0\fheight)},"
|
||||||
|
"scale only axis,"
|
||||||
|
"axis on top,"
|
||||||
|
"xmin=" + formatPgfNumber(xLimits(1)) + ","
|
||||||
|
"xmax=" + formatPgfNumber(xLimits(2)) + ","
|
||||||
|
"xlabel style={font=\color{white!15!black}\small},"
|
||||||
|
"xlabel={" + options.xLabel + "},"
|
||||||
|
"ymin=" + formatPgfNumber(yLimits(1)) + ","
|
||||||
|
"ymax=" + formatPgfNumber(yLimits(2)) + ","
|
||||||
|
"ylabel style={font=\color{white!15!black}\small},"
|
||||||
|
"ylabel={" + options.yLabel + "},"
|
||||||
|
"axis background/.style={fill=white},"
|
||||||
|
"xmajorgrids,"
|
||||||
|
"ymajorgrids,"
|
||||||
|
"grid style={line width=0.4pt, dotted, color=black!20},"
|
||||||
|
"scaled ticks=false,"
|
||||||
|
"tick label style={/pgf/number format/fixed, /pgf/number format/1000 sep={}},"
|
||||||
|
"legend columns=1"
|
||||||
|
};
|
||||||
|
|
||||||
|
fprintf(fid, "%% This file was generated by %s.\n", options.generatedBy);
|
||||||
|
fprintf(fid, "%%\n");
|
||||||
|
for lineIdx = 1:numel(linePlots)
|
||||||
|
fprintf(fid, "%s\n", formatTikzColorDefinition(lineIdx, linePlots(lineIdx).color));
|
||||||
|
end
|
||||||
|
if ~isempty(linePlots)
|
||||||
|
fprintf(fid, "%%\n");
|
||||||
|
end
|
||||||
|
fprintf(fid, "%s\n\n", "\begin{tikzpicture}");
|
||||||
|
fprintf(fid, "%s\n", "\begin{axis}[%");
|
||||||
|
for optionIdx = 1:numel(axisOptions)
|
||||||
|
fprintf(fid, "%s\n", axisOptions{optionIdx});
|
||||||
|
end
|
||||||
|
fprintf(fid, "%s\n", "]");
|
||||||
|
|
||||||
|
graphicsLine = sprintf( ...
|
||||||
|
"\\addplot [forget plot] graphics [xmin=%s, xmax=%s, ymin=%s, ymax=%s] {%s};", ...
|
||||||
|
formatPgfNumber(xLimits(1)), ...
|
||||||
|
formatPgfNumber(xLimits(2)), ...
|
||||||
|
formatPgfNumber(yLimits(1)), ...
|
||||||
|
formatPgfNumber(yLimits(2)), ...
|
||||||
|
char(strrep(pngTikzPath, "\", "/")));
|
||||||
|
fprintf(fid, "%s\n\n", graphicsLine);
|
||||||
|
writeTikzLinePlots(fid, linePlots);
|
||||||
|
writeTikzTextAnnotations(fid, textAnnotations);
|
||||||
|
fprintf(fid, "%s\n\n", "\end{axis}");
|
||||||
|
fprintf(fid, "%s", "\end{tikzpicture}%");
|
||||||
|
end
|
||||||
|
|
||||||
|
function valueText = formatPgfNumber(value)
|
||||||
|
valueText = string(sprintf("%.15g", value));
|
||||||
|
end
|
||||||
|
|
||||||
|
function colorDefinition = formatTikzColorDefinition(colorIdx, rgbColor)
|
||||||
|
colorDefinition = sprintf( ...
|
||||||
|
"\\definecolor{mycolor%d}{rgb}{%.5f,%.5f,%.5f}%%", ...
|
||||||
|
colorIdx, rgbColor(1), rgbColor(2), rgbColor(3));
|
||||||
|
end
|
||||||
|
|
||||||
|
function writeTikzLinePlots(fid, linePlots)
|
||||||
|
for lineIdx = 1:numel(linePlots)
|
||||||
|
fprintf(fid, "\\addplot [color=mycolor%d, line width=%.1fpt, forget plot]\n", ...
|
||||||
|
lineIdx, linePlots(lineIdx).lineWidth);
|
||||||
|
fprintf(fid, " table[row sep=crcr]{%%\n");
|
||||||
|
|
||||||
|
for sampleIdx = 1:numel(linePlots(lineIdx).xData)
|
||||||
|
fprintf(fid, "%s\t%s\\\\\n", ...
|
||||||
|
formatPgfNumber(linePlots(lineIdx).xData(sampleIdx)), ...
|
||||||
|
formatPgfNumber(linePlots(lineIdx).yData(sampleIdx)));
|
||||||
|
end
|
||||||
|
|
||||||
|
fprintf(fid, "};\n\n");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function writeTikzTextAnnotations(fid, textAnnotations)
|
||||||
|
for textIdx = 1:numel(textAnnotations)
|
||||||
|
fprintf(fid, "\\node[font=\\scriptsize, anchor=%s, fill=white, draw=black!40, rounded corners=2pt, inner sep=2pt]\n", ...
|
||||||
|
textAnnotations(textIdx).anchor);
|
||||||
|
fprintf(fid, " at (axis cs:%s,%s) {%s};\n\n", ...
|
||||||
|
formatPgfNumber(textAnnotations(textIdx).x), ...
|
||||||
|
formatPgfNumber(textAnnotations(textIdx).y), ...
|
||||||
|
escapeTikzText(textAnnotations(textIdx).text));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function anchor = matlabTextAlignmentToTikzAnchor(horizontalAlignment, verticalAlignment)
|
||||||
|
horizontalAlignment = string(horizontalAlignment);
|
||||||
|
verticalAlignment = string(verticalAlignment);
|
||||||
|
|
||||||
|
if horizontalAlignment == "left"
|
||||||
|
horizontalAnchor = "west";
|
||||||
|
elseif horizontalAlignment == "right"
|
||||||
|
horizontalAnchor = "east";
|
||||||
|
else
|
||||||
|
horizontalAnchor = "";
|
||||||
|
end
|
||||||
|
|
||||||
|
if verticalAlignment == "top"
|
||||||
|
verticalAnchor = "north";
|
||||||
|
elseif verticalAlignment == "bottom"
|
||||||
|
verticalAnchor = "south";
|
||||||
|
else
|
||||||
|
verticalAnchor = "";
|
||||||
|
end
|
||||||
|
|
||||||
|
if strlength(horizontalAnchor) > 0 && strlength(verticalAnchor) > 0
|
||||||
|
anchor = verticalAnchor + " " + horizontalAnchor;
|
||||||
|
elseif strlength(horizontalAnchor) > 0
|
||||||
|
anchor = horizontalAnchor;
|
||||||
|
elseif strlength(verticalAnchor) > 0
|
||||||
|
anchor = verticalAnchor;
|
||||||
|
else
|
||||||
|
anchor = "center";
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function textOut = escapeTikzText(textIn)
|
||||||
|
textOut = char(textIn);
|
||||||
|
% textOut = strrep(textOut, "\", "\textbackslash{}");
|
||||||
|
textOut = strrep(textOut, "{", "\{");
|
||||||
|
textOut = strrep(textOut, "}", "\}");
|
||||||
|
textOut = strrep(textOut, "%", "\%");
|
||||||
|
textOut = strrep(textOut, "&", "\&");
|
||||||
|
textOut = strrep(textOut, "#", "\#");
|
||||||
|
textOut = strrep(textOut, "_", "\_");
|
||||||
|
% textOut = strrep(textOut, "$", "\$");
|
||||||
|
end
|
||||||
@@ -1,4 +1,4 @@
|
|||||||
function [symbols_for_lvl, avg_for_lvl, info] = showLevelScatter(rxInput, refSymbols, options)
|
function [symbols_for_lvl, avg_for_lvl, xAxisUs, info] = showLevelScatter(rxInput, refSymbols, options)
|
||||||
%SHOWLEVELSCATTER Plot received samples separated by reference PAM level.
|
%SHOWLEVELSCATTER Plot received samples separated by reference PAM level.
|
||||||
% Supports plain numeric vectors, Signal objects, synchronized scope cell
|
% Supports plain numeric vectors, Signal objects, synchronized scope cell
|
||||||
% arrays, and raw unsynchronized Signal input. Raw Signal input is
|
% arrays, and raw unsynchronized Signal input. Raw Signal input is
|
||||||
@@ -290,7 +290,7 @@ for levelIdx = 1:numLevels
|
|||||||
xAxisUs, avg_for_lvl(levelIdx, :), ...
|
xAxisUs, avg_for_lvl(levelIdx, :), ...
|
||||||
options.avgLineMaxPoints, options.avgLineSmoothWindow);
|
options.avgLineMaxPoints, options.avgLineSmoothWindow);
|
||||||
plot(ax, xReduced, yReduced, ...
|
plot(ax, xReduced, yReduced, ...
|
||||||
"LineWidth", 2, ...
|
"LineWidth", 1, ...
|
||||||
"Color", cols(levelIdx, :));
|
"Color", cols(levelIdx, :));
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|||||||
@@ -3,6 +3,9 @@ function output = dsp_runid(run_id, options)
|
|||||||
arguments
|
arguments
|
||||||
run_id
|
run_id
|
||||||
options.append_to_db = 0;
|
options.append_to_db = 0;
|
||||||
|
options.append_mpi_reduction_db (1,1) logical = false;
|
||||||
|
options.mpi_reduction_study_name string = "mpi_reduction_v1";
|
||||||
|
options.mpi_reduction_writer_path string = "";
|
||||||
options.max_occurences = 4;
|
options.max_occurences = 4;
|
||||||
options.start_occurence = 1;
|
options.start_occurence = 1;
|
||||||
options.userParameters = struct();
|
options.userParameters = struct();
|
||||||
@@ -24,7 +27,11 @@ try
|
|||||||
database = [];
|
database = [];
|
||||||
inputSource = normalizeDspInputSource(options.mode);
|
inputSource = normalizeDspInputSource(options.mode);
|
||||||
|
|
||||||
if inputSource == "run_id" || options.append_to_db
|
if options.append_mpi_reduction_db && strlength(options.mpi_reduction_writer_path) > 0
|
||||||
|
addpath(options.mpi_reduction_writer_path);
|
||||||
|
end
|
||||||
|
|
||||||
|
if inputSource == "run_id" || options.append_to_db || options.append_mpi_reduction_db
|
||||||
database = DBHandler("dataBase", [options.dataBase], "type", options.database_type, ...
|
database = DBHandler("dataBase", [options.dataBase], "type", options.database_type, ...
|
||||||
"user", options.user, "password", options.password, ...
|
"user", options.user, "password", options.password, ...
|
||||||
"server", options.server, "port", options.port);
|
"server", options.server, "port", options.port);
|
||||||
@@ -60,6 +67,12 @@ try
|
|||||||
if options.append_to_db
|
if options.append_to_db
|
||||||
appendDspOutputToDatabase(database, run_id, dspOutput);
|
appendDspOutputToDatabase(database, run_id, dspOutput);
|
||||||
end
|
end
|
||||||
|
if options.append_mpi_reduction_db
|
||||||
|
occurrence_idx = options.start_occurence + r - 1;
|
||||||
|
appendMpiReductionDspOutput(database, run_id, occurrence_idx, dspOutput, options, ...
|
||||||
|
"study_name", options.mpi_reduction_study_name, ...
|
||||||
|
"verbose", false);
|
||||||
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
catch ME
|
catch ME
|
||||||
|
|||||||
105
Tests/04_DSP/Equalizer/FFE_plain_test.m
Normal file
105
Tests/04_DSP/Equalizer/FFE_plain_test.m
Normal file
@@ -0,0 +1,105 @@
|
|||||||
|
classdef FFE_plain_test < IMDDTestCase
|
||||||
|
methods (Test, TestTags = {'unit', 'fast', 'dsp', 'ffe'})
|
||||||
|
function constructorKeepsPlainDefaults(testCase)
|
||||||
|
ffe = FFE_plain();
|
||||||
|
|
||||||
|
testCase.verifyEqual(ffe.sps, 2);
|
||||||
|
testCase.verifyEqual(ffe.order, 15);
|
||||||
|
testCase.verifyEqual(ffe.len_tr, 4096);
|
||||||
|
testCase.verifyEqual(ffe.mu_tr, 0);
|
||||||
|
testCase.verifyEqual(ffe.epochs_tr, 5);
|
||||||
|
testCase.verifyEqual(ffe.adaption_technique, adaption_method.lms);
|
||||||
|
testCase.verifyEqual(ffe.dd_mode, 1);
|
||||||
|
testCase.verifyEqual(ffe.mu_dd, 1e-5);
|
||||||
|
testCase.verifyEqual(ffe.epochs_dd, 5);
|
||||||
|
testCase.verifyEqual(ffe.dd_len_fraction, 1);
|
||||||
|
testCase.verifyFalse(ffe.decide);
|
||||||
|
testCase.verifyEqual(ffe.e, zeros(ffe.order,1));
|
||||||
|
testCase.verifyFalse(isprop(ffe,"dc_tracking_mu"));
|
||||||
|
testCase.verifyFalse(isprop(ffe,"dc_avg_bufferlength_a1"));
|
||||||
|
testCase.verifyFalse(isprop(ffe,"dc_level_avg_bufferlength_a2"));
|
||||||
|
end
|
||||||
|
|
||||||
|
function plainMatchesFullCoreForLms(testCase)
|
||||||
|
result = runCoreParity(adaption_method.lms,0.02,0.01);
|
||||||
|
|
||||||
|
verifyCoreParity(testCase,result);
|
||||||
|
end
|
||||||
|
|
||||||
|
function plainMatchesFullCoreForNlms(testCase)
|
||||||
|
result = runCoreParity(adaption_method.nlms,0.08,0.03);
|
||||||
|
|
||||||
|
verifyCoreParity(testCase,result);
|
||||||
|
end
|
||||||
|
|
||||||
|
function plainMatchesFullCoreForRls(testCase)
|
||||||
|
result = runCoreParity(adaption_method.rls,0.995,0.998);
|
||||||
|
|
||||||
|
verifyCoreParity(testCase,result);
|
||||||
|
end
|
||||||
|
|
||||||
|
function optimizationSignalsUsesShortenedAlignedPrefix(testCase)
|
||||||
|
ffe = FFE_plain( ...
|
||||||
|
"sps", 2, ...
|
||||||
|
"len_tr", 16, ...
|
||||||
|
"mu_optimization_len", 22);
|
||||||
|
x = (1:100).';
|
||||||
|
d = (1:50).';
|
||||||
|
|
||||||
|
[x_opt,d_opt,N_opt] = ffe.optimizationSignals(x,d);
|
||||||
|
|
||||||
|
testCase.verifyEqual(N_opt, 22);
|
||||||
|
testCase.verifyEqual(x_opt, x(1:22));
|
||||||
|
testCase.verifyEqual(d_opt, d(1:11));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function result = runCoreParity(adaption,mu_tr,mu_dd)
|
||||||
|
x = [ ...
|
||||||
|
-2.2; -0.9; 0.8; 2.1; ...
|
||||||
|
-2.0; -1.1; 1.1; 2.2; ...
|
||||||
|
-2.1; -0.8; 0.9; 2.0; ...
|
||||||
|
-2.3; -1.0; 1.0; 2.3];
|
||||||
|
d = [ ...
|
||||||
|
-3; -1; 1; 3; ...
|
||||||
|
-3; -1; 1; 3; ...
|
||||||
|
-3; -1; 1; 3; ...
|
||||||
|
-3; -1; 1; 3];
|
||||||
|
settings = { ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 3, ...
|
||||||
|
"len_tr", numel(x), ...
|
||||||
|
"epochs_tr", 2, ...
|
||||||
|
"mu_tr", mu_tr, ...
|
||||||
|
"dd_mode", 1, ...
|
||||||
|
"epochs_dd", 2, ...
|
||||||
|
"mu_dd", mu_dd, ...
|
||||||
|
"adaption_technique", adaption};
|
||||||
|
|
||||||
|
plain = FFE_plain(settings{:});
|
||||||
|
full = FFE(settings{:});
|
||||||
|
plain.constellation = unique(d);
|
||||||
|
full.constellation = unique(d);
|
||||||
|
plain.P = (1/0.05) * eye(plain.order);
|
||||||
|
full.P = (1/0.05) * eye(full.order);
|
||||||
|
|
||||||
|
plain.equalize(x,d,plain.mu_tr,plain.epochs_tr,plain.len_tr,true,false);
|
||||||
|
full.equalize(x,d,full.mu_tr,full.epochs_tr,full.len_tr,true,false);
|
||||||
|
plain.e_tr = plain.e;
|
||||||
|
full.e_tr = full.e;
|
||||||
|
|
||||||
|
[result.y_plain,result.dhat_plain] = plain.equalize(x,d,plain.mu_dd,plain.epochs_dd,numel(x),false,false);
|
||||||
|
[result.y_full,result.dhat_full] = full.equalize(x,d,full.mu_dd,full.epochs_dd,numel(x),false,false);
|
||||||
|
result.e_plain = plain.e;
|
||||||
|
result.e_full = full.e;
|
||||||
|
result.e_tr_plain = plain.e_tr;
|
||||||
|
result.e_tr_full = full.e_tr;
|
||||||
|
end
|
||||||
|
|
||||||
|
function verifyCoreParity(testCase,result)
|
||||||
|
testCase.verifyEqual(result.y_plain,result.y_full,"AbsTol",1e-12);
|
||||||
|
testCase.verifyEqual(result.dhat_plain,result.dhat_full,"AbsTol",1e-12);
|
||||||
|
testCase.verifyEqual(result.e_plain,result.e_full,"AbsTol",1e-12);
|
||||||
|
testCase.verifyEqual(result.e_tr_plain,result.e_tr_full,"AbsTol",1e-12);
|
||||||
|
end
|
||||||
@@ -12,6 +12,23 @@ classdef FFE_test < IMDDTestCase
|
|||||||
testCase.verifyEqual(ffe.dd_mode, 1);
|
testCase.verifyEqual(ffe.dd_mode, 1);
|
||||||
testCase.verifyEqual(ffe.mu_dd, 1e-5);
|
testCase.verifyEqual(ffe.mu_dd, 1e-5);
|
||||||
testCase.verifyEqual(ffe.epochs_dd, 5);
|
testCase.verifyEqual(ffe.epochs_dd, 5);
|
||||||
|
testCase.verifyFalse(logical(ffe.optimize_dc_tracking_params));
|
||||||
|
testCase.verifyEqual(ffe.dc_tracking_optimization_delay_weight, 1e-3);
|
||||||
|
testCase.verifyEqual(ffe.dc_tracking_persistence_gain, 0);
|
||||||
|
testCase.verifyEqual(ffe.dc_tracking_power_exponent, 2);
|
||||||
|
testCase.verifyEqual(ffe.dc_avg_bufferlength_a1, 0);
|
||||||
|
testCase.verifyEqual(ffe.dc_avg_update_blocklength_a1, 0);
|
||||||
|
testCase.verifyEqual(ffe.dc_smoothing_a1, 0);
|
||||||
|
testCase.verifyEqual(ffe.dc_level_avg_bufferlength_a2, 0);
|
||||||
|
testCase.verifyEqual(ffe.dc_level_update_blocklength_a2, 0);
|
||||||
|
testCase.verifyEqual(ffe.dc_smoothing_a2, 0);
|
||||||
|
testCase.verifyEqual(ffe.dc_level_decision_mode_a2, "residual");
|
||||||
|
testCase.verifyEqual(ffe.dc_level_weights_a2, 0);
|
||||||
|
testCase.verifyEqual(ffe.dc_tracking_buffer_len, 1);
|
||||||
|
testCase.verifyEqual(ffe.ffe_update_buffer_len, 1);
|
||||||
|
testCase.verifyFalse(logical(ffe.optimize_a2_level_weights));
|
||||||
|
testCase.verifyEqual(ffe.a2_level_weight_optimization_max_evals, 30);
|
||||||
|
testCase.verifyEqual(ffe.a2_level_weight_max, 1);
|
||||||
testCase.verifyFalse(ffe.decide);
|
testCase.verifyFalse(ffe.decide);
|
||||||
testCase.verifyEqual(ffe.e, zeros(ffe.order, 1));
|
testCase.verifyEqual(ffe.e, zeros(ffe.order, 1));
|
||||||
testCase.verifyEqual(ffe.error, 0);
|
testCase.verifyEqual(ffe.error, 0);
|
||||||
@@ -69,6 +86,468 @@ classdef FFE_test < IMDDTestCase
|
|||||||
testCase.verifyNotEqual(y.signal, zeros(size(x.signal)));
|
testCase.verifyNotEqual(y.signal, zeros(size(x.signal)));
|
||||||
testCase.verifyEqual(length(y.signal), length(x.signal));
|
testCase.verifyEqual(length(y.signal), length(x.signal));
|
||||||
end
|
end
|
||||||
|
|
||||||
|
function dcTrackingBufferDelaysDcUpdateUntilBlockBoundary(testCase)
|
||||||
|
x = zeros(4, 1);
|
||||||
|
d = ones(4, 1);
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_tracking_mu", 0.1, ...
|
||||||
|
"dc_tracking_buffer_len", 2, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
|
||||||
|
ffe.constellation = unique(d);
|
||||||
|
ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(ffe.e_dc, 0.19, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcTrackingUnbufferedPathUpdatesEverySymbol(testCase)
|
||||||
|
x = zeros(4, 1);
|
||||||
|
d = ones(4, 1);
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_tracking_mu", 0.1, ...
|
||||||
|
"dc_tracking_buffer_len", 1, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
|
||||||
|
ffe.constellation = unique(d);
|
||||||
|
ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(ffe.e_dc, 1 - 0.9^4, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcTrackingBlockUpdateUsesMeanBlockError(testCase)
|
||||||
|
ffe = FFE("dc_tracking_mu", 0.1);
|
||||||
|
|
||||||
|
[e_dc_next,stats] = ffe.dcTrackingBlockUpdate(0.5, [1; 2; 3]);
|
||||||
|
|
||||||
|
testCase.verifyEqual(e_dc_next, 0.7, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(stats.err_mean, 2, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(stats.mu_eff, 0.1, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(stats.update, 0.2, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcTrackingBlockUpdateDoesNotSuppressLargeErrorPower(testCase)
|
||||||
|
ffe = FFE("dc_tracking_mu", 0.1);
|
||||||
|
|
||||||
|
[e_dc_next,stats] = ffe.dcTrackingBlockUpdate(0, [10; 10]);
|
||||||
|
|
||||||
|
testCase.verifyEqual(e_dc_next, 1, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(stats.err_mean, 10, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(stats.mu_eff, 0.1, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcTrackingBlockUpdateCanBoostPersistentMean(testCase)
|
||||||
|
ffe = FFE("dc_tracking_mu", 0.1);
|
||||||
|
|
||||||
|
[e_dc_next,stats] = ffe.dcTrackingBlockUpdate(0, [2; 2; 2], ...
|
||||||
|
"persistence_gain", 1);
|
||||||
|
|
||||||
|
testCase.verifyEqual(e_dc_next, 0.4, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(stats.persistence_scale, 1, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(stats.mu_eff, 0.2, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcTrackingLoopUsesBlockPersistenceGain(testCase)
|
||||||
|
x = zeros(4, 1);
|
||||||
|
d = ones(4, 1);
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_tracking_mu", 0.1, ...
|
||||||
|
"dc_tracking_adaptive_enabled", true, ...
|
||||||
|
"dc_tracking_persistence_gain", 1, ...
|
||||||
|
"dc_tracking_buffer_len", 2, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
|
||||||
|
ffe.constellation = unique(d);
|
||||||
|
ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(ffe.e_dc, 0.36, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function ffeDcTrackingClassMatchesFullUnbufferedPath(testCase)
|
||||||
|
x = zeros(4, 1);
|
||||||
|
d = ones(4, 1);
|
||||||
|
settings = { ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_tracking_mu", 0.1, ...
|
||||||
|
"dc_tracking_buffer_len", 1, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms};
|
||||||
|
|
||||||
|
full = FFE(settings{:});
|
||||||
|
tracking = FFE_DCTracking(settings{:});
|
||||||
|
full.constellation = unique(d);
|
||||||
|
tracking.constellation = unique(d);
|
||||||
|
|
||||||
|
[y_full,d_hat_full] = full.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
[y_tracking,d_hat_tracking] = tracking.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y_tracking, y_full, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(d_hat_tracking, d_hat_full, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(tracking.e_dc, full.e_dc, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(tracking.debug_struct.dc_tracking_est, ...
|
||||||
|
full.debug_struct.dc_tracking_est, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function ffeDcTrackingClassMatchesFullBufferedPersistencePath(testCase)
|
||||||
|
x = zeros(4, 1);
|
||||||
|
d = ones(4, 1);
|
||||||
|
settings = { ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_tracking_mu", 0.1, ...
|
||||||
|
"dc_tracking_adaptive_enabled", true, ...
|
||||||
|
"dc_tracking_persistence_gain", 1, ...
|
||||||
|
"dc_tracking_buffer_len", 2, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms};
|
||||||
|
|
||||||
|
full = FFE(settings{:});
|
||||||
|
tracking = FFE_DCTracking(settings{:});
|
||||||
|
full.constellation = unique(d);
|
||||||
|
tracking.constellation = unique(d);
|
||||||
|
|
||||||
|
[y_full,d_hat_full] = full.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
[y_tracking,d_hat_tracking] = tracking.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y_tracking, y_full, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(d_hat_tracking, d_hat_full, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(tracking.e_dc, full.e_dc, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(tracking.debug_struct.dc_tracking_mu_eff, ...
|
||||||
|
full.debug_struct.dc_tracking_mu_eff, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(tracking.debug_struct.dc_tracking_est, ...
|
||||||
|
full.debug_struct.dc_tracking_est, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function ffeBufferDelaysTapUpdateUntilBlockBoundary(testCase)
|
||||||
|
x = ones(4, 1);
|
||||||
|
d = ones(4, 1);
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_tracking_mu", 0, ...
|
||||||
|
"ffe_update_buffer_len", 2, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
|
||||||
|
ffe.constellation = unique(d);
|
||||||
|
ffe.equalize(x, d, 0.1, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(ffe.e, 0.19, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcAvgA1BufferlengthOneLeavesInputUnchanged(testCase)
|
||||||
|
x = (1:4).';
|
||||||
|
d = zeros(4, 1);
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_avg_bufferlength_a1", 1, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
|
||||||
|
[y,~] = ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, x, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_avg_offset, zeros(1,4), "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcAvgA1SubtractsCausalMovingAverageByDefault(testCase)
|
||||||
|
x = (1:4).';
|
||||||
|
d = zeros(4, 1);
|
||||||
|
expected_offset = [0, 0, 0, 2];
|
||||||
|
expected_y = [1; 2; 3; 2];
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_avg_bufferlength_a1", 3, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
|
||||||
|
[y,~] = ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, expected_y, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_avg_offset, expected_offset, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcAvgA1SmoothingIsReservedForOfflineVariants(testCase)
|
||||||
|
x = (1:4).';
|
||||||
|
d = zeros(4, 1);
|
||||||
|
expected_offset = [0, 0, 0, 2];
|
||||||
|
expected_y = [1; 2; 3; 2];
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_avg_bufferlength_a1", 3, ...
|
||||||
|
"dc_smoothing_a1", 0.5, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
|
||||||
|
[y,~] = ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, expected_y, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_avg_offset, expected_offset, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcAvgA1UpdateBlocklengthOneRefreshesEverySymbol(testCase)
|
||||||
|
x = (1:4).';
|
||||||
|
d = zeros(4, 1);
|
||||||
|
expected_offset = [0, 1, 1.5, 2];
|
||||||
|
expected_y = [1; 1; 1.5; 2];
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_avg_bufferlength_a1", 3, ...
|
||||||
|
"dc_avg_update_blocklength_a1", 1, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
|
||||||
|
[y,~] = ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, expected_y, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_avg_offset, expected_offset, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcAvgA1WarnsWhenCombinedWithDcTracking(testCase)
|
||||||
|
testCase.verifyWarning(@() FFE( ...
|
||||||
|
"dc_avg_bufferlength_a1", 3, ...
|
||||||
|
"dc_tracking_mu", 0.1), "FFE:DCAvgWithDcTracking");
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcLevelAvgA2SubtractsScalarWeightedResidual(testCase)
|
||||||
|
x = (1:4).';
|
||||||
|
d = zeros(4, 1);
|
||||||
|
expected_y = [1; 2; 1.5; 2.5];
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 2, ...
|
||||||
|
"dc_level_weights_a2", 1, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
ffe.constellation = 0;
|
||||||
|
|
||||||
|
[y,~] = ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, expected_y, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_mpi_est, [0, 0, 1.5, 1.5], "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_weight, [0, 0, 1, 1], "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_valid_count, [0, 0, 2, 2]);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcLevelAvgA2UpdateBlocklengthOneRefreshesEverySymbol(testCase)
|
||||||
|
x = (1:4).';
|
||||||
|
d = zeros(4, 1);
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 2, ...
|
||||||
|
"dc_level_update_blocklength_a2", 1, ...
|
||||||
|
"dc_level_weights_a2", 1, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
ffe.constellation = 0;
|
||||||
|
|
||||||
|
[y,~] = ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, [1; 1.5; 1.5; 1.5], "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_mpi_est, [0, 1, 1.5, 2.5], "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_weight, [0, 0.5, 1, 1], "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_valid_count, [0, 1, 2, 2]);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcLevelAvgA2UsesVectorWeightsInConstellationOrder(testCase)
|
||||||
|
x = [1; 3; 1; 4];
|
||||||
|
d = [0; 2; 0; 2];
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 2, ...
|
||||||
|
"dc_level_weights_a2", [0, 1], ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
ffe.constellation = [0; 2];
|
||||||
|
|
||||||
|
[y,~] = ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, [1; 3; 1; 3.5], "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcLevelAvgA2TrackedLevelsUseAdaptiveDecision(testCase)
|
||||||
|
x = [0.5; 2.5; 0.5; 2.5; 1.3];
|
||||||
|
d = [0; 2; 0; 2; 0];
|
||||||
|
|
||||||
|
ffe = FFE( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 2, ...
|
||||||
|
"dc_level_update_blocklength_a2", 1, ...
|
||||||
|
"dc_level_decision_mode_a2", "tracked_levels", ...
|
||||||
|
"dc_level_weights_a2", 1, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
ffe.constellation = [0; 2];
|
||||||
|
|
||||||
|
[y,d_hat] = ffe.equalize(x, d, 0, 1, numel(x), false, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, x, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(d_hat, d, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual( ...
|
||||||
|
ffe.debug_struct.dc_level_decision_level, ...
|
||||||
|
[0, 2, 0.25, 2.25, 0.5], ...
|
||||||
|
"AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function ffeA2ResidualClassSubtractsScalarWeightedResidual(testCase)
|
||||||
|
x = (1:4).';
|
||||||
|
d = zeros(4, 1);
|
||||||
|
|
||||||
|
ffe = FFE_A2Residual( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 2, ...
|
||||||
|
"dc_level_weights_a2", 1, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
ffe.constellation = 0;
|
||||||
|
|
||||||
|
[y,~] = ffe.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, [1; 2; 1.5; 2.5], "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_mpi_est, [0, 0, 1.5, 1.5], "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_weight, [0, 0, 1, 1], "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(ffe.debug_struct.dc_level_valid_count, [0, 0, 2, 2]);
|
||||||
|
end
|
||||||
|
|
||||||
|
function ffeA2ResidualClassMatchesFullResidualPath(testCase)
|
||||||
|
x = [1; 3; 1; 4; 2; 5];
|
||||||
|
d = [0; 2; 0; 2; 0; 2];
|
||||||
|
settings = { ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 2, ...
|
||||||
|
"dc_level_update_blocklength_a2", 1, ...
|
||||||
|
"dc_level_weights_a2", [0.25, 0.75], ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms};
|
||||||
|
|
||||||
|
full = FFE(settings{:});
|
||||||
|
residual = FFE_A2Residual(settings{:});
|
||||||
|
full.e = 1;
|
||||||
|
residual.e = 1;
|
||||||
|
full.constellation = [0; 2];
|
||||||
|
residual.constellation = [0; 2];
|
||||||
|
|
||||||
|
[y_full,d_hat_full] = full.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
[y_residual,d_hat_residual] = residual.equalize(x, d, 0, 1, numel(x), true, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y_residual, y_full, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(d_hat_residual, d_hat_full, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(residual.debug_struct.dc_level_mpi_est, ...
|
||||||
|
full.debug_struct.dc_level_mpi_est, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(residual.debug_struct.dc_level_weight, ...
|
||||||
|
full.debug_struct.dc_level_weight, "AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcLevelAvgA2RejectsUnknownDecisionMode(testCase)
|
||||||
|
testCase.verifyError(@() FFE("dc_level_decision_mode_a2", "unknown"), ...
|
||||||
|
"FFE:InvalidA2DecisionMode");
|
||||||
|
end
|
||||||
|
|
||||||
|
function ffeA2TrackedLevelsClassUsesAdaptiveDecision(testCase)
|
||||||
|
x = [0.5; 2.5; 0.5; 2.5; 1.3];
|
||||||
|
d = [0; 2; 0; 2; 0];
|
||||||
|
|
||||||
|
ffe = FFE_A2TrackedLevels( ...
|
||||||
|
"sps", 1, ...
|
||||||
|
"order", 1, ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 2, ...
|
||||||
|
"dc_level_update_blocklength_a2", 1, ...
|
||||||
|
"dc_level_weights_a2", 1, ...
|
||||||
|
"save_debug", true, ...
|
||||||
|
"dd_mode", 0, ...
|
||||||
|
"adaption_technique", adaption_method.lms);
|
||||||
|
ffe.e = 1;
|
||||||
|
ffe.constellation = [0; 2];
|
||||||
|
|
||||||
|
[y,d_hat] = ffe.equalize(x, d, 0, 1, numel(x), false, false);
|
||||||
|
|
||||||
|
testCase.verifyEqual(y, x, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual(d_hat, d, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyEqual( ...
|
||||||
|
ffe.debug_struct.dc_level_decision_level, ...
|
||||||
|
[0, 2, 0.25, 2.25, 0.5], ...
|
||||||
|
"AbsTol", 1e-12);
|
||||||
|
end
|
||||||
|
|
||||||
|
function dcLevelAvgA2WarnsWhenCombinedWithDcTracking(testCase)
|
||||||
|
testCase.verifyWarning(@() FFE( ...
|
||||||
|
"dc_level_avg_bufferlength_a2", 2, ...
|
||||||
|
"dc_level_weights_a2", 1, ...
|
||||||
|
"dc_tracking_mu", 0.1), "FFE:DCLevelAvgWithOtherDcSuppression");
|
||||||
|
end
|
||||||
|
|
||||||
|
function a2InitialWeightGuessFollowsLevelVarianceSlope(testCase)
|
||||||
|
x = [ ...
|
||||||
|
0.98; 1.02; 1.00; 1.01; ...
|
||||||
|
1.90; 2.10; 2.00; 2.08; ...
|
||||||
|
2.50; 3.50; 3.00; 3.40];
|
||||||
|
d = [ ...
|
||||||
|
ones(4,1); ...
|
||||||
|
2*ones(4,1); ...
|
||||||
|
3*ones(4,1)];
|
||||||
|
|
||||||
|
ffe = FFE("sps", 1, "a2_level_weight_max", 1);
|
||||||
|
ffe.constellation = [1; 2; 3];
|
||||||
|
|
||||||
|
[weights,stats] = ffe.a2LevelWeightInitialGuess(x,d);
|
||||||
|
|
||||||
|
testCase.verifySize(weights, [3, 1]);
|
||||||
|
testCase.verifyGreaterThan(weights(3), weights(2));
|
||||||
|
testCase.verifyGreaterThan(weights(2), weights(1));
|
||||||
|
testCase.verifyEqual(max(weights), 0.7, "AbsTol", 1e-12);
|
||||||
|
testCase.verifyGreaterThan(stats.variance_slope, 0);
|
||||||
|
end
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
|
|
||||||
|
|||||||
BIN
before_eq_-1.png
Normal file
BIN
before_eq_-1.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 250 KiB |
243
projects/Diss/MPI_revisit/algorithms/PLOT_ber_vs_sir.m
Normal file
243
projects/Diss/MPI_revisit/algorithms/PLOT_ber_vs_sir.m
Normal file
@@ -0,0 +1,243 @@
|
|||||||
|
%% Simple BER over SIR plot from the recombined warehouses
|
||||||
|
% This script intentionally keeps the mechanics visible:
|
||||||
|
% 1) load the run_id warehouse and the grouped config warehouse
|
||||||
|
% 2) loop over occupied config cells
|
||||||
|
% 3) read the physical coordinates of the cell
|
||||||
|
% 4) extract all BER values stored in that cell
|
||||||
|
% 5) plot individual BER dots and one larger mean marker per SIR
|
||||||
|
|
||||||
|
clear; clc;
|
||||||
|
|
||||||
|
scriptDir = fileparts("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\MPI_revisit\algorithms\");
|
||||||
|
|
||||||
|
|
||||||
|
runWhFile = fullfile(scriptDir, "combined_by_run_id_results.mat");
|
||||||
|
configWhFile = fullfile(scriptDir, "combined_by_config_results.mat");
|
||||||
|
|
||||||
|
runWhData = load(runWhFile);
|
||||||
|
configWhData = load(configWhFile);
|
||||||
|
|
||||||
|
wh_run_id_combined = runWhData.wh_run_id_combined;
|
||||||
|
wh_config_combined = configWhData.wh_config_combined;
|
||||||
|
algorithmStorageNames = configWhData.algorithmStorageNames;
|
||||||
|
|
||||||
|
fprintf("Loaded run_id warehouse:\n %s\n", runWhFile);
|
||||||
|
wh_run_id_combined.showInfo;
|
||||||
|
|
||||||
|
fprintf("\nLoaded config-grouped warehouse:\n %s\n", configWhFile);
|
||||||
|
wh_config_combined.showInfo;
|
||||||
|
|
||||||
|
%% Plot settings
|
||||||
|
|
||||||
|
pathLengthToPlot = 1000; % use 300 to see duplicate run_ids per config; set 1000 for the previous path
|
||||||
|
selectedPamLevels = 8; %wh_config_combined.parameter.pam_level.values;
|
||||||
|
selectedAlgorithms = algorithmStorageNames([1,2,3,5]);
|
||||||
|
|
||||||
|
useBoundedLines = true; % switch uncertainty bands on/off here
|
||||||
|
usePolyfit = true; % switch fitted dashed trend lines on/off here
|
||||||
|
polyfitOrderMax = 4;
|
||||||
|
|
||||||
|
|
||||||
|
% SIR vs BER: one subplot per PAM level, all algorithms compared
|
||||||
|
|
||||||
|
if exist("linspecer", "file")
|
||||||
|
algColors = linspecer(numel(selectedAlgorithms));
|
||||||
|
else
|
||||||
|
algColors = lines(numel(selectedAlgorithms));
|
||||||
|
end
|
||||||
|
|
||||||
|
figure(); clf;
|
||||||
|
tiledlayout(numel(selectedPamLevels), 1, "TileSpacing", "compact");
|
||||||
|
|
||||||
|
for pamIdx = 1:numel(selectedPamLevels)
|
||||||
|
pamLevel = selectedPamLevels(pamIdx);
|
||||||
|
nexttile; hold on;
|
||||||
|
|
||||||
|
for algIdx = 1:numel(selectedAlgorithms)
|
||||||
|
algorithmName = selectedAlgorithms{algIdx};
|
||||||
|
algColor = algColors(algIdx, :);
|
||||||
|
|
||||||
|
berTable = buildBerTable(wh_config_combined, algorithmName, pathLengthToPlot);
|
||||||
|
berTable = berTable(berTable.pam_level == pamLevel, :);
|
||||||
|
|
||||||
|
% berTable(berTable.sir == 23,:) = [];
|
||||||
|
|
||||||
|
if isempty(berTable)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
sirValues = unique(berTable.sir(:).');
|
||||||
|
sirValues = sort(sirValues);
|
||||||
|
|
||||||
|
meanBer = nan(size(sirValues));
|
||||||
|
minBer = nan(size(sirValues));
|
||||||
|
maxBer = nan(size(sirValues));
|
||||||
|
|
||||||
|
for sirIdx = 1:numel(sirValues)
|
||||||
|
sirValue = sirValues(sirIdx);
|
||||||
|
sirRows = berTable(berTable.sir == sirValue, :);
|
||||||
|
berValues = [sirRows.ber_values{:}];
|
||||||
|
berValues = berValues(isfinite(berValues));
|
||||||
|
berValues = berValues(berValues<0.1);
|
||||||
|
berValues = rmoutliers(berValues);
|
||||||
|
|
||||||
|
|
||||||
|
if isempty(berValues)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
scatter(repmat(sirValue, size(berValues)), berValues, ...
|
||||||
|
30, ...
|
||||||
|
"Marker", ".", ...
|
||||||
|
"MarkerEdgeColor", algColor, ...
|
||||||
|
"MarkerFaceColor", algColor, ...
|
||||||
|
"HandleVisibility", "off");
|
||||||
|
|
||||||
|
meanBer(sirIdx) = mean(berValues, "omitnan");
|
||||||
|
minBer(sirIdx) = min(berValues);
|
||||||
|
maxBer(sirIdx) = max(berValues);
|
||||||
|
end
|
||||||
|
|
||||||
|
valid = isfinite(sirValues) & isfinite(meanBer);
|
||||||
|
if ~any(valid)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
if useBoundedLines && exist("boundedline", "file")
|
||||||
|
yLower = max(meanBer - minBer, 0);
|
||||||
|
yUpper = max(maxBer - meanBer, 0);
|
||||||
|
yBounds = [yLower(:), yUpper(:)];
|
||||||
|
|
||||||
|
[hl, hp] = boundedline(sirValues(valid).', meanBer(valid).', yBounds(valid, :), ...
|
||||||
|
'alpha', 'transparency', 0.08, ...
|
||||||
|
'cmap', algColor, ...
|
||||||
|
'nan', 'fill', ...
|
||||||
|
'orientation', 'vert');
|
||||||
|
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
|
||||||
|
set(hp, "LineStyle", "none", "HandleVisibility", "off");
|
||||||
|
end
|
||||||
|
|
||||||
|
scatter(sirValues(valid), meanBer(valid), ...
|
||||||
|
20, ...
|
||||||
|
"Marker", "o", ...
|
||||||
|
"MarkerEdgeColor", algColor, ...
|
||||||
|
"MarkerFaceColor", algColor, ...
|
||||||
|
"LineWidth", 1, ...
|
||||||
|
"DisplayName", algorithmName);
|
||||||
|
|
||||||
|
if usePolyfit
|
||||||
|
fitMask = valid & meanBer > 0;
|
||||||
|
if nnz(fitMask) >= 2
|
||||||
|
fitOrder = min(polyfitOrderMax, nnz(fitMask) - 1);
|
||||||
|
fitCoeff = polyfit(sirValues(fitMask), log10(meanBer(fitMask)), fitOrder);
|
||||||
|
xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
|
||||||
|
yFit = 10 .^ polyval(fitCoeff, xFit);
|
||||||
|
|
||||||
|
plot(xFit, yFit, ...
|
||||||
|
"LineStyle", "--", ...
|
||||||
|
"LineWidth", 1.1, ...
|
||||||
|
"Color", algColor, ...
|
||||||
|
"HandleVisibility", "off");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
yline(2.2e-4, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||||
|
yline(3.8e-3, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||||
|
yline(2e-2, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||||
|
ylim([9e-5, 0.1]);
|
||||||
|
|
||||||
|
title(sprintf("PAM %.0f, path %.0f m", pamLevel, pathLengthToPlot));
|
||||||
|
xlabel("SIR (dB)");
|
||||||
|
ylabel("BER");
|
||||||
|
set(gca, "YScale", "log");
|
||||||
|
xlim([15,45]);
|
||||||
|
grid on;
|
||||||
|
box on;
|
||||||
|
legend("Location", "best", "Interpreter", "none");
|
||||||
|
end
|
||||||
|
|
||||||
|
%% Local helpers
|
||||||
|
|
||||||
|
function berTable = buildBerTable(wh, storageName, pathLength)
|
||||||
|
rows = struct( ...
|
||||||
|
"pam_level", {}, ...
|
||||||
|
"symbolrate", {}, ...
|
||||||
|
"interference_path_length", {}, ...
|
||||||
|
"sir", {}, ...
|
||||||
|
"block_update", {}, ...
|
||||||
|
"run_ids", {}, ...
|
||||||
|
"n_run_ids", {}, ...
|
||||||
|
"ber_values", {}, ...
|
||||||
|
"n_ber_values", {});
|
||||||
|
|
||||||
|
for linIdx = 1:numel(wh.sto.(storageName))
|
||||||
|
storedValue = wh.sto.(storageName){linIdx};
|
||||||
|
if isempty(storedValue)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
phys = linearIndexToStruct(wh, linIdx);
|
||||||
|
if phys.interference_path_length ~= pathLength
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
berValues = extractBerValues(storedValue);
|
||||||
|
if isempty(berValues)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
runIds = wh.sto.run_id{linIdx};
|
||||||
|
if isempty(runIds)
|
||||||
|
runIds = NaN;
|
||||||
|
end
|
||||||
|
|
||||||
|
row = struct();
|
||||||
|
row.pam_level = phys.pam_level;
|
||||||
|
row.symbolrate = phys.symbolrate;
|
||||||
|
row.interference_path_length = phys.interference_path_length;
|
||||||
|
row.sir = phys.sir;
|
||||||
|
row.block_update = phys.block_update;
|
||||||
|
row.run_ids = {runIds};
|
||||||
|
row.n_run_ids = numel(runIds);
|
||||||
|
row.ber_values = {berValues};
|
||||||
|
row.n_ber_values = numel(berValues);
|
||||||
|
|
||||||
|
rows(end+1) = row; %#ok<AGROW>
|
||||||
|
end
|
||||||
|
|
||||||
|
if isempty(rows)
|
||||||
|
berTable = struct2table(rows);
|
||||||
|
else
|
||||||
|
berTable = struct2table(rows);
|
||||||
|
berTable = sortrows(berTable, ["pam_level", "sir", "symbolrate"]);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function physStruct = linearIndexToStruct(wh, linIdx)
|
||||||
|
[physValues, physNames] = wh.getPhysIndicesByLinIndex(linIdx);
|
||||||
|
physStruct = struct();
|
||||||
|
|
||||||
|
for paramIdx = 1:numel(physNames)
|
||||||
|
physStruct.(char(physNames{paramIdx})) = physValues{paramIdx};
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function berValues = extractBerValues(value)
|
||||||
|
berValues = [];
|
||||||
|
|
||||||
|
if isstruct(value)
|
||||||
|
if isfield(value, "metrics") && hasBer(value.metrics)
|
||||||
|
berValues(end+1) = value.metrics.BER;
|
||||||
|
end
|
||||||
|
elseif iscell(value)
|
||||||
|
for valueIdx = 1:numel(value)
|
||||||
|
berValues = [berValues, extractBerValues(value{valueIdx})]; %#ok<AGROW>
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function tf = hasBer(metrics)
|
||||||
|
tf = (isstruct(metrics) && isfield(metrics, "BER")) || ...
|
||||||
|
(isobject(metrics) && isprop(metrics, "BER"));
|
||||||
|
end
|
||||||
@@ -0,0 +1,254 @@
|
|||||||
|
%% BER over SIR from MpiReductionResults
|
||||||
|
% 1) gather data from the database
|
||||||
|
% 2) clean data and remove per-curve outliers
|
||||||
|
% 3) plot BER over SIR grouped by algorithm
|
||||||
|
|
||||||
|
clear; clc;
|
||||||
|
|
||||||
|
%% 1) Gather data
|
||||||
|
|
||||||
|
studyName = "pam4_112_greater_3153";
|
||||||
|
pathLengthToPlot = 0;
|
||||||
|
selectedBlockUpdate = 1; % set [] to pool all block_update values
|
||||||
|
selectedPamLevels = []; % set [] to use all PAM levels in the query result
|
||||||
|
selectedAlgorithms = []; % set [] to use all algorithms in the query result
|
||||||
|
|
||||||
|
useBoundedLines = true;
|
||||||
|
usePolyfit = true;
|
||||||
|
polyfitOrderMax = 4;
|
||||||
|
maxBerForPlot = 0.1;
|
||||||
|
|
||||||
|
db = DBHandler( ...
|
||||||
|
"dataBase", "labor", ...
|
||||||
|
"type", "mysql", ...
|
||||||
|
"server", "192.168.178.192", ...
|
||||||
|
"user", "silas", ...
|
||||||
|
"password", "silas");
|
||||||
|
db.refresh();
|
||||||
|
|
||||||
|
fp = QueryFilter();
|
||||||
|
fp.where('Runs', 'interference_path_length', 'EQUALS', pathLengthToPlot);
|
||||||
|
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||||
|
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||||
|
fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
|
||||||
|
if ~isempty(selectedBlockUpdate)
|
||||||
|
fp.where('MpiReductionResults', 'block_update', 'EQUALS', selectedBlockUpdate);
|
||||||
|
end
|
||||||
|
|
||||||
|
selectedFields = { ...
|
||||||
|
'Runs.run_id', ...
|
||||||
|
'Runs.sir', ...
|
||||||
|
'Runs.pam_level', ...
|
||||||
|
'Runs.symbolrate', ...
|
||||||
|
'Runs.interference_path_length', ...
|
||||||
|
'Runs.power_mpi_interference', ...
|
||||||
|
'MpiReductionResults.occurrence_idx', ...
|
||||||
|
'MpiReductionResults.storage_name', ...
|
||||||
|
'MpiReductionResults.algorithm', ...
|
||||||
|
'MpiReductionResults.algorithm_variant', ...
|
||||||
|
'MpiReductionResults.eq_class', ...
|
||||||
|
'MpiReductionResults.block_update', ...
|
||||||
|
'MpiReductionResults.BER', ...
|
||||||
|
'MpiReductionResults.BER_precoded', ...
|
||||||
|
'MpiReductionResults.SNR', ...
|
||||||
|
'MpiReductionResults.GMI', ...
|
||||||
|
'MpiReductionResults.AIR'};
|
||||||
|
selectedFields = selectedFields(:);
|
||||||
|
|
||||||
|
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||||
|
disp(query);
|
||||||
|
fprintf("Fetched %d MPI reduction rows.\n", height(rawData));
|
||||||
|
|
||||||
|
%% 2) Clean data
|
||||||
|
|
||||||
|
data = rawData;
|
||||||
|
numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
|
||||||
|
"interference_path_length", "occurrence_idx", "block_update", ...
|
||||||
|
"power_mpi_interference", "BER", "BER_precoded", "SNR", "GMI", "AIR"];
|
||||||
|
for fieldIdx = 1:numel(numericFields)
|
||||||
|
fieldName = numericFields(fieldIdx);
|
||||||
|
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||||
|
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
|
||||||
|
for fieldIdx = 1:numel(stringFields)
|
||||||
|
fieldName = stringFields(fieldIdx);
|
||||||
|
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||||
|
data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
|
||||||
|
data.sir_exact = -7 - data.power_mpi_interference;
|
||||||
|
if isempty(data)
|
||||||
|
warning("PLOT_mpi_reduction_db_ber_vs_sir:NoRows", ...
|
||||||
|
"No rows remain after initial BER/path/study/block filtering.");
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if isempty(selectedPamLevels)
|
||||||
|
selectedPamLevels = unique(data.pam_level(isfinite(data.pam_level))).';
|
||||||
|
else
|
||||||
|
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||||
|
end
|
||||||
|
|
||||||
|
if isempty(selectedAlgorithms)
|
||||||
|
selectedAlgorithms = unique(data.algorithm, "stable").';
|
||||||
|
else
|
||||||
|
selectedAlgorithms = string(selectedAlgorithms);
|
||||||
|
data = data(ismember(data.algorithm, selectedAlgorithms), :);
|
||||||
|
end
|
||||||
|
|
||||||
|
if isempty(data)
|
||||||
|
warning("PLOT_mpi_reduction_db_ber_vs_sir:NoSelectedRows", ...
|
||||||
|
"No rows remain after selectedPamLevels/selectedAlgorithms filtering.");
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
data.clean_keep = true(height(data), 1);
|
||||||
|
[groupId, groupPam, groupAlgorithm, groupSir] = findgroups(data.pam_level, data.algorithm, data.sir);
|
||||||
|
for groupIdx = 1:max(groupId)
|
||||||
|
rowMask = groupId == groupIdx;
|
||||||
|
berValues = data.BER(rowMask);
|
||||||
|
if nnz(rowMask) > 3
|
||||||
|
data.clean_keep(rowMask) = ~isoutlier(berValues);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
cleanData = data(data.clean_keep, :);
|
||||||
|
|
||||||
|
summaryTable = groupsummary(cleanData, ["pam_level", "algorithm", "sir"], ...
|
||||||
|
{"mean", "min", "max"}, "BER");
|
||||||
|
sirExactTable = groupsummary(cleanData, ["pam_level", "algorithm", "sir"], ...
|
||||||
|
"median", "sir_exact");
|
||||||
|
summaryTable.sir_exact = sirExactTable.median_sir_exact;
|
||||||
|
summaryTable = sortrows(summaryTable, ["pam_level", "algorithm", "sir"]);
|
||||||
|
|
||||||
|
fprintf("Cleaned to %d rows across %d PAM/algorithm/SIR groups.\n", ...
|
||||||
|
height(cleanData), height(summaryTable));
|
||||||
|
disp(groupcounts(cleanData, ["pam_level", "algorithm"]));
|
||||||
|
|
||||||
|
%% 3) Plot data
|
||||||
|
|
||||||
|
if exist("linspecer", "file")
|
||||||
|
algColors = linspecer(numel(selectedAlgorithms));
|
||||||
|
else
|
||||||
|
algColors = lines(numel(selectedAlgorithms));
|
||||||
|
end
|
||||||
|
|
||||||
|
figure(); clf;
|
||||||
|
tiledlayout(numel(selectedPamLevels), 1, "TileSpacing", "compact");
|
||||||
|
|
||||||
|
for pamIdx = 1:numel(selectedPamLevels)
|
||||||
|
pamLevel = selectedPamLevels(pamIdx);
|
||||||
|
nexttile; hold on;
|
||||||
|
|
||||||
|
for algIdx = 1:numel(selectedAlgorithms)
|
||||||
|
algorithmName = selectedAlgorithms(algIdx);
|
||||||
|
algColor = algColors(algIdx, :);
|
||||||
|
rawMask = cleanData.pam_level == pamLevel & cleanData.algorithm == algorithmName;
|
||||||
|
curveMask = summaryTable.pam_level == pamLevel & summaryTable.algorithm == algorithmName;
|
||||||
|
|
||||||
|
if ~any(curveMask)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
scatter(cleanData.sir_exact(rawMask), cleanData.BER(rawMask), ...
|
||||||
|
30, ...
|
||||||
|
"Marker", ".", ...
|
||||||
|
"MarkerEdgeColor", algColor, ...
|
||||||
|
"HandleVisibility", "off");
|
||||||
|
|
||||||
|
sirValues = summaryTable.sir_exact(curveMask).';
|
||||||
|
meanBer = summaryTable.mean_BER(curveMask).';
|
||||||
|
minBer = summaryTable.min_BER(curveMask).';
|
||||||
|
maxBer = summaryTable.max_BER(curveMask).';
|
||||||
|
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
|
||||||
|
|
||||||
|
if useBoundedLines && exist("boundedline", "file") && any(valid)
|
||||||
|
yLower = max(meanBer - minBer, 0);
|
||||||
|
yUpper = max(maxBer - meanBer, 0);
|
||||||
|
yBounds = [yLower(:), yUpper(:)];
|
||||||
|
|
||||||
|
[hl, hp] = boundedline(sirValues(valid).', meanBer(valid).', yBounds(valid, :), ...
|
||||||
|
'alpha', 'transparency', 0.08, ...
|
||||||
|
'cmap', algColor, ...
|
||||||
|
'nan', 'fill', ...
|
||||||
|
'orientation', 'vert');
|
||||||
|
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
|
||||||
|
set(hp, "LineStyle", "none", "HandleVisibility", "off");
|
||||||
|
end
|
||||||
|
|
||||||
|
plot(sirValues(valid), meanBer(valid), ...
|
||||||
|
"LineStyle", "none", ...
|
||||||
|
"Marker", "o", ...
|
||||||
|
"MarkerSize", 5, ...
|
||||||
|
"LineWidth", 1.2, ...
|
||||||
|
"Color", algColor, ...
|
||||||
|
"MarkerFaceColor", algColor, ...
|
||||||
|
"DisplayName", char(algorithmName));
|
||||||
|
|
||||||
|
if usePolyfit
|
||||||
|
fitMask = valid & meanBer > 0;
|
||||||
|
if nnz(fitMask) >= 2
|
||||||
|
fitOrder = min(polyfitOrderMax, nnz(fitMask) - 1);
|
||||||
|
fitCoeff = polyfit(sirValues(fitMask), log10(meanBer(fitMask)), fitOrder);
|
||||||
|
xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
|
||||||
|
yFit = 10 .^ polyval(fitCoeff, xFit);
|
||||||
|
|
||||||
|
plot(xFit, yFit, ...
|
||||||
|
"LineStyle", "--", ...
|
||||||
|
"LineWidth", 1.1, ...
|
||||||
|
"Color", algColor, ...
|
||||||
|
"HandleVisibility", "off");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
yline(2.2e-4, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||||
|
yline(3.8e-3, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||||
|
yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||||
|
|
||||||
|
title(sprintf("PAM %.0f, path %.0f m, block update %s", ...
|
||||||
|
pamLevel, pathLengthToPlot, blockUpdateLabel(selectedBlockUpdate)));
|
||||||
|
xlabel("SIR (dB)");
|
||||||
|
ylabel("BER");
|
||||||
|
set(gca, "YScale", "log");
|
||||||
|
ylim([9e-5, maxBerForPlot]);
|
||||||
|
grid on;
|
||||||
|
box on;
|
||||||
|
legend("Location", "best", "Interpreter", "none");
|
||||||
|
end
|
||||||
|
|
||||||
|
if exist("beautifyBERplot", "file")
|
||||||
|
beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
|
||||||
|
end
|
||||||
|
|
||||||
|
%% Local helpers
|
||||||
|
|
||||||
|
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 = stringColumn(values)
|
||||||
|
if iscell(values)
|
||||||
|
values = string(values);
|
||||||
|
elseif ischar(values)
|
||||||
|
values = string(values);
|
||||||
|
end
|
||||||
|
values = strip(string(values));
|
||||||
|
end
|
||||||
|
|
||||||
|
function label = blockUpdateLabel(selectedBlockUpdate)
|
||||||
|
if isempty(selectedBlockUpdate)
|
||||||
|
label = "pooled";
|
||||||
|
else
|
||||||
|
label = string(selectedBlockUpdate);
|
||||||
|
end
|
||||||
|
end
|
||||||
539
projects/Diss/MPI_revisit/algorithms/recombine_warehouses.m
Normal file
539
projects/Diss/MPI_revisit/algorithms/recombine_warehouses.m
Normal file
@@ -0,0 +1,539 @@
|
|||||||
|
% === DSP settings ===
|
||||||
|
dsp_options = struct();
|
||||||
|
dsp_options.mode = "run_id";
|
||||||
|
dsp_options.recipe = @mpi_recipe_dev;
|
||||||
|
dsp_options.append_to_db = false;
|
||||||
|
dsp_options.start_occurence = 1;
|
||||||
|
dsp_options.max_occurences = 15;
|
||||||
|
dsp_options.debug_plots = false;
|
||||||
|
|
||||||
|
dsp_options.database_type = "mysql";
|
||||||
|
|
||||||
|
dsp_options.dataBase = "labor";
|
||||||
|
dsp_options.storage_path = "W:\labdata\ECOC Silas\ecoc_2025";
|
||||||
|
|
||||||
|
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);
|
||||||
|
|
||||||
|
scriptDir = fileparts(mfilename("fullpath"));
|
||||||
|
|
||||||
|
whpam4_loaded = load(fullfile(scriptDir, "pam_4_results.mat"));
|
||||||
|
whpam6_loaded = load(fullfile(scriptDir, "pam_6_results.mat"));
|
||||||
|
whpam8_loaded = load(fullfile(scriptDir, "pam_8_results.mat"));
|
||||||
|
|
||||||
|
whpam4 = whpam4_loaded.obj;
|
||||||
|
whpam6 = whpam6_loaded.obj;
|
||||||
|
whpam8 = whpam8_loaded.obj;
|
||||||
|
|
||||||
|
whList = {whpam4, whpam6, whpam8};
|
||||||
|
algorithmStorageNames = fieldnames(whpam4.sto);
|
||||||
|
|
||||||
|
%% Build a lossless run_id warehouse and cache run metadata
|
||||||
|
|
||||||
|
pamformats = [4, 6, 8];
|
||||||
|
baudrates = [112e9, 96e9, 72e9];
|
||||||
|
runMetaTable = queryRunMetadata(db, whList, pamformats, baudrates);
|
||||||
|
|
||||||
|
wh_run_id_combined = mergeDataStoragesByUnion(whList);
|
||||||
|
wh_run_id_combined = addRunMetadataStorages(wh_run_id_combined, runMetaTable);
|
||||||
|
|
||||||
|
runIdSavePath = fullfile(scriptDir, "combined_by_run_id_results.mat");
|
||||||
|
save(runIdSavePath, "wh_run_id_combined", "runMetaTable", "algorithmStorageNames", '-v7.3');
|
||||||
|
|
||||||
|
fprintf("Saved run_id-combined warehouse:\n %s\n", runIdSavePath);
|
||||||
|
wh_run_id_combined.showInfo;
|
||||||
|
|
||||||
|
%% Build a grouped config warehouse for analysis views
|
||||||
|
|
||||||
|
configFields = ["pam_level", "symbolrate", "interference_path_length", "sir"];
|
||||||
|
wh_config_combined = buildConfigWarehouse( ...
|
||||||
|
wh_run_id_combined, runMetaTable, algorithmStorageNames, configFields);
|
||||||
|
|
||||||
|
configSavePath = fullfile(scriptDir, "combined_by_config_results.mat");
|
||||||
|
save(configSavePath, "wh_config_combined", "runMetaTable", "algorithmStorageNames", '-v7.3');
|
||||||
|
|
||||||
|
fprintf("Saved config-grouped warehouse:\n %s\n", configSavePath);
|
||||||
|
wh_config_combined.showInfo;
|
||||||
|
|
||||||
|
%% BER over SIR at fixed path length
|
||||||
|
|
||||||
|
plot_options = struct();
|
||||||
|
plot_options.path_length = 1000;
|
||||||
|
plot_options.use_boundedlines = true;
|
||||||
|
plot_options.boundedline_alpha = 0.10;
|
||||||
|
plot_options.polyfit_order_max = 3;
|
||||||
|
plot_options.fec_ber_threshold = 2e-2;
|
||||||
|
plot_options.figure_base = 7100;
|
||||||
|
plot_options.xlim = [15 35];
|
||||||
|
|
||||||
|
plotBerOverSirForPath(wh_config_combined, algorithmStorageNames, plot_options);
|
||||||
|
|
||||||
|
%% Local functions
|
||||||
|
|
||||||
|
function runMetaTable = queryRunMetadata(db, whList, pamformats, baudrates)
|
||||||
|
metaTables = {};
|
||||||
|
metadataFields = db.getTableFieldNames('Runs');
|
||||||
|
|
||||||
|
for whIdx = 1:numel(whList)
|
||||||
|
curWh = whList{whIdx};
|
||||||
|
curRunIds = curWh.parameter.run_id.values(:);
|
||||||
|
|
||||||
|
fp = QueryFilter();
|
||||||
|
fp.where('Runs', 'run_id', 'GREATER_EQUAL', 3153);
|
||||||
|
fp.where('Runs', 'symbolrate', 'EQUALS', baudrates(whIdx));
|
||||||
|
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||||
|
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||||
|
fp.where('Runs', 'pam_level', 'EQUALS', pamformats(whIdx));
|
||||||
|
|
||||||
|
[dataTable, ~] = db.queryDB(fp, metadataFields);
|
||||||
|
dataTable.run_id = numericColumn(dataTable.run_id);
|
||||||
|
dataTable = dataTable(ismember(dataTable.run_id, curRunIds), :);
|
||||||
|
|
||||||
|
missingRunIds = setdiff(curRunIds, dataTable.run_id);
|
||||||
|
if ~isempty(missingRunIds)
|
||||||
|
warning("recombine_warehouses:MissingMetadata", ...
|
||||||
|
"%d run_id(s) from PAM %.0f warehouse were not found in the metadata query.", ...
|
||||||
|
numel(missingRunIds), pamformats(whIdx));
|
||||||
|
end
|
||||||
|
|
||||||
|
metaTables{end+1} = dataTable; %#ok<AGROW>
|
||||||
|
end
|
||||||
|
|
||||||
|
runMetaTable = vertcat(metaTables{:});
|
||||||
|
[~, uniqueIdx] = unique(numericColumn(runMetaTable.run_id), "stable");
|
||||||
|
runMetaTable = runMetaTable(uniqueIdx, :);
|
||||||
|
|
||||||
|
numericFields = ["run_id", "pam_level", "symbolrate", "interference_path_length", "sir"];
|
||||||
|
for fieldIdx = 1:numel(numericFields)
|
||||||
|
fieldName = numericFields(fieldIdx);
|
||||||
|
if ismember(fieldName, string(runMetaTable.Properties.VariableNames))
|
||||||
|
runMetaTable.(char(fieldName)) = numericColumn(runMetaTable.(char(fieldName)));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
[~, sortIdx] = sort(runMetaTable.run_id);
|
||||||
|
runMetaTable = runMetaTable(sortIdx, :);
|
||||||
|
end
|
||||||
|
|
||||||
|
function x = numericColumn(x)
|
||||||
|
if isnumeric(x)
|
||||||
|
x = double(x);
|
||||||
|
elseif iscell(x)
|
||||||
|
x = str2double(string(x));
|
||||||
|
elseif isstring(x) || ischar(x) || iscategorical(x)
|
||||||
|
x = str2double(string(x));
|
||||||
|
else
|
||||||
|
x = double(x);
|
||||||
|
end
|
||||||
|
x = x(:);
|
||||||
|
end
|
||||||
|
|
||||||
|
function whMerged = mergeDataStoragesByUnion(whList)
|
||||||
|
templateWh = whList{1};
|
||||||
|
paramNames = cellstr(templateWh.fn);
|
||||||
|
|
||||||
|
for whIdx = 2:numel(whList)
|
||||||
|
if ~isequal(sort(cellstr(whList{whIdx}.fn)), sort(paramNames))
|
||||||
|
error("recombine_warehouses:ParameterMismatch", ...
|
||||||
|
"All input warehouses must use the same parameter names.");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
mergedParams = struct();
|
||||||
|
for paramIdx = 1:numel(paramNames)
|
||||||
|
paramName = paramNames{paramIdx};
|
||||||
|
values = [];
|
||||||
|
|
||||||
|
for whIdx = 1:numel(whList)
|
||||||
|
newValues = whList{whIdx}.parameter.(paramName).values(:).';
|
||||||
|
values = [values, newValues]; %#ok<AGROW>
|
||||||
|
end
|
||||||
|
|
||||||
|
values = unique(values, "stable");
|
||||||
|
if isnumeric(values)
|
||||||
|
values = sort(values);
|
||||||
|
end
|
||||||
|
mergedParams.(paramName) = values;
|
||||||
|
end
|
||||||
|
|
||||||
|
whMerged = DataStorage(mergedParams);
|
||||||
|
|
||||||
|
for whIdx = 1:numel(whList)
|
||||||
|
curWh = whList{whIdx};
|
||||||
|
curStorageNames = fieldnames(curWh.sto);
|
||||||
|
|
||||||
|
for storageIdx = 1:numel(curStorageNames)
|
||||||
|
storageName = curStorageNames{storageIdx};
|
||||||
|
if ~isfield(whMerged.sto, storageName)
|
||||||
|
whMerged.addStorage(storageName);
|
||||||
|
end
|
||||||
|
|
||||||
|
for linIdx = 1:numel(curWh.sto.(storageName))
|
||||||
|
storedValue = curWh.sto.(storageName){linIdx};
|
||||||
|
if isempty(storedValue)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
targetLinIdx = mapLinearIndex(curWh, whMerged, linIdx);
|
||||||
|
existingValue = whMerged.sto.(storageName){targetLinIdx};
|
||||||
|
whMerged.sto.(storageName){targetLinIdx} = mergeStoredValue(existingValue, storedValue);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function wh = addRunMetadataStorages(wh, runMetaTable)
|
||||||
|
metadataStorageNames = ["meta_pam_level", "meta_symbolrate", ...
|
||||||
|
"meta_interference_path_length", "meta_sir"];
|
||||||
|
metadataTableFields = ["pam_level", "symbolrate", "interference_path_length", "sir"];
|
||||||
|
|
||||||
|
for fieldIdx = 1:numel(metadataStorageNames)
|
||||||
|
if ~isfield(wh.sto, metadataStorageNames(fieldIdx))
|
||||||
|
wh.addStorage(char(metadataStorageNames(fieldIdx)));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
for linIdx = 1:wh.getLastLinIndice()
|
||||||
|
physStruct = linearIndexToStruct(wh, linIdx);
|
||||||
|
if ~isfield(physStruct, "run_id")
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
metaIdx = find(runMetaTable.run_id == physStruct.run_id, 1);
|
||||||
|
if isempty(metaIdx)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
for fieldIdx = 1:numel(metadataStorageNames)
|
||||||
|
wh.sto.(char(metadataStorageNames(fieldIdx))){linIdx} = ...
|
||||||
|
runMetaTable.(char(metadataTableFields(fieldIdx)))(metaIdx);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function whConfig = buildConfigWarehouse(whRun, runMetaTable, algorithmStorageNames, configFields)
|
||||||
|
runParamNames = cellstr(whRun.fn);
|
||||||
|
extraParamNames = setdiff(string(runParamNames), "run_id", "stable");
|
||||||
|
|
||||||
|
configParams = struct();
|
||||||
|
for fieldIdx = 1:numel(configFields)
|
||||||
|
fieldName = configFields(fieldIdx);
|
||||||
|
values = unique(runMetaTable.(char(fieldName))(:).', "stable");
|
||||||
|
if isnumeric(values)
|
||||||
|
values = sort(values);
|
||||||
|
end
|
||||||
|
configParams.(char(fieldName)) = values;
|
||||||
|
end
|
||||||
|
|
||||||
|
for paramIdx = 1:numel(extraParamNames)
|
||||||
|
paramName = extraParamNames(paramIdx);
|
||||||
|
configParams.(char(paramName)) = whRun.parameter.(char(paramName)).values;
|
||||||
|
end
|
||||||
|
|
||||||
|
whConfig = DataStorage(configParams);
|
||||||
|
for storageIdx = 1:numel(algorithmStorageNames)
|
||||||
|
whConfig.addStorage(algorithmStorageNames{storageIdx});
|
||||||
|
end
|
||||||
|
whConfig.addStorage("run_id");
|
||||||
|
|
||||||
|
for storageIdx = 1:numel(algorithmStorageNames)
|
||||||
|
storageName = algorithmStorageNames{storageIdx};
|
||||||
|
|
||||||
|
for linIdx = 1:numel(whRun.sto.(storageName))
|
||||||
|
storedValue = whRun.sto.(storageName){linIdx};
|
||||||
|
if isempty(storedValue)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
sourcePhys = linearIndexToStruct(whRun, linIdx);
|
||||||
|
metaIdx = find(runMetaTable.run_id == sourcePhys.run_id, 1);
|
||||||
|
if isempty(metaIdx)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
targetArgs = configTargetArgs(whConfig, sourcePhys, runMetaTable, metaIdx, configFields);
|
||||||
|
targetLinIdx = whConfig.getIndicesByPhys(targetArgs);
|
||||||
|
|
||||||
|
existingValue = whConfig.sto.(storageName){targetLinIdx};
|
||||||
|
whConfig.sto.(storageName){targetLinIdx} = mergeStoredValue(existingValue, storedValue);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
for linIdx = 1:whRun.getLastLinIndice()
|
||||||
|
sourcePhys = linearIndexToStruct(whRun, linIdx);
|
||||||
|
metaIdx = find(runMetaTable.run_id == sourcePhys.run_id, 1);
|
||||||
|
if isempty(metaIdx)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
targetArgs = configTargetArgs(whConfig, sourcePhys, runMetaTable, metaIdx, configFields);
|
||||||
|
targetLinIdx = whConfig.getIndicesByPhys(targetArgs);
|
||||||
|
whConfig.sto.run_id{targetLinIdx} = mergeUniqueNumeric( ...
|
||||||
|
whConfig.sto.run_id{targetLinIdx}, sourcePhys.run_id);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function targetArgs = configTargetArgs(whConfig, sourcePhys, runMetaTable, metaIdx, configFields)
|
||||||
|
targetArgs = cell(1, numel(whConfig.fn));
|
||||||
|
|
||||||
|
for paramIdx = 1:numel(whConfig.fn)
|
||||||
|
paramName = string(whConfig.fn(paramIdx));
|
||||||
|
if any(configFields == paramName)
|
||||||
|
targetArgs{paramIdx} = runMetaTable.(char(paramName))(metaIdx);
|
||||||
|
else
|
||||||
|
targetArgs{paramIdx} = sourcePhys.(char(paramName));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function targetLinIdx = mapLinearIndex(sourceWh, targetWh, sourceLinIdx)
|
||||||
|
sourcePhys = linearIndexToStruct(sourceWh, sourceLinIdx);
|
||||||
|
targetArgs = cell(1, numel(targetWh.fn));
|
||||||
|
|
||||||
|
for paramIdx = 1:numel(targetWh.fn)
|
||||||
|
paramName = char(targetWh.fn(paramIdx));
|
||||||
|
targetArgs{paramIdx} = sourcePhys.(paramName);
|
||||||
|
end
|
||||||
|
|
||||||
|
targetLinIdx = targetWh.getIndicesByPhys(targetArgs);
|
||||||
|
end
|
||||||
|
|
||||||
|
function physStruct = linearIndexToStruct(wh, linIdx)
|
||||||
|
[physValues, physNames] = wh.getPhysIndicesByLinIndex(linIdx);
|
||||||
|
physStruct = struct();
|
||||||
|
|
||||||
|
for paramIdx = 1:numel(physNames)
|
||||||
|
physStruct.(char(physNames{paramIdx})) = physValues{paramIdx};
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function out = mergeStoredValue(existingValue, newValue)
|
||||||
|
if isempty(existingValue)
|
||||||
|
out = newValue;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
existingPackages = asPackageCell(existingValue);
|
||||||
|
newPackages = asPackageCell(newValue);
|
||||||
|
out = [existingPackages(:); newPackages(:)].';
|
||||||
|
end
|
||||||
|
|
||||||
|
function packages = asPackageCell(value)
|
||||||
|
if isempty(value)
|
||||||
|
packages = {};
|
||||||
|
elseif iscell(value)
|
||||||
|
packages = value(:).';
|
||||||
|
else
|
||||||
|
packages = {value};
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function out = mergeUniqueNumeric(existingValue, newValue)
|
||||||
|
if isempty(existingValue)
|
||||||
|
out = newValue;
|
||||||
|
else
|
||||||
|
out = unique([existingValue(:); newValue(:)].', "stable");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function plotBerOverSirForPath(whConfig, algorithmStorageNames, plotOptions)
|
||||||
|
paramNames = string(whConfig.fn);
|
||||||
|
|
||||||
|
if ~any(paramNames == "pam_level") || ~any(paramNames == "interference_path_length") || ~any(paramNames == "sir")
|
||||||
|
error("recombine_warehouses:MissingPlotParameters", ...
|
||||||
|
"The config warehouse must contain pam_level, interference_path_length, and sir parameters.");
|
||||||
|
end
|
||||||
|
|
||||||
|
pamLevels = whConfig.parameter.pam_level.values;
|
||||||
|
hasBlockUpdate = any(paramNames == "block_update");
|
||||||
|
if hasBlockUpdate
|
||||||
|
blockUpdates = whConfig.parameter.block_update.values;
|
||||||
|
else
|
||||||
|
blockUpdates = NaN;
|
||||||
|
end
|
||||||
|
|
||||||
|
if exist("linspecer", "file")
|
||||||
|
curveColors = linspecer(max(numel(algorithmStorageNames), 1));
|
||||||
|
else
|
||||||
|
curveColors = lines(max(numel(algorithmStorageNames), 1));
|
||||||
|
end
|
||||||
|
|
||||||
|
figureIdx = plotOptions.figure_base;
|
||||||
|
for blockIdx = 1:numel(blockUpdates)
|
||||||
|
for pamIdx = 1:numel(pamLevels)
|
||||||
|
pamLevel = pamLevels(pamIdx);
|
||||||
|
figureIdx = figureIdx + 1;
|
||||||
|
figure(figureIdx); clf; hold on
|
||||||
|
|
||||||
|
for storageIdx = 1:numel(algorithmStorageNames)
|
||||||
|
storageName = algorithmStorageNames{storageIdx};
|
||||||
|
curveColor = curveColors(storageIdx, :);
|
||||||
|
|
||||||
|
filter = struct();
|
||||||
|
filter.pam_level = pamLevel;
|
||||||
|
filter.interference_path_length = plotOptions.path_length;
|
||||||
|
if hasBlockUpdate
|
||||||
|
filter.block_update = blockUpdates(blockIdx);
|
||||||
|
end
|
||||||
|
|
||||||
|
[sirValues, berGroups] = collectBerBySir(whConfig, storageName, filter);
|
||||||
|
if isempty(sirValues)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
[sirValues, sortIdx] = sort(sirValues);
|
||||||
|
berGroups = berGroups(sortIdx);
|
||||||
|
|
||||||
|
berAvg = nan(1, numel(sirValues));
|
||||||
|
berMin = nan(1, numel(sirValues));
|
||||||
|
berMax = nan(1, numel(sirValues));
|
||||||
|
|
||||||
|
for sirIdx = 1:numel(sirValues)
|
||||||
|
rawBer = berGroups{sirIdx};
|
||||||
|
rawBer = rawBer(isfinite(rawBer));
|
||||||
|
if isempty(rawBer)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
scatter(repmat(sirValues(sirIdx), size(rawBer)), rawBer, ...
|
||||||
|
12, ...
|
||||||
|
"Marker", ".", ...
|
||||||
|
"MarkerEdgeColor", curveColor, ...
|
||||||
|
"MarkerFaceColor", curveColor, ...
|
||||||
|
"HandleVisibility", "off");
|
||||||
|
|
||||||
|
berAvg(sirIdx) = mean(rawBer, "omitnan");
|
||||||
|
berMin(sirIdx) = min(rawBer);
|
||||||
|
berMax(sirIdx) = max(rawBer);
|
||||||
|
end
|
||||||
|
|
||||||
|
validAvg = isfinite(sirValues) & isfinite(berAvg);
|
||||||
|
if ~any(validAvg)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
if plotOptions.use_boundedlines && exist("boundedline", "file")
|
||||||
|
yLower = max(berAvg - berMin, 0);
|
||||||
|
yUpper = max(berMax - berAvg, 0);
|
||||||
|
yBounds = [yLower(:), yUpper(:)];
|
||||||
|
|
||||||
|
[hl, hp] = boundedline(sirValues(:), berAvg(:), yBounds, ...
|
||||||
|
"alpha", "transparency", plotOptions.boundedline_alpha, ...
|
||||||
|
"cmap", curveColor, ...
|
||||||
|
"nan", "fill", ...
|
||||||
|
"orientation", "vert");
|
||||||
|
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
|
||||||
|
set(hp, "HandleVisibility", "off", "LineStyle", "none");
|
||||||
|
end
|
||||||
|
|
||||||
|
scatter(sirValues(validAvg), berAvg(validAvg), ...
|
||||||
|
54, ...
|
||||||
|
"Marker", "o", ...
|
||||||
|
"MarkerEdgeColor", curveColor, ...
|
||||||
|
"MarkerFaceColor", "none", ...
|
||||||
|
"LineWidth", 1.2, ...
|
||||||
|
"DisplayName", storageName);
|
||||||
|
|
||||||
|
fitMask = validAvg & berAvg > 0;
|
||||||
|
if nnz(fitMask) >= 2
|
||||||
|
fitOrder = min(plotOptions.polyfit_order_max, nnz(fitMask) - 1);
|
||||||
|
fitCoeff = polyfit(sirValues(fitMask), log10(berAvg(fitMask)), fitOrder);
|
||||||
|
xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
|
||||||
|
yFit = 10 .^ polyval(fitCoeff, xFit);
|
||||||
|
|
||||||
|
plot(xFit, yFit, ...
|
||||||
|
"LineWidth", 1.2, ...
|
||||||
|
"LineStyle", "--", ...
|
||||||
|
"Marker", "none", ...
|
||||||
|
"Color", curveColor, ...
|
||||||
|
"HandleVisibility", "off");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if hasBlockUpdate
|
||||||
|
title(sprintf("BER over SIR, PAM %.0f, path %.0f m, block update = %g", ...
|
||||||
|
pamLevel, plotOptions.path_length, blockUpdates(blockIdx)));
|
||||||
|
else
|
||||||
|
title(sprintf("BER over SIR, PAM %.0f, path %.0f m", ...
|
||||||
|
pamLevel, plotOptions.path_length));
|
||||||
|
end
|
||||||
|
xlabel("SIR (dB)");
|
||||||
|
ylabel("BER");
|
||||||
|
xlim(plotOptions.xlim);
|
||||||
|
grid on
|
||||||
|
box on
|
||||||
|
|
||||||
|
if exist("beautifyBERplot", "file")
|
||||||
|
beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
|
||||||
|
else
|
||||||
|
set(gca, "YScale", "log");
|
||||||
|
end
|
||||||
|
legend("Location", "best", "Interpreter", "none");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function [sirValues, berGroups] = collectBerBySir(whConfig, storageName, filter)
|
||||||
|
sirValues = [];
|
||||||
|
berGroups = {};
|
||||||
|
|
||||||
|
for linIdx = 1:numel(whConfig.sto.(storageName))
|
||||||
|
storedValue = whConfig.sto.(storageName){linIdx};
|
||||||
|
if isempty(storedValue)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
physStruct = linearIndexToStruct(whConfig, linIdx);
|
||||||
|
if ~matchesFilter(physStruct, filter)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
berValues = extractBerValues(storedValue);
|
||||||
|
if isempty(berValues)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
sirValue = physStruct.sir;
|
||||||
|
sirIdx = find(sirValues == sirValue, 1);
|
||||||
|
if isempty(sirIdx)
|
||||||
|
sirValues(end+1) = sirValue; %#ok<AGROW>
|
||||||
|
berGroups{end+1} = berValues; %#ok<AGROW>
|
||||||
|
else
|
||||||
|
berGroups{sirIdx} = [berGroups{sirIdx}, berValues]; %#ok<AGROW>
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function tf = matchesFilter(physStruct, filter)
|
||||||
|
tf = true;
|
||||||
|
filterFields = fieldnames(filter);
|
||||||
|
|
||||||
|
for fieldIdx = 1:numel(filterFields)
|
||||||
|
fieldName = filterFields{fieldIdx};
|
||||||
|
if ~isfield(physStruct, fieldName) || physStruct.(fieldName) ~= filter.(fieldName)
|
||||||
|
tf = false;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function berValues = extractBerValues(value)
|
||||||
|
berValues = [];
|
||||||
|
|
||||||
|
if isstruct(value)
|
||||||
|
if isfield(value, "metrics") && isfield(value.metrics, "BER")
|
||||||
|
berValues(end+1) = value.metrics.BER;
|
||||||
|
end
|
||||||
|
elseif iscell(value)
|
||||||
|
for valueIdx = 1:numel(value)
|
||||||
|
berValues = [berValues, extractBerValues(value{valueIdx})]; %#ok<AGROW>
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
99
projects/Diss/MPI_revisit/db/appendMpiReductionDspOutput.m
Normal file
99
projects/Diss/MPI_revisit/db/appendMpiReductionDspOutput.m
Normal file
@@ -0,0 +1,99 @@
|
|||||||
|
function [summary, rows] = appendMpiReductionDspOutput(db, run_id, occurrence_idx, dspOutput, dsp_options, options)
|
||||||
|
%appendMpiReductionDspOutput Store one DSP occurrence in MpiReductionResults.
|
||||||
|
|
||||||
|
arguments
|
||||||
|
db
|
||||||
|
run_id
|
||||||
|
occurrence_idx
|
||||||
|
dspOutput struct
|
||||||
|
dsp_options struct = struct()
|
||||||
|
options.study_name string = "mpi_reduction_v1"
|
||||||
|
options.dry_run (1,1) logical = false
|
||||||
|
options.verbose (1,1) logical = false
|
||||||
|
end
|
||||||
|
|
||||||
|
summary = emptySummary();
|
||||||
|
rows = {};
|
||||||
|
|
||||||
|
packageNames = fieldnames(dspOutput);
|
||||||
|
if isempty(packageNames)
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
block_update = resolveBlockUpdate(dspOutput, dsp_options);
|
||||||
|
wh = DataStorage(struct( ...
|
||||||
|
"run_id", double(run_id), ...
|
||||||
|
"block_update", double(block_update)));
|
||||||
|
|
||||||
|
hasPackages = false;
|
||||||
|
for packageIdx = 1:numel(packageNames)
|
||||||
|
packageName = packageNames{packageIdx};
|
||||||
|
package = dspOutput.(packageName);
|
||||||
|
if isempty(package)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
if iscell(package)
|
||||||
|
packageCell = package;
|
||||||
|
else
|
||||||
|
packageCell = {package};
|
||||||
|
end
|
||||||
|
|
||||||
|
wh.addStorage(packageName);
|
||||||
|
wh.addValueToStorageByLinIdx(packageCell, packageName, 1);
|
||||||
|
hasPackages = true;
|
||||||
|
end
|
||||||
|
|
||||||
|
if ~hasPackages
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
dsp_options_local = dsp_options;
|
||||||
|
dsp_options_local.start_occurence = occurrence_idx;
|
||||||
|
dsp_options_local.userParameters.block_update = block_update;
|
||||||
|
|
||||||
|
[summary, rows] = appendMpiReductionWarehouse(db, wh, run_id, dsp_options_local, ...
|
||||||
|
"study_name", options.study_name, ...
|
||||||
|
"dry_run", options.dry_run, ...
|
||||||
|
"verbose", options.verbose);
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
function summary = emptySummary()
|
||||||
|
summary = struct( ...
|
||||||
|
"rows_considered", 0, ...
|
||||||
|
"rows_inserted", 0, ...
|
||||||
|
"rows_skipped_duplicate", 0, ...
|
||||||
|
"rows_skipped_existing_key", 0, ...
|
||||||
|
"rows_skipped_empty", 0);
|
||||||
|
end
|
||||||
|
|
||||||
|
function block_update = resolveBlockUpdate(dspOutput, dsp_options)
|
||||||
|
if isfield(dsp_options, "userParameters") && ...
|
||||||
|
isfield(dsp_options.userParameters, "block_update") && ...
|
||||||
|
isscalar(dsp_options.userParameters.block_update)
|
||||||
|
block_update = dsp_options.userParameters.block_update;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
packageNames = fieldnames(dspOutput);
|
||||||
|
for packageIdx = 1:numel(packageNames)
|
||||||
|
package = dspOutput.(packageNames{packageIdx});
|
||||||
|
if isempty(package)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
if iscell(package)
|
||||||
|
package = package{1};
|
||||||
|
end
|
||||||
|
if isstruct(package) && ...
|
||||||
|
isfield(package, "mpi_reduction_config") && ...
|
||||||
|
isfield(package.mpi_reduction_config, "params") && ...
|
||||||
|
isfield(package.mpi_reduction_config.params, "block_update")
|
||||||
|
block_update = package.mpi_reduction_config.params.block_update;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
error("appendMpiReductionDspOutput:MissingBlockUpdate", ...
|
||||||
|
"Could not resolve scalar block_update from dsp_options or package metadata.");
|
||||||
|
end
|
||||||
307
projects/Diss/MPI_revisit/db/appendMpiReductionWarehouse.m
Normal file
307
projects/Diss/MPI_revisit/db/appendMpiReductionWarehouse.m
Normal file
@@ -0,0 +1,307 @@
|
|||||||
|
function [summary, rows] = appendMpiReductionWarehouse(db, wh, run_ids, dsp_options, options)
|
||||||
|
%appendMpiReductionWarehouse Store MPI reduction warehouse entries in MySQL.
|
||||||
|
|
||||||
|
arguments
|
||||||
|
db
|
||||||
|
wh
|
||||||
|
run_ids
|
||||||
|
dsp_options struct
|
||||||
|
options.study_name string = "mpi_reduction_v1"
|
||||||
|
options.dry_run (1,1) logical = false
|
||||||
|
options.verbose (1,1) logical = true
|
||||||
|
end
|
||||||
|
|
||||||
|
ensureTableVisible(db);
|
||||||
|
|
||||||
|
storageNames = fieldnames(wh.sto);
|
||||||
|
summary = struct( ...
|
||||||
|
"rows_considered", 0, ...
|
||||||
|
"rows_inserted", 0, ...
|
||||||
|
"rows_skipped_duplicate", 0, ...
|
||||||
|
"rows_skipped_existing_key", 0, ...
|
||||||
|
"rows_skipped_empty", 0);
|
||||||
|
rows = {};
|
||||||
|
|
||||||
|
for storageIdx = 1:numel(storageNames)
|
||||||
|
storageName = storageNames{storageIdx};
|
||||||
|
storageArray = wh.sto.(storageName);
|
||||||
|
|
||||||
|
for linIdx = 1:numel(storageArray)
|
||||||
|
packageCell = storageArray{linIdx};
|
||||||
|
if isempty(packageCell)
|
||||||
|
summary.rows_skipped_empty = summary.rows_skipped_empty + 1;
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
phys = physicalCoordinates(wh, linIdx);
|
||||||
|
run_id = resolveRunId(phys, run_ids);
|
||||||
|
block_update = resolveBlockUpdate(phys, dsp_options);
|
||||||
|
packageCell = normalizePackageCell(packageCell);
|
||||||
|
|
||||||
|
for packageIdx = 1:numel(packageCell)
|
||||||
|
package = packageCell{packageIdx};
|
||||||
|
if isempty(package) || ~isstruct(package) || ~isfield(package, "metrics")
|
||||||
|
summary.rows_skipped_empty = summary.rows_skipped_empty + 1;
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
occurrence_idx = startOccurrence(dsp_options) + packageIdx - 1;
|
||||||
|
row = buildRow(db, package, storageName, run_id, occurrence_idx, ...
|
||||||
|
block_update, options.study_name);
|
||||||
|
summary.rows_considered = summary.rows_considered + 1;
|
||||||
|
rows{end+1,1} = row; %#ok<AGROW>
|
||||||
|
|
||||||
|
if options.dry_run
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
if resultHashExists(db, row.result_hash)
|
||||||
|
summary.rows_skipped_duplicate = summary.rows_skipped_duplicate + 1;
|
||||||
|
if options.verbose
|
||||||
|
fprintf("MPI reduction DB: skipped duplicate %s, run_id=%d, occurrence=%d, block_update=%d\n", ...
|
||||||
|
row.storage_name, row.run_id, row.occurrence_idx, row.block_update);
|
||||||
|
end
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
if uniqueResultKeyExists(db, row)
|
||||||
|
summary.rows_skipped_existing_key = summary.rows_skipped_existing_key + 1;
|
||||||
|
if options.verbose
|
||||||
|
fprintf("MPI reduction DB: skipped existing key %s, run_id=%d, occurrence=%d, block_update=%d\n", ...
|
||||||
|
row.storage_name, row.run_id, row.occurrence_idx, row.block_update);
|
||||||
|
end
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
db.appendToTable("MpiReductionResults", row);
|
||||||
|
summary.rows_inserted = summary.rows_inserted + 1;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if options.verbose
|
||||||
|
fprintf("MPI reduction DB: considered %d rows, inserted %d, duplicates %d, existing keys %d, empty %d\n", ...
|
||||||
|
summary.rows_considered, summary.rows_inserted, ...
|
||||||
|
summary.rows_skipped_duplicate, summary.rows_skipped_existing_key, ...
|
||||||
|
summary.rows_skipped_empty);
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
function ensureTableVisible(db)
|
||||||
|
if ~isfield(db.tables, "MpiReductionResults")
|
||||||
|
db.refresh();
|
||||||
|
end
|
||||||
|
|
||||||
|
if ~isfield(db.tables, "MpiReductionResults")
|
||||||
|
error("appendMpiReductionWarehouse:MissingTable", ...
|
||||||
|
"Table MpiReductionResults is not visible to DBHandler. Create the table and call db.refresh().");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function phys = physicalCoordinates(wh, linIdx)
|
||||||
|
phys = struct();
|
||||||
|
[physValues, physNames] = wh.getPhysIndicesByLinIndex(linIdx);
|
||||||
|
|
||||||
|
for physIdx = 1:numel(physNames)
|
||||||
|
name = char(physNames{physIdx});
|
||||||
|
phys.(name) = physValues{physIdx};
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function run_id = resolveRunId(phys, run_ids)
|
||||||
|
if isfield(phys, "run_id")
|
||||||
|
run_id = phys.run_id;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if isscalar(run_ids)
|
||||||
|
run_id = run_ids;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
error("appendMpiReductionWarehouse:MissingRunId", ...
|
||||||
|
"Warehouse has no run_id axis, but run_ids is not scalar.");
|
||||||
|
end
|
||||||
|
|
||||||
|
function block_update = resolveBlockUpdate(phys, dsp_options)
|
||||||
|
if isfield(phys, "block_update")
|
||||||
|
block_update = phys.block_update;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if isfield(dsp_options, "userParameters") && ...
|
||||||
|
isfield(dsp_options.userParameters, "block_update") && ...
|
||||||
|
isscalar(dsp_options.userParameters.block_update)
|
||||||
|
block_update = dsp_options.userParameters.block_update;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
error("appendMpiReductionWarehouse:MissingBlockUpdate", ...
|
||||||
|
"Could not resolve block_update from warehouse coordinates or scalar userParameters.");
|
||||||
|
end
|
||||||
|
|
||||||
|
function packageCell = normalizePackageCell(packageCell)
|
||||||
|
if ~iscell(packageCell)
|
||||||
|
packageCell = {packageCell};
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function occurrence = startOccurrence(dsp_options)
|
||||||
|
occurrence = 1;
|
||||||
|
if isfield(dsp_options, "start_occurence")
|
||||||
|
occurrence = dsp_options.start_occurence;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function row = buildRow(db, package, storageName, run_id, occurrence_idx, block_update, study_name)
|
||||||
|
config = fieldOr(package, "mpi_reduction_config", fallbackConfig(storageName, block_update));
|
||||||
|
params = fieldOr(config, "params", struct());
|
||||||
|
if isempty(params)
|
||||||
|
params = struct();
|
||||||
|
end
|
||||||
|
|
||||||
|
metrics = package.metrics;
|
||||||
|
if isobject(metrics) && ismethod(metrics, "toStruct")
|
||||||
|
metrics = metrics.toStruct();
|
||||||
|
end
|
||||||
|
|
||||||
|
param_json = jsonencode(params);
|
||||||
|
if strlength(string(param_json)) == 0
|
||||||
|
param_json = "{}";
|
||||||
|
end
|
||||||
|
|
||||||
|
row = struct();
|
||||||
|
row.run_id = double(run_id);
|
||||||
|
row.occurrence_idx = double(occurrence_idx);
|
||||||
|
row.study_name = char(study_name);
|
||||||
|
row.storage_name = char(fieldOr(config, "storage_name", storageName));
|
||||||
|
row.algorithm = char(fieldOr(config, "algorithm", "unknown"));
|
||||||
|
row.algorithm_variant = char(fieldOr(config, "algorithm_variant", ""));
|
||||||
|
row.eq_class = char(fieldOr(config, "eq_class", "unknown"));
|
||||||
|
row.block_update = double(block_update);
|
||||||
|
|
||||||
|
row.sps = scalarParam(params, "sps");
|
||||||
|
row.eq_order = scalarParam(params, "order");
|
||||||
|
row.len_tr = scalarParam(params, "len_tr");
|
||||||
|
row.epochs_tr = scalarParam(params, "epochs_tr");
|
||||||
|
row.mu_tr = scalarParam(params, "mu_tr");
|
||||||
|
row.dd_mode = logicalParam(params, "dd_mode");
|
||||||
|
row.epochs_dd = scalarParam(params, "epochs_dd");
|
||||||
|
row.mu_dd = scalarParam(params, "mu_dd");
|
||||||
|
|
||||||
|
row.numBits = metricScalar(metrics, "numBits", 0);
|
||||||
|
row.numBitErr = metricScalar(metrics, "numBitErr", 0);
|
||||||
|
row.BER = metricScalar(metrics, "BER", NaN);
|
||||||
|
row.numBitErr_precoded = metricScalar(metrics, "numBitErr_precoded", NaN);
|
||||||
|
row.BER_precoded = metricScalar(metrics, "BER_precoded", NaN);
|
||||||
|
row.SNR = metricScalar(metrics, "SNR", NaN);
|
||||||
|
row.SNR_level = metricJson(metrics, "SNR_level");
|
||||||
|
row.GMI = metricScalar(metrics, "GMI", NaN);
|
||||||
|
row.AIR = metricScalar(metrics, "AIR", NaN);
|
||||||
|
row.EVM = metricScalar(metrics, "EVM", NaN);
|
||||||
|
row.EVM_level = metricJson(metrics, "EVM_level");
|
||||||
|
row.STD = metricScalar(metrics, "STD", NaN);
|
||||||
|
row.STD_level = metricJson(metrics, "STD_level");
|
||||||
|
row.STDrx = metricScalar(metrics, "STDrx", NaN);
|
||||||
|
row.STDrx_level = metricJson(metrics, "STDrx_level");
|
||||||
|
row.Alpha = firstFiniteScalar(fieldOr(metrics, "Alpha", NaN));
|
||||||
|
|
||||||
|
row.param_json = char(param_json);
|
||||||
|
row.param_hash = db.calcHash(params);
|
||||||
|
row.result_hash = db.calcHash(row);
|
||||||
|
end
|
||||||
|
|
||||||
|
function config = fallbackConfig(storageName, block_update)
|
||||||
|
params = struct("block_update", block_update);
|
||||||
|
config = struct( ...
|
||||||
|
"storage_name", storageName, ...
|
||||||
|
"algorithm", "unknown", ...
|
||||||
|
"algorithm_variant", "", ...
|
||||||
|
"eq_class", "unknown", ...
|
||||||
|
"params", params);
|
||||||
|
end
|
||||||
|
|
||||||
|
function value = fieldOr(s, fieldName, defaultValue)
|
||||||
|
fieldName = char(fieldName);
|
||||||
|
if isstruct(s) && isfield(s, fieldName) && ~isempty(s.(fieldName))
|
||||||
|
value = s.(fieldName);
|
||||||
|
else
|
||||||
|
value = defaultValue;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function value = scalarParam(params, fieldName)
|
||||||
|
value = firstFiniteScalar(fieldOr(params, fieldName, NaN));
|
||||||
|
end
|
||||||
|
|
||||||
|
function value = logicalParam(params, fieldName)
|
||||||
|
value = firstFiniteScalar(fieldOr(params, fieldName, false));
|
||||||
|
if isnan(value)
|
||||||
|
value = false;
|
||||||
|
else
|
||||||
|
value = logical(value);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function value = metricScalar(metrics, fieldName, defaultValue)
|
||||||
|
value = firstFiniteScalar(fieldOr(metrics, fieldName, defaultValue));
|
||||||
|
end
|
||||||
|
|
||||||
|
function value = metricJson(metrics, fieldName)
|
||||||
|
metricValue = fieldOr(metrics, fieldName, []);
|
||||||
|
value = char(jsonencode(metricValue));
|
||||||
|
end
|
||||||
|
|
||||||
|
function value = firstFiniteScalar(valueIn)
|
||||||
|
if islogical(valueIn)
|
||||||
|
valueIn = double(valueIn);
|
||||||
|
end
|
||||||
|
|
||||||
|
if isempty(valueIn)
|
||||||
|
value = NaN;
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
if isnumeric(valueIn)
|
||||||
|
finiteValues = valueIn(isfinite(valueIn));
|
||||||
|
if isempty(finiteValues)
|
||||||
|
value = NaN;
|
||||||
|
else
|
||||||
|
value = double(finiteValues(1));
|
||||||
|
end
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
value = str2double(string(valueIn));
|
||||||
|
if isnan(value)
|
||||||
|
value = NaN;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function exists = resultHashExists(db, resultHash)
|
||||||
|
query = sprintf("SELECT mpi_result_id FROM MpiReductionResults WHERE result_hash = '%s' LIMIT 1", ...
|
||||||
|
char(resultHash));
|
||||||
|
existing = db.fetch(query);
|
||||||
|
exists = ~isempty(existing);
|
||||||
|
end
|
||||||
|
|
||||||
|
function exists = uniqueResultKeyExists(db, row)
|
||||||
|
query = sprintf( ...
|
||||||
|
"SELECT mpi_result_id FROM MpiReductionResults " + ...
|
||||||
|
"WHERE run_id = %d " + ...
|
||||||
|
"AND occurrence_idx = %d " + ...
|
||||||
|
"AND study_name = '%s' " + ...
|
||||||
|
"AND storage_name = '%s' " + ...
|
||||||
|
"AND block_update = %d " + ...
|
||||||
|
"AND param_hash = '%s' LIMIT 1", ...
|
||||||
|
row.run_id, row.occurrence_idx, ...
|
||||||
|
sqlString(row.study_name), sqlString(row.storage_name), ...
|
||||||
|
row.block_update, sqlString(row.param_hash));
|
||||||
|
existing = db.fetch(query);
|
||||||
|
exists = ~isempty(existing);
|
||||||
|
end
|
||||||
|
|
||||||
|
function value = sqlString(value)
|
||||||
|
value = strrep(char(value), '''', '''''');
|
||||||
|
end
|
||||||
@@ -4,8 +4,14 @@ dsp_options.mode = "run_id";
|
|||||||
dsp_options.recipe = @mpi_recipe_dev;
|
dsp_options.recipe = @mpi_recipe_dev;
|
||||||
dsp_options.append_to_db = false;
|
dsp_options.append_to_db = false;
|
||||||
dsp_options.start_occurence = 1;
|
dsp_options.start_occurence = 1;
|
||||||
dsp_options.max_occurences = 10;
|
dsp_options.max_occurences = 15;
|
||||||
dsp_options.debug_plots = true;
|
dsp_options.debug_plots = false;
|
||||||
|
|
||||||
|
write_mpi_reduction_db = 1;
|
||||||
|
stream_mpi_reduction_db = 1;
|
||||||
|
mpi_reduction_study_name = "block_update_sweep";
|
||||||
|
mpi_reduction_writer_path = fullfile(fileparts(mfilename('fullpath')),"db");
|
||||||
|
addpath(mpi_reduction_writer_path);
|
||||||
|
|
||||||
dsp_options.database_type = "mysql";
|
dsp_options.database_type = "mysql";
|
||||||
|
|
||||||
@@ -16,6 +22,9 @@ dsp_options.server = "192.168.178.192";
|
|||||||
dsp_options.port = 3306;
|
dsp_options.port = 3306;
|
||||||
dsp_options.user = "silas";
|
dsp_options.user = "silas";
|
||||||
dsp_options.password = "silas";
|
dsp_options.password = "silas";
|
||||||
|
dsp_options.append_mpi_reduction_db = write_mpi_reduction_db && stream_mpi_reduction_db;
|
||||||
|
dsp_options.mpi_reduction_study_name = mpi_reduction_study_name;
|
||||||
|
dsp_options.mpi_reduction_writer_path = mpi_reduction_writer_path;
|
||||||
|
|
||||||
db = DBHandler("dataBase", [dsp_options.dataBase],...
|
db = DBHandler("dataBase", [dsp_options.dataBase],...
|
||||||
"type", dsp_options.database_type,...
|
"type", dsp_options.database_type,...
|
||||||
@@ -23,37 +32,41 @@ db = DBHandler("dataBase", [dsp_options.dataBase],...
|
|||||||
"user", dsp_options.user, "password", dsp_options.password);
|
"user", dsp_options.user, "password", dsp_options.password);
|
||||||
|
|
||||||
%%
|
%%
|
||||||
|
|
||||||
|
pamformats = [4,6,8];
|
||||||
|
baudrates = [112e9,96e9,72e9];
|
||||||
|
|
||||||
|
for i = 1
|
||||||
|
|
||||||
|
B = baudrates(i);
|
||||||
|
M = pamformats(i);
|
||||||
|
|
||||||
fp = QueryFilter();
|
fp = QueryFilter();
|
||||||
fp.where('Runs','run_id','GREATER_EQUAL',3153);
|
fp.where('Runs','run_id','GREATER_EQUAL',3153);
|
||||||
fp.where('Runs', 'symbolrate', 'EQUALS', 112e9);
|
fp.where('Runs', 'symbolrate', 'EQUALS', B); % 72 96 112
|
||||||
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||||
fp.where('Runs', 'interference_path_length', 'EQUALS', 1000);
|
fp.where('Runs', 'interference_path_length', 'EQUALS', 1000);
|
||||||
fp.where('Runs', 'sir', 'LESS_EQUAL', 30);
|
% fp.where('Runs', 'sir', 'LESS_EQUAL', 20);
|
||||||
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||||
% fp.where('Runs', 'is_mpi', 'EQUALS', 1);
|
% fp.where('Runs', 'is_mpi', 'EQUALS', 1);
|
||||||
fp.where('Runs', 'pam_level', 'EQUALS', 4);
|
fp.where('Runs', 'pam_level', 'EQUALS', M);
|
||||||
% fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
|
% fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
|
||||||
|
|
||||||
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||||
|
|
||||||
% [~, uniqueSirRows] = unique(dataTable.sir, "stable");
|
|
||||||
% dataTable = dataTable(uniqueSirRows, :);
|
|
||||||
|
|
||||||
[~, sortIdx] = sort(dataTable.sir, 'descend');
|
[~, sortIdx] = sort(dataTable.sir, 'descend');
|
||||||
dataTable = dataTable(sortIdx, :);
|
dataTable = dataTable(sortIdx, :);
|
||||||
run_ids = dataTable.run_id;
|
|
||||||
|
|
||||||
if isempty(run_ids)
|
% dataTable = dataTable(1,:);
|
||||||
error("run_minimal_recipe:MissingRunIds", ...
|
|
||||||
"Set run_ids to one or more known run IDs before running this example.");
|
run_ids = dataTable.run_id;
|
||||||
end
|
|
||||||
|
|
||||||
%% === Warehouse setup ===
|
%% === Warehouse setup ===
|
||||||
dsp_options.userParameters = struct();
|
dsp_options.userParameters = struct();
|
||||||
% dsp_options.userParameters.smoothing_length = [0,linspace(100,1000,10),2500,5000,10000];
|
dsp_options.userParameters.block_update = [1,2,4,8,16,32,64,112,224,448,448*2,1024,2048,4096,8192,16384];%%logspace(-3.8,-1,22);%[linspace(2,4096,22)];
|
||||||
|
% dsp_options.userParameters.block_update = linspace(1,224,22);
|
||||||
wh = DataStorage(dsp_options.userParameters);
|
wh = DataStorage(dsp_options.userParameters);
|
||||||
|
|
||||||
|
|
||||||
%%
|
%%
|
||||||
n_realizations = (dsp_options.max_occurences - dsp_options.start_occurence + 1);
|
n_realizations = (dsp_options.max_occurences - dsp_options.start_occurence + 1);
|
||||||
n_userparams = prod(wh.dim);
|
n_userparams = prod(wh.dim);
|
||||||
@@ -67,38 +80,74 @@ fprintf("-> [ %d run_id(s) × %d userParam combination(s) = %d parallel job(s) ]
|
|||||||
%% === Run ===
|
%% === Run ===
|
||||||
% submitJobs returns the raw per-job results and the filled Warehouse. For a
|
% submitJobs returns the raw per-job results and the filled Warehouse. For a
|
||||||
% single run_id and remove_dc = [0, 1], results is a 2-by-1 cell array.
|
% single run_id and remove_dc = [0, 1], results is a 2-by-1 cell array.
|
||||||
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.serial, ...
|
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.parallel, ...
|
||||||
"wh", wh, ...
|
"wh", wh, ...
|
||||||
"waitbar", true);
|
"waitbar", true);
|
||||||
|
|
||||||
|
%%
|
||||||
|
|
||||||
|
if write_mpi_reduction_db && ~stream_mpi_reduction_db
|
||||||
|
db.refresh();
|
||||||
|
appendMpiReductionWarehouse(db, wh, run_ids, dsp_options, ...
|
||||||
|
"study_name", mpi_reduction_study_name);
|
||||||
|
end
|
||||||
|
|
||||||
|
% savepath = fullfile("C:","Users","Silas","Documents","MATLAB","imdd_simulation","projects","Diss","MPI_revisit","algorithms",sprintf("pam_%d_results.mat",M));
|
||||||
|
% save(wh,savepath)
|
||||||
|
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
%% Analyze
|
%% Analyze
|
||||||
|
|
||||||
storageNames = fieldnames(wh.sto);
|
storageNames = fieldnames(wh.sto);
|
||||||
x_vars = run_ids;%dsp_options.userParameters.smoothing_length;
|
|
||||||
x_vars = reshape(x_vars,1,[]);
|
|
||||||
|
|
||||||
figure(2026);hold on
|
x_base = dataTable.sir(:).';
|
||||||
for i = 1:numel(run_ids)
|
% x_base = dsp_options.userParameters.block_update;
|
||||||
disp(run_ids(i))
|
|
||||||
result = wh.getStoValue(storageNames{1}, x_vars);
|
figure(2026); clf; hold on
|
||||||
% Each cell in result is a cell array (packageCell) containing multiple packages.
|
for storage_idx = 1:numel(storageNames)
|
||||||
% Extract BERs from all packages and, for example, average them per x_var.
|
storageName = storageNames{storage_idx};
|
||||||
ber_all = cellfun(@(packageCell) cellfun(@(pkg) pkg.metrics.BER, packageCell), result, 'UniformOutput', false);
|
result = wh.sto.(storageName);
|
||||||
% Convert to numeric matrix (rows = packages, cols = x_vars) by padding if needed
|
result = result(:).';
|
||||||
|
|
||||||
|
% Each stored entry is a package cell containing one or more occurrences.
|
||||||
|
ber_all = cell(1,numel(result));
|
||||||
|
for result_idx = 1:numel(result)
|
||||||
|
packageCell = result{result_idx};
|
||||||
|
if isempty(packageCell)
|
||||||
|
ber_all{result_idx} = NaN;
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
ber_values = nan(1,numel(packageCell));
|
||||||
|
for package_idx = 1:numel(packageCell)
|
||||||
|
if isstruct(packageCell{package_idx}) && isfield(packageCell{package_idx},"metrics")
|
||||||
|
ber_values(package_idx) = packageCell{package_idx}.metrics.BER;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
ber_all{result_idx} = ber_values;
|
||||||
|
end
|
||||||
|
|
||||||
maxPackages = max(cellfun(@numel,ber_all));
|
maxPackages = max(cellfun(@numel,ber_all));
|
||||||
ber_mat = nan(maxPackages,numel(ber_all));
|
ber_mat = nan(maxPackages,numel(ber_all));
|
||||||
for k = 1:numel(ber_all)
|
for k = 1:numel(ber_all)
|
||||||
ber_mat(1:numel(ber_all{k}),k) = ber_all{k};
|
ber_mat(1:numel(ber_all{k}),k) = ber_all{k};
|
||||||
end
|
end
|
||||||
% Choose aggregation: mean across packages (ignore NaNs)
|
ber = mean(ber_mat,1,"omitnan");
|
||||||
ber = mean(ber_mat, 1, 'omitnan');
|
|
||||||
|
|
||||||
|
x = x_base;
|
||||||
x = dataTable.sir;
|
if numel(x) ~= numel(ber)
|
||||||
y = ber;
|
x = 1:numel(ber);
|
||||||
|
|
||||||
plot(x,ber);
|
|
||||||
scatter(x,ber_mat)
|
|
||||||
beautifyBERplot("logscale",true,"setcolors",false,"setmarkers",true);
|
|
||||||
end
|
end
|
||||||
|
|
||||||
|
h = plot(x,ber,"DisplayName",storageName);
|
||||||
|
for k = 1:numel(x)
|
||||||
|
scatter(repmat(x(k),maxPackages,1),ber_mat(:,k), ...
|
||||||
|
"MarkerEdgeColor",h.Color, ...
|
||||||
|
"HandleVisibility","off");
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
beautifyBERplot("logscale",true,"setcolors",false,"setmarkers",true);
|
||||||
|
legend("Location","best","Interpreter","none");
|
||||||
|
|||||||
@@ -0,0 +1,73 @@
|
|||||||
|
% === DSP settings ===
|
||||||
|
dsp_options = struct();
|
||||||
|
dsp_options.mode = "run_id";
|
||||||
|
dsp_options.recipe = @mpi_recipe;
|
||||||
|
dsp_options.append_to_db = false;
|
||||||
|
dsp_options.start_occurence = 1;
|
||||||
|
dsp_options.max_occurences = 15;
|
||||||
|
dsp_options.debug_plots = false;
|
||||||
|
|
||||||
|
dsp_options.database_type = "mysql";
|
||||||
|
|
||||||
|
dsp_options.dataBase = "labor";
|
||||||
|
dsp_options.storage_path = "W:\labdata\ECOC Silas\ecoc_2025";
|
||||||
|
|
||||||
|
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);
|
||||||
|
|
||||||
|
%%
|
||||||
|
fp = QueryFilter();
|
||||||
|
fp.where('Runs','run_id','GREATER_EQUAL',3153);
|
||||||
|
fp.where('Runs', 'symbolrate', 'EQUALS', 72e9); % 72 96 112
|
||||||
|
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||||
|
% fp.where('Runs', 'interference_path_length', 'EQUALS', 1000);
|
||||||
|
% fp.where('Runs', 'sir', 'LESS_EQUAL', 50);
|
||||||
|
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||||
|
% fp.where('Runs', 'is_mpi', 'EQUALS', 1);
|
||||||
|
fp.where('Runs', 'pam_level', 'EQUALS', 8);
|
||||||
|
% fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
|
||||||
|
|
||||||
|
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||||
|
|
||||||
|
% [~, uniqueSirRows] = unique(dataTable.sir, "stable");
|
||||||
|
% dataTable = dataTable(uniqueSirRows, :);
|
||||||
|
|
||||||
|
[~, sortIdx] = sort(dataTable.sir, 'descend');
|
||||||
|
dataTable = dataTable(sortIdx, :);
|
||||||
|
% dataTable = dataTable(1, :);
|
||||||
|
run_ids = dataTable.run_id;
|
||||||
|
|
||||||
|
if isempty(run_ids)
|
||||||
|
error("run_minimal_recipe:MissingRunIds", ...
|
||||||
|
"Set run_ids to one or more known run IDs before running this example.");
|
||||||
|
end
|
||||||
|
|
||||||
|
%% === Warehouse setup ===
|
||||||
|
dsp_options.userParameters = struct();
|
||||||
|
dsp_options.userParameters.block_update = [1,2,4,8,16,32,64,112,224,448,512,1024,2048,2048*2,2048*4];%%logspace(-3.8,-1,22);%[linspace(2,4096,22)];
|
||||||
|
wh = DataStorage(dsp_options.userParameters);
|
||||||
|
|
||||||
|
|
||||||
|
%%
|
||||||
|
n_realizations = (dsp_options.max_occurences - dsp_options.start_occurence + 1);
|
||||||
|
n_userparams = prod(wh.dim);
|
||||||
|
n_run_ids = numel(run_ids);
|
||||||
|
parallel_jobs = n_userparams * n_run_ids;
|
||||||
|
queried_jobs = n_realizations * n_userparams * n_run_ids;
|
||||||
|
|
||||||
|
fprintf("-> [ %d run_id(s) × %d userParam combination(s) = %d parallel job(s) ] × %d realizations = %d total jobs \n", ...
|
||||||
|
n_run_ids, n_userparams, parallel_jobs, n_realizations, queried_jobs);
|
||||||
|
|
||||||
|
%% === Run ===
|
||||||
|
% submitJobs returns the raw per-job results and the filled Warehouse. For a
|
||||||
|
% single run_id and remove_dc = [0, 1], results is a 2-by-1 cell array.
|
||||||
|
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.parallel, ...
|
||||||
|
"wh", wh, ...
|
||||||
|
"waitbar", true);
|
||||||
Binary file not shown.
@@ -0,0 +1,389 @@
|
|||||||
|
%% Analyze
|
||||||
|
% === DSP settings ===
|
||||||
|
dsp_options = struct();
|
||||||
|
dsp_options.mode = "run_id";
|
||||||
|
dsp_options.recipe = @mpi_recipe_dev;
|
||||||
|
dsp_options.append_to_db = false;
|
||||||
|
dsp_options.start_occurence = 1;
|
||||||
|
dsp_options.max_occurences = 15;
|
||||||
|
dsp_options.debug_plots = false;
|
||||||
|
|
||||||
|
dsp_options.database_type = "mysql";
|
||||||
|
|
||||||
|
dsp_options.dataBase = "labor";
|
||||||
|
dsp_options.storage_path = "W:\labdata\ECOC Silas\ecoc_2025";
|
||||||
|
|
||||||
|
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);
|
||||||
|
|
||||||
|
%%
|
||||||
|
|
||||||
|
fp = QueryFilter();
|
||||||
|
fp.where('Runs','run_id','GREATER_EQUAL',3153);
|
||||||
|
fp.where('Runs', 'symbolrate', 'EQUALS', 112e9); % 72 96 112
|
||||||
|
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||||
|
fp.where('Runs', 'interference_path_length', 'EQUALS', 1000);
|
||||||
|
% fp.where('Runs', 'sir', 'LESS_EQUAL', 50);
|
||||||
|
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||||
|
% fp.where('Runs', 'is_mpi', 'EQUALS', 1);
|
||||||
|
fp.where('Runs', 'pam_level', 'EQUALS', 4);
|
||||||
|
% fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
|
||||||
|
|
||||||
|
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||||
|
|
||||||
|
% [~, uniqueSirRows] = unique(dataTable.sir, "stable");
|
||||||
|
% dataTable = dataTable(uniqueSirRows, :);
|
||||||
|
|
||||||
|
[~, sortIdx] = sort(dataTable.sir, 'descend');
|
||||||
|
dataTable = dataTable(sortIdx, :);
|
||||||
|
% dataTable = dataTable(1, :);
|
||||||
|
run_ids = dataTable.run_id;
|
||||||
|
%%
|
||||||
|
|
||||||
|
wh_ = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\MPI_revisit\parallelization_analysis\wh_block_update_pam4_combined.mat");
|
||||||
|
wh = wh_.wh;
|
||||||
|
|
||||||
|
% wh_ = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\MPI_revisit\parallelization_analysis\wh_block_update_pam4_short_blocks.mat");
|
||||||
|
% wh2 = wh_.wh;
|
||||||
|
%
|
||||||
|
% wh = mergeDataStorages(wh1,wh2);
|
||||||
|
|
||||||
|
%%
|
||||||
|
|
||||||
|
|
||||||
|
storageNames = fieldnames(wh.sto);
|
||||||
|
storageNames = storageNames([1,2,3,5]);
|
||||||
|
ber_by_storage = struct();
|
||||||
|
ber_mat_by_storage = struct();
|
||||||
|
|
||||||
|
sir_values = dataTable.sir(:).';
|
||||||
|
block_updates = wh.parameter.block_update.values;
|
||||||
|
storage_parameter_names = cellstr(wh.fn);
|
||||||
|
block_axis = find(strcmp(storage_parameter_names,"block_update"),1);
|
||||||
|
|
||||||
|
if exist('linspecer','file')
|
||||||
|
curve_colors = linspecer(max(numel(storageNames),1));
|
||||||
|
else
|
||||||
|
curve_colors = lines(max(numel(storageNames),1));
|
||||||
|
end
|
||||||
|
|
||||||
|
useBoundedLines = true;
|
||||||
|
usePolyfit = true;
|
||||||
|
polyfitOrderMax = 4;
|
||||||
|
fec_ber_threshold = 3.8e-3;%3.8e-3;
|
||||||
|
fit_coeff_by_storage = struct();
|
||||||
|
required_sir_fec_by_storage = struct();
|
||||||
|
for storage_idx = 1:numel(storageNames)
|
||||||
|
storageName = storageNames{storage_idx};
|
||||||
|
fit_coeff_by_storage.(storageName) = cell(1,numel(block_updates));
|
||||||
|
required_sir_fec_by_storage.(storageName) = nan(1,numel(block_updates));
|
||||||
|
end
|
||||||
|
|
||||||
|
for block_idx = 1:numel(block_updates)
|
||||||
|
block_update = block_updates(block_idx);
|
||||||
|
|
||||||
|
figure(3000 + block_idx); clf; hold on
|
||||||
|
for storage_idx = 1:numel(storageNames)
|
||||||
|
storageName = storageNames{storage_idx};
|
||||||
|
curve_color = curve_colors(storage_idx,:);
|
||||||
|
result = wh.sto.(storageName);
|
||||||
|
|
||||||
|
if ~isempty(block_axis) && size(result,block_axis) == numel(block_updates)
|
||||||
|
result_subs = repmat({':'},1,ndims(result));
|
||||||
|
result_subs{block_axis} = block_idx;
|
||||||
|
result_for_block = result(result_subs{:});
|
||||||
|
result_for_block = result_for_block(:).';
|
||||||
|
else
|
||||||
|
result_for_block = result(:).';
|
||||||
|
end
|
||||||
|
|
||||||
|
% Each stored entry is a package cell containing one or more occurrences.
|
||||||
|
ber_all = cell(1,numel(result_for_block));
|
||||||
|
for result_idx = 1:numel(result_for_block)
|
||||||
|
packageCell = result_for_block{result_idx};
|
||||||
|
if isempty(packageCell)
|
||||||
|
ber_all{result_idx} = NaN;
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
ber_values = nan(1,numel(packageCell));
|
||||||
|
for package_idx = 1:numel(packageCell)
|
||||||
|
if isstruct(packageCell{package_idx}) && isfield(packageCell{package_idx},"metrics")
|
||||||
|
ber_values(package_idx) = packageCell{package_idx}.metrics.BER;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
ber_all{result_idx} = ber_values;
|
||||||
|
end
|
||||||
|
|
||||||
|
maxPackages = max(cellfun(@numel,ber_all));
|
||||||
|
ber_mat = nan(maxPackages,numel(ber_all));
|
||||||
|
for k = 1:numel(ber_all)
|
||||||
|
ber_mat(1:numel(ber_all{k}),k) = ber_all{k};
|
||||||
|
end
|
||||||
|
|
||||||
|
% Match the BER-over-SIR plot cleanup: remove high BER points and outliers per SIR.
|
||||||
|
for col = 1:size(ber_mat,2)
|
||||||
|
colData = ber_mat(:,col);
|
||||||
|
colData(~isfinite(colData) | colData >= 0.1) = NaN;
|
||||||
|
validColData = colData(isfinite(colData));
|
||||||
|
if isempty(validColData)
|
||||||
|
ber_mat(:,col) = NaN;
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
outliers = isoutlier(validColData);
|
||||||
|
validColData(outliers) = NaN;
|
||||||
|
colData(isfinite(colData)) = validColData;
|
||||||
|
ber_mat(:,col) = colData;
|
||||||
|
end
|
||||||
|
|
||||||
|
ber = mean(ber_mat,1,"omitnan");
|
||||||
|
ber_min = nan(1,numel(ber));
|
||||||
|
ber_max = nan(1,numel(ber));
|
||||||
|
for k = 1:numel(ber)
|
||||||
|
ber_here = ber_mat(:,k);
|
||||||
|
ber_here = ber_here(isfinite(ber_here));
|
||||||
|
if isempty(ber_here)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
ber_min(k) = min(ber_here);
|
||||||
|
ber_max(k) = max(ber_here);
|
||||||
|
end
|
||||||
|
|
||||||
|
x = sir_values;
|
||||||
|
if numel(x) ~= numel(ber)
|
||||||
|
x = 1:numel(ber);
|
||||||
|
end
|
||||||
|
|
||||||
|
ber_by_storage.(storageName){block_idx} = ber;
|
||||||
|
ber_mat_by_storage.(storageName){block_idx} = ber_mat;
|
||||||
|
|
||||||
|
for k = 1:numel(x)
|
||||||
|
scatter(repmat(x(k),maxPackages,1),ber_mat(:,k), ...
|
||||||
|
30, ...
|
||||||
|
'Marker','.', ...
|
||||||
|
'MarkerEdgeColor',curve_color, ...
|
||||||
|
'MarkerFaceColor',curve_color, ...
|
||||||
|
'HandleVisibility','off');
|
||||||
|
end
|
||||||
|
|
||||||
|
valid = isfinite(x) & isfinite(ber);
|
||||||
|
if ~any(valid)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
% if useBoundedLines && exist("boundedline","file")
|
||||||
|
% y_lower = max(ber - ber_min,0);
|
||||||
|
% y_upper = max(ber_max - ber,0);
|
||||||
|
% y_bounds = [y_lower(:), y_upper(:)];
|
||||||
|
%
|
||||||
|
% [hl, hp] = boundedline(x(valid).',ber(valid).',y_bounds(valid,:), ...
|
||||||
|
% 'alpha', 'transparency', 0.08, ...
|
||||||
|
% 'cmap', curve_color, ...
|
||||||
|
% 'nan', 'fill', ...
|
||||||
|
% 'orientation', 'vert');
|
||||||
|
% set(hl, ...
|
||||||
|
% 'LineStyle','none', ...
|
||||||
|
% 'Marker','none', ...
|
||||||
|
% 'HandleVisibility','off');
|
||||||
|
% set(hp, ...
|
||||||
|
% 'HandleVisibility','off', ...
|
||||||
|
% 'LineStyle','none');
|
||||||
|
% end
|
||||||
|
|
||||||
|
scatter(x(valid),ber(valid), ...
|
||||||
|
20, ...
|
||||||
|
'Marker','o', ...
|
||||||
|
'MarkerEdgeColor',curve_color, ...
|
||||||
|
'MarkerFaceColor',curve_color, ...
|
||||||
|
'LineWidth',1, ...
|
||||||
|
'DisplayName',storageName);
|
||||||
|
|
||||||
|
fit_mask = valid & ber > 0;
|
||||||
|
if usePolyfit && nnz(fit_mask) >= 2
|
||||||
|
fit_order = min(polyfitOrderMax,nnz(fit_mask)-1);
|
||||||
|
fit_coeff = polyfit(x(fit_mask),log10(ber(fit_mask)),fit_order);
|
||||||
|
x_fit = linspace(min(x(fit_mask)),max(x(fit_mask)),300);
|
||||||
|
y_fit = 10.^polyval(fit_coeff,x_fit);
|
||||||
|
fit_coeff_by_storage.(storageName){block_idx} = fit_coeff;
|
||||||
|
|
||||||
|
sir_req = NaN;
|
||||||
|
threshold_mask = isfinite(y_fit) & y_fit <= fec_ber_threshold;
|
||||||
|
if any(threshold_mask)
|
||||||
|
first_threshold_idx = find(threshold_mask,1,"first");
|
||||||
|
if first_threshold_idx == 1
|
||||||
|
sir_req = x_fit(first_threshold_idx);
|
||||||
|
else
|
||||||
|
x_pair = x_fit(first_threshold_idx-1:first_threshold_idx);
|
||||||
|
y_pair = log10(y_fit(first_threshold_idx-1:first_threshold_idx));
|
||||||
|
if all(isfinite(y_pair)) && diff(y_pair) ~= 0
|
||||||
|
sir_req = interp1(y_pair,x_pair,log10(fec_ber_threshold), ...
|
||||||
|
"linear","extrap");
|
||||||
|
else
|
||||||
|
sir_req = x_fit(first_threshold_idx);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
required_sir_fec_by_storage.(storageName)(block_idx) = sir_req;
|
||||||
|
|
||||||
|
plot(x_fit,y_fit, ...
|
||||||
|
'LineWidth',1.1, ...
|
||||||
|
'LineStyle','--', ...
|
||||||
|
'Color',curve_color, ...
|
||||||
|
'HandleVisibility','off');
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
title(sprintf("BER over SIR, block update = %g",block_update));
|
||||||
|
xlabel("SIR (dB)");
|
||||||
|
ylabel("BER");
|
||||||
|
|
||||||
|
yline(2.2e-4, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||||
|
yline(3.8e-3, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||||
|
yline(2e-2, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||||
|
ylim([9e-5, 0.1]);
|
||||||
|
|
||||||
|
set(gca, "YScale", "log");
|
||||||
|
xlim([15,45]);
|
||||||
|
grid on;
|
||||||
|
box on;
|
||||||
|
legend("Location", "best", "Interpreter", "none");
|
||||||
|
end
|
||||||
|
|
||||||
|
%% Required SIR to reach FEC over parallelization
|
||||||
|
|
||||||
|
figure(5000); clf; hold on
|
||||||
|
for storage_idx = 1:numel(storageNames)
|
||||||
|
storageName = storageNames{storage_idx};
|
||||||
|
curve_color = curve_colors(storage_idx,:);
|
||||||
|
required_sir = required_sir_fec_by_storage.(storageName);
|
||||||
|
valid = isfinite(block_updates) & isfinite(required_sir);
|
||||||
|
|
||||||
|
if ~any(valid)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
plot(block_updates(valid),required_sir(valid), ...
|
||||||
|
'LineWidth',1.4, ...
|
||||||
|
'LineStyle','-', ...
|
||||||
|
'Marker','o', ...
|
||||||
|
'MarkerSize',5, ...
|
||||||
|
'Color',curve_color, ...
|
||||||
|
'DisplayName',storageName);
|
||||||
|
end
|
||||||
|
|
||||||
|
set(gca,'XScale','log');
|
||||||
|
xticks(block_updates);
|
||||||
|
xticklabels(string(block_updates));
|
||||||
|
grid on
|
||||||
|
box on
|
||||||
|
xlabel("Parallelization / block update");
|
||||||
|
ylabel(sprintf("Required SIR at BER = %.1e (dB)",fec_ber_threshold));
|
||||||
|
title("Required SIR to reach FEC over parallelization");
|
||||||
|
legend("Location","best","Interpreter","none");
|
||||||
|
|
||||||
|
|
||||||
|
%%
|
||||||
|
|
||||||
|
|
||||||
|
function whMerged = mergeDataStorages(varargin)
|
||||||
|
% mergeDataStorages merges compatible DataStorage objects by physical indices.
|
||||||
|
% Existing duplicate entries are concatenated when both values are package cells.
|
||||||
|
|
||||||
|
whList = varargin;
|
||||||
|
templateWh = whList{1};
|
||||||
|
paramNames = cellstr(templateWh.fn);
|
||||||
|
|
||||||
|
mergedParams = struct();
|
||||||
|
for param_idx = 1:numel(paramNames)
|
||||||
|
paramName = paramNames{param_idx};
|
||||||
|
values = templateWh.parameter.(paramName).values(:).';
|
||||||
|
|
||||||
|
for wh_idx = 2:numel(whList)
|
||||||
|
curWh = whList{wh_idx};
|
||||||
|
if ~isequal(sort(cellstr(curWh.fn)),sort(paramNames))
|
||||||
|
error("mergeDataStorages:ParameterMismatch", ...
|
||||||
|
"All warehouses must use the same parameter names.");
|
||||||
|
end
|
||||||
|
if ~isfield(curWh.parameter,paramName)
|
||||||
|
error("mergeDataStorages:ParameterMismatch", ...
|
||||||
|
"Warehouse %d does not contain parameter '%s'.",wh_idx,paramName);
|
||||||
|
end
|
||||||
|
|
||||||
|
newValues = curWh.parameter.(paramName).values(:).';
|
||||||
|
for value_idx = 1:numel(newValues)
|
||||||
|
if ~ismember(newValues(value_idx),values)
|
||||||
|
values(end+1) = newValues(value_idx); %#ok<AGROW>
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
if strcmp(paramName,"block_update") && isnumeric(values)
|
||||||
|
values = sort(values);
|
||||||
|
end
|
||||||
|
|
||||||
|
mergedParams.(paramName) = values;
|
||||||
|
end
|
||||||
|
|
||||||
|
whMerged = DataStorage(mergedParams);
|
||||||
|
|
||||||
|
for wh_idx = 1:numel(whList)
|
||||||
|
curWh = whList{wh_idx};
|
||||||
|
curStorageNames = fieldnames(curWh.sto);
|
||||||
|
|
||||||
|
for storage_idx = 1:numel(curStorageNames)
|
||||||
|
storageName = curStorageNames{storage_idx};
|
||||||
|
if ~isfield(whMerged.sto,storageName)
|
||||||
|
whMerged.addStorage(storageName);
|
||||||
|
end
|
||||||
|
|
||||||
|
for lin_idx = 1:numel(curWh.sto.(storageName))
|
||||||
|
storedValue = curWh.sto.(storageName){lin_idx};
|
||||||
|
if isempty(storedValue)
|
||||||
|
continue
|
||||||
|
end
|
||||||
|
|
||||||
|
[physValues,physNames] = curWh.getPhysIndicesByLinIndex(lin_idx);
|
||||||
|
physNames = cellfun(@char,physNames,'UniformOutput',false);
|
||||||
|
targetSubscripts = cell(1,numel(whMerged.fn));
|
||||||
|
|
||||||
|
for param_idx = 1:numel(whMerged.fn)
|
||||||
|
paramName = char(whMerged.fn(param_idx));
|
||||||
|
sourceParamIdx = find(strcmp(physNames,paramName),1);
|
||||||
|
if isempty(sourceParamIdx)
|
||||||
|
error("mergeDataStorages:ParameterMismatch", ...
|
||||||
|
"Storage entry is missing parameter '%s'.",paramName);
|
||||||
|
end
|
||||||
|
targetSubscripts{param_idx} = whMerged.getIndexByPhys(paramName,physValues{sourceParamIdx});
|
||||||
|
end
|
||||||
|
|
||||||
|
if isscalar(targetSubscripts)
|
||||||
|
targetLinIdx = targetSubscripts{1};
|
||||||
|
else
|
||||||
|
targetLinIdx = sub2ind(whMerged.getStorageSize(),targetSubscripts{:});
|
||||||
|
end
|
||||||
|
|
||||||
|
existingValue = whMerged.sto.(storageName){targetLinIdx};
|
||||||
|
whMerged.sto.(storageName){targetLinIdx} = mergeStoredValue(existingValue,storedValue);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function out = mergeStoredValue(existingValue,newValue)
|
||||||
|
if isempty(existingValue)
|
||||||
|
out = newValue;
|
||||||
|
elseif iscell(existingValue) && iscell(newValue)
|
||||||
|
out = [existingValue(:); newValue(:)].';
|
||||||
|
else
|
||||||
|
out = newValue;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
@@ -127,7 +127,9 @@ for sirval = [20,30,50]
|
|||||||
pngTikzPath = "04_Experimental_Evaluation/tikz/mpi/" + exportName + "-1.png";
|
pngTikzPath = "04_Experimental_Evaluation/tikz/mpi/" + exportName + "-1.png";
|
||||||
|
|
||||||
figure(sirval + pathlen);
|
figure(sirval + pathlen);
|
||||||
exportNakedPlotWithTikz(gca, pngFile, tikzFile, pngTikzPath, 150);
|
exportLevelScatterTikz(gca, pngFile, tikzFile, pngTikzPath, ...
|
||||||
|
"resolutionDpi", 150, ...
|
||||||
|
"generatedBy", "plot_mpi_timesignal.m");
|
||||||
|
|
||||||
end
|
end
|
||||||
end
|
end
|
||||||
@@ -145,309 +147,3 @@ end
|
|||||||
% var_slope = p(1);
|
% var_slope = p(1);
|
||||||
% var_offset = p(2);c
|
% var_offset = p(2);c
|
||||||
|
|
||||||
function exportNakedPlotWithTikz(sourceAx, pngFile, tikzFile, pngTikzPath, resolutionDpi)
|
|
||||||
outputDir = fileparts(pngFile);
|
|
||||||
if ~exist(outputDir, "dir")
|
|
||||||
mkdir(outputDir);
|
|
||||||
end
|
|
||||||
|
|
||||||
exportScatterLayerPng(sourceAx, pngFile, resolutionDpi);
|
|
||||||
|
|
||||||
linePlots = collectTikzLinePlots(sourceAx);
|
|
||||||
textAnnotations = collectTikzTextAnnotations(sourceAx);
|
|
||||||
writeTikzPngAxis(tikzFile, pngTikzPath, sourceAx, linePlots, textAnnotations);
|
|
||||||
end
|
|
||||||
|
|
||||||
function exportScatterLayerPng(sourceAx, pngFile, resolutionDpi)
|
|
||||||
sourceFig = ancestor(sourceAx, "figure");
|
|
||||||
xLimits = sourceAx.XLim;
|
|
||||||
yLimits = sourceAx.YLim;
|
|
||||||
scatterHandles = findGraphicsObjectsByType(sourceAx, "scatter");
|
|
||||||
|
|
||||||
exportFig = figure( ...
|
|
||||||
"Visible", "off", ...
|
|
||||||
"Color", "white", ...
|
|
||||||
"Units", sourceFig.Units, ...
|
|
||||||
"Position", sourceFig.Position);
|
|
||||||
cleanupFigure = onCleanup(@() close(exportFig));
|
|
||||||
|
|
||||||
exportAx = copyobj(sourceAx, exportFig);
|
|
||||||
exportAx.Units = "normalized";
|
|
||||||
exportAx.Position = [0 0 1 1];
|
|
||||||
exportAx.XLim = xLimits;
|
|
||||||
exportAx.YLim = yLimits;
|
|
||||||
exportAx.XLimMode = "manual";
|
|
||||||
exportAx.YLimMode = "manual";
|
|
||||||
stripAxesToScatterOnly(exportAx);
|
|
||||||
hideAxesInkForRasterExport(exportAx);
|
|
||||||
|
|
||||||
fprintf("Exporting scatter PNG with XLim=[%g %g], YLim=[%g %g], scatter objects=%d: %s\n", ...
|
|
||||||
xLimits(1), xLimits(2), yLimits(1), yLimits(2), numel(scatterHandles), pngFile);
|
|
||||||
drawnow;
|
|
||||||
exportFig.PaperPositionMode = "auto";
|
|
||||||
print(exportFig, char(pngFile), "-dpng", sprintf("-r%d", resolutionDpi));
|
|
||||||
end
|
|
||||||
|
|
||||||
function stripAxesToScatterOnly(ax)
|
|
||||||
plotObjects = findall(ax);
|
|
||||||
for objIdx = 1:numel(plotObjects)
|
|
||||||
obj = plotObjects(objIdx);
|
|
||||||
if obj == ax || ~isvalid(obj)
|
|
||||||
continue
|
|
||||||
end
|
|
||||||
|
|
||||||
objType = string(get(obj, "Type"));
|
|
||||||
if lower(objType) ~= "scatter"
|
|
||||||
delete(obj);
|
|
||||||
end
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
function hideAxesInkForRasterExport(ax)
|
|
||||||
ax.Visible = "on";
|
|
||||||
ax.Color = "white";
|
|
||||||
ax.Box = "off";
|
|
||||||
ax.XGrid = "off";
|
|
||||||
ax.YGrid = "off";
|
|
||||||
ax.XMinorGrid = "off";
|
|
||||||
ax.YMinorGrid = "off";
|
|
||||||
ax.XTick = [];
|
|
||||||
ax.YTick = [];
|
|
||||||
ax.XColor = "white";
|
|
||||||
ax.YColor = "white";
|
|
||||||
ax.Title.String = "";
|
|
||||||
ax.XLabel.String = "";
|
|
||||||
ax.YLabel.String = "";
|
|
||||||
end
|
|
||||||
|
|
||||||
function handles = findGraphicsObjectsByType(parentHandle, objectType)
|
|
||||||
allHandles = findall(parentHandle);
|
|
||||||
matches = false(size(allHandles));
|
|
||||||
for handleIdx = 1:numel(allHandles)
|
|
||||||
currentHandle = allHandles(handleIdx);
|
|
||||||
if ~isvalid(currentHandle)
|
|
||||||
continue
|
|
||||||
end
|
|
||||||
|
|
||||||
matches(handleIdx) = lower(string(get(currentHandle, "Type"))) == lower(string(objectType));
|
|
||||||
end
|
|
||||||
|
|
||||||
handles = allHandles(matches);
|
|
||||||
end
|
|
||||||
|
|
||||||
function linePlots = collectTikzLinePlots(sourceAx)
|
|
||||||
lineHandles = findGraphicsObjectsByType(sourceAx, "line");
|
|
||||||
lineHandles = flipud(lineHandles(:));
|
|
||||||
|
|
||||||
linePlots = repmat(struct( ...
|
|
||||||
"xData", [], ...
|
|
||||||
"yData", [], ...
|
|
||||||
"color", [], ...
|
|
||||||
"lineWidth", []), numel(lineHandles), 1);
|
|
||||||
validLineCount = 0;
|
|
||||||
|
|
||||||
for lineIdx = 1:numel(lineHandles)
|
|
||||||
lineHandle = lineHandles(lineIdx);
|
|
||||||
xData = lineHandle.XData(:);
|
|
||||||
yData = lineHandle.YData(:);
|
|
||||||
validSamples = isfinite(xData) & isfinite(yData);
|
|
||||||
|
|
||||||
if ~any(validSamples)
|
|
||||||
continue
|
|
||||||
end
|
|
||||||
|
|
||||||
validLineCount = validLineCount + 1;
|
|
||||||
linePlots(validLineCount).xData = xData(validSamples);
|
|
||||||
linePlots(validLineCount).yData = yData(validSamples);
|
|
||||||
linePlots(validLineCount).color = lineHandle.Color;
|
|
||||||
linePlots(validLineCount).lineWidth = lineHandle.LineWidth;
|
|
||||||
end
|
|
||||||
|
|
||||||
linePlots = linePlots(1:validLineCount);
|
|
||||||
end
|
|
||||||
|
|
||||||
function textAnnotations = collectTikzTextAnnotations(sourceAx)
|
|
||||||
textHandles = findGraphicsObjectsByType(sourceAx, "text");
|
|
||||||
textHandles = flipud(textHandles(:));
|
|
||||||
xLimits = sourceAx.XLim;
|
|
||||||
yLimits = sourceAx.YLim;
|
|
||||||
|
|
||||||
textAnnotations = repmat(struct( ...
|
|
||||||
"x", [], ...
|
|
||||||
"y", [], ...
|
|
||||||
"text", "", ...
|
|
||||||
"anchor", "center"), numel(textHandles), 1);
|
|
||||||
validTextCount = 0;
|
|
||||||
|
|
||||||
for textIdx = 1:numel(textHandles)
|
|
||||||
textHandle = textHandles(textIdx);
|
|
||||||
labelText = string(textHandle.String);
|
|
||||||
if strlength(strtrim(labelText)) == 0
|
|
||||||
continue
|
|
||||||
end
|
|
||||||
|
|
||||||
textPosition = textHandle.Position;
|
|
||||||
if textPosition(1) < xLimits(1) || textPosition(1) > xLimits(2) || ...
|
|
||||||
textPosition(2) < yLimits(1) || textPosition(2) > yLimits(2)
|
|
||||||
continue
|
|
||||||
end
|
|
||||||
|
|
||||||
validTextCount = validTextCount + 1;
|
|
||||||
textAnnotations(validTextCount).x = textPosition(1);
|
|
||||||
textAnnotations(validTextCount).y = textPosition(2);
|
|
||||||
textAnnotations(validTextCount).text = labelText;
|
|
||||||
textAnnotations(validTextCount).anchor = matlabTextAlignmentToTikzAnchor( ...
|
|
||||||
textHandle.HorizontalAlignment, textHandle.VerticalAlignment);
|
|
||||||
end
|
|
||||||
|
|
||||||
textAnnotations = textAnnotations(1:validTextCount);
|
|
||||||
end
|
|
||||||
|
|
||||||
function writeTikzPngAxis(tikzFile, pngTikzPath, sourceAx, linePlots, textAnnotations)
|
|
||||||
outputDir = fileparts(tikzFile);
|
|
||||||
if ~exist(outputDir, "dir")
|
|
||||||
mkdir(outputDir);
|
|
||||||
end
|
|
||||||
|
|
||||||
xLimits = sourceAx.XLim;
|
|
||||||
yLimits = sourceAx.YLim;
|
|
||||||
fid = fopen(tikzFile, "w");
|
|
||||||
if fid < 0
|
|
||||||
error("plot_mpi_timesignal:TikzOpenFailed", ...
|
|
||||||
"Could not open TikZ export file: %s", tikzFile);
|
|
||||||
end
|
|
||||||
cleanupFile = onCleanup(@() fclose(fid));
|
|
||||||
|
|
||||||
axisOptions = {
|
|
||||||
" every axis/.append style={font=\scriptsize},"
|
|
||||||
"width=\fwidth,"
|
|
||||||
"height=\fheight,"
|
|
||||||
"at={(0\fwidth,0\fheight)},"
|
|
||||||
"scale only axis,"
|
|
||||||
"axis on top,"
|
|
||||||
"xmin=" + formatPgfNumber(xLimits(1)) + ","
|
|
||||||
"xmax=" + formatPgfNumber(xLimits(2)) + ","
|
|
||||||
"xlabel style={font=\color{white!15!black}\small},"
|
|
||||||
"xlabel={Time in $\mu$s},"
|
|
||||||
"ymin=" + formatPgfNumber(yLimits(1)) + ","
|
|
||||||
"ymax=" + formatPgfNumber(yLimits(2)) + ","
|
|
||||||
"ylabel style={font=\color{white!15!black}\small},"
|
|
||||||
"ylabel={Normalized Amplitude},"
|
|
||||||
"axis background/.style={fill=white},"
|
|
||||||
"xmajorgrids,"
|
|
||||||
"ymajorgrids,"
|
|
||||||
"grid style={line width=0.4pt, dotted, color=black!20},"
|
|
||||||
"scaled ticks=false,"
|
|
||||||
"tick label style={/pgf/number format/fixed, /pgf/number format/1000 sep={}},"
|
|
||||||
"legend columns=1"
|
|
||||||
};
|
|
||||||
|
|
||||||
fprintf(fid, "%% This file was generated by plot_mpi_timesignal.m.\n");
|
|
||||||
fprintf(fid, "%%\n");
|
|
||||||
for lineIdx = 1:numel(linePlots)
|
|
||||||
fprintf(fid, "%s\n", formatTikzColorDefinition(lineIdx, linePlots(lineIdx).color));
|
|
||||||
end
|
|
||||||
if ~isempty(linePlots)
|
|
||||||
fprintf(fid, "%%\n");
|
|
||||||
end
|
|
||||||
fprintf(fid, "%s\n\n", "\begin{tikzpicture}");
|
|
||||||
fprintf(fid, "%s\n", "\begin{axis}[%");
|
|
||||||
for optionIdx = 1:numel(axisOptions)
|
|
||||||
fprintf(fid, "%s\n", axisOptions{optionIdx});
|
|
||||||
end
|
|
||||||
fprintf(fid, "%s\n", "]");
|
|
||||||
|
|
||||||
graphicsLine = sprintf( ...
|
|
||||||
"\\addplot [forget plot] graphics [xmin=%s, xmax=%s, ymin=%s, ymax=%s] {%s};", ...
|
|
||||||
formatPgfNumber(xLimits(1)), ...
|
|
||||||
formatPgfNumber(xLimits(2)), ...
|
|
||||||
formatPgfNumber(yLimits(1)), ...
|
|
||||||
formatPgfNumber(yLimits(2)), ...
|
|
||||||
char(strrep(pngTikzPath, "\", "/")));
|
|
||||||
fprintf(fid, "%s\n\n", graphicsLine);
|
|
||||||
writeTikzLinePlots(fid, linePlots);
|
|
||||||
writeTikzTextAnnotations(fid, textAnnotations);
|
|
||||||
fprintf(fid, "%s\n\n", "\end{axis}");
|
|
||||||
fprintf(fid, "%s", "\end{tikzpicture}%");
|
|
||||||
end
|
|
||||||
|
|
||||||
function valueText = formatPgfNumber(value)
|
|
||||||
valueText = string(sprintf("%.15g", value));
|
|
||||||
end
|
|
||||||
|
|
||||||
function colorDefinition = formatTikzColorDefinition(colorIdx, rgbColor)
|
|
||||||
colorDefinition = sprintf( ...
|
|
||||||
"\\definecolor{mycolor%d}{rgb}{%.5f,%.5f,%.5f}%%", ...
|
|
||||||
colorIdx, rgbColor(1), rgbColor(2), rgbColor(3));
|
|
||||||
end
|
|
||||||
|
|
||||||
function writeTikzLinePlots(fid, linePlots)
|
|
||||||
for lineIdx = 1:numel(linePlots)
|
|
||||||
fprintf(fid, "\\addplot [color=mycolor%d, line width=%.1fpt, forget plot]\n", ...
|
|
||||||
lineIdx, linePlots(lineIdx).lineWidth);
|
|
||||||
fprintf(fid, " table[row sep=crcr]{%%\n");
|
|
||||||
|
|
||||||
for sampleIdx = 1:numel(linePlots(lineIdx).xData)
|
|
||||||
fprintf(fid, "%s\t%s\\\\\n", ...
|
|
||||||
formatPgfNumber(linePlots(lineIdx).xData(sampleIdx)), ...
|
|
||||||
formatPgfNumber(linePlots(lineIdx).yData(sampleIdx)));
|
|
||||||
end
|
|
||||||
|
|
||||||
fprintf(fid, "};\n\n");
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
function writeTikzTextAnnotations(fid, textAnnotations)
|
|
||||||
for textIdx = 1:numel(textAnnotations)
|
|
||||||
fprintf(fid, "\\node[font=\\scriptsize, anchor=%s, fill=white, draw=black!40, rounded corners=2pt, inner sep=2pt]\n", ...
|
|
||||||
textAnnotations(textIdx).anchor);
|
|
||||||
fprintf(fid, " at (axis cs:%s,%s) {%s};\n\n", ...
|
|
||||||
formatPgfNumber(textAnnotations(textIdx).x), ...
|
|
||||||
formatPgfNumber(textAnnotations(textIdx).y), ...
|
|
||||||
escapeTikzText(textAnnotations(textIdx).text));
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
function anchor = matlabTextAlignmentToTikzAnchor(horizontalAlignment, verticalAlignment)
|
|
||||||
horizontalAlignment = string(horizontalAlignment);
|
|
||||||
verticalAlignment = string(verticalAlignment);
|
|
||||||
|
|
||||||
if horizontalAlignment == "left"
|
|
||||||
horizontalAnchor = "west";
|
|
||||||
elseif horizontalAlignment == "right"
|
|
||||||
horizontalAnchor = "east";
|
|
||||||
else
|
|
||||||
horizontalAnchor = "";
|
|
||||||
end
|
|
||||||
|
|
||||||
if verticalAlignment == "top"
|
|
||||||
verticalAnchor = "north";
|
|
||||||
elseif verticalAlignment == "bottom"
|
|
||||||
verticalAnchor = "south";
|
|
||||||
else
|
|
||||||
verticalAnchor = "";
|
|
||||||
end
|
|
||||||
|
|
||||||
if strlength(horizontalAnchor) > 0 && strlength(verticalAnchor) > 0
|
|
||||||
anchor = verticalAnchor + " " + horizontalAnchor;
|
|
||||||
elseif strlength(horizontalAnchor) > 0
|
|
||||||
anchor = horizontalAnchor;
|
|
||||||
elseif strlength(verticalAnchor) > 0
|
|
||||||
anchor = verticalAnchor;
|
|
||||||
else
|
|
||||||
anchor = "center";
|
|
||||||
end
|
|
||||||
end
|
|
||||||
|
|
||||||
function textOut = escapeTikzText(textIn)
|
|
||||||
textOut = char(textIn);
|
|
||||||
% textOut = strrep(textOut, "\", "\textbackslash{}");
|
|
||||||
textOut = strrep(textOut, "{", "\{");
|
|
||||||
textOut = strrep(textOut, "}", "\}");
|
|
||||||
textOut = strrep(textOut, "%", "\%");
|
|
||||||
textOut = strrep(textOut, "&", "\&");
|
|
||||||
textOut = strrep(textOut, "#", "\#");
|
|
||||||
textOut = strrep(textOut, "_", "\_");
|
|
||||||
% textOut = strrep(textOut, "$", "\$");
|
|
||||||
end
|
|
||||||
|
|
||||||
|
|||||||
BIN
projects/Diss/MPI_revisit/workerError.mat
Normal file
BIN
projects/Diss/MPI_revisit/workerError.mat
Normal file
Binary file not shown.
@@ -133,7 +133,7 @@ for i = 1:numel(fsym_values)
|
|||||||
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
|
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
|
||||||
"mu_dd",1e-3,"mu_tr",0.4,"order",50, ...
|
"mu_dd",1e-3,"mu_tr",0.4,"order",50, ...
|
||||||
"sps",2,"decide",0,"optmize_mus",0,"dd_mode",1, ...
|
"sps",2,"decide",0,"optmize_mus",0,"dd_mode",1, ...
|
||||||
"adaption_technique","nlms","mu_dc",1e-3);
|
"adaption_technique","nlms","dc_tracking_mu",1e-3);
|
||||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||||
|
|
||||||
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
||||||
|
|||||||
@@ -134,7 +134,7 @@ for i = 1:numel(fsym_values)
|
|||||||
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
|
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
|
||||||
"mu_dd",1e-1,"mu_tr",0.4,"order",50, ...
|
"mu_dd",1e-1,"mu_tr",0.4,"order",50, ...
|
||||||
"sps",2,"decide",0,"optmize_mus",1,"dd_mode",1, ...
|
"sps",2,"decide",0,"optmize_mus",1,"dd_mode",1, ...
|
||||||
"adaption_technique","nlms","mu_dc",1.021e-05);
|
"adaption_technique","nlms","dc_tracking_mu",1.021e-05);
|
||||||
case "VNLE"
|
case "VNLE"
|
||||||
eq_ = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
|
eq_ = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
|
||||||
"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0, ...
|
"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0, ...
|
||||||
|
|||||||
@@ -1,49 +1,10 @@
|
|||||||
|
|
||||||
|
|
||||||
% dsp_options.database_type = 'mysql';
|
|
||||||
% dsp_options.dataBase = 'labor';
|
|
||||||
% dsp_options.storage_path = 'Z:\2025\ECOC Silas\ecoc_2025\';
|
|
||||||
% database = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
|
|
||||||
% filterParams = database.tables;
|
|
||||||
% filterParams.Runs.loop_id = 209;
|
|
||||||
% % filterParams.Configurations = struct( ...
|
|
||||||
% % 'symbolrate', 112e9, ... %[224,336,360,390,420,448]
|
|
||||||
% % 'fiber_length', 0, ...
|
|
||||||
% % 'db_mode', '"no_db"', ...
|
|
||||||
% % 'interference_attenuation', [], ...
|
|
||||||
% % 'interference_path_length', 1000, ...
|
|
||||||
% % 'is_mpi', 1, ...
|
|
||||||
% % 'pam_level', 4, ...
|
|
||||||
% % 'wavelength', 1310, ...
|
|
||||||
% % 'precomp_amp', [], ...
|
|
||||||
% % 'signal_attenuation', [], ...
|
|
||||||
% % 'v_awg', [], ...
|
|
||||||
% % 'v_bias', [] ...
|
|
||||||
% % );
|
|
||||||
%
|
|
||||||
% % if 1
|
|
||||||
% % % filterParams.EqualizerParameters.dc_buffer_len = 1;
|
|
||||||
% % filterParams.EqualizerParameters.ffe_buffer_len = 1;
|
|
||||||
% % filterParams.EqualizerParameters.smoothing_buffer_len = 4096;
|
|
||||||
% % filterParams.EqualizerParameters.smoothing_buffer_update = 224;
|
|
||||||
% % filterParams.EqualizerParameters.DCmu = 0;
|
|
||||||
% % end
|
|
||||||
% a = database.getTableFieldNames('Runs');
|
|
||||||
% b = database.getTableFieldNames('Results');
|
|
||||||
% c = database.getTableFieldNames('EqualizerParameters');
|
|
||||||
% d = [a;b;c];
|
|
||||||
%
|
|
||||||
% [dataTable,~] = database.queryDB(filterParams, d);
|
|
||||||
%
|
|
||||||
% selectedFields = {'Configurations.run_id' 'Runs.loop_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Runs.bitrate' 'Runs.v_bias' 'Runs.v_awg' 'Runs.precomp_amp' 'Runs.symbolrate' 'Runs.pam_level'...
|
|
||||||
% 'Runs.db_mode' 'Runs.rop_attenuation' 'Runs.is_mpi' 'Runs.interference_attenuation' 'Runs.interference_path_length' 'Runs.signal_attenuation' ...
|
|
||||||
% 'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'EqualizerParameters.dc_buffer_len' 'EqualizerParameters.ffe_buffer_len' 'EqualizerParameters.smoothing_buffer_len' 'EqualizerParameters.smoothing_buffer_update' 'EqualizerParameters.DCmu' 'Measurements.power_pd_in' ...
|
|
||||||
% 'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.EVM' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'};
|
|
||||||
|
|
||||||
db = DBHandler("type","mysql","dataBase",'labor');
|
db = DBHandler("type","mysql","dataBase",'labor');
|
||||||
|
|
||||||
fp = QueryFilter();
|
fp = QueryFilter();
|
||||||
% fp.where('mpi_superview', 'loop_id','EQUALS', 209);
|
% fp.where('mpi_superview', 'loop_id','EQUALS', 209);
|
||||||
|
% fp.where('mpi_superview','run_id','GREATER_EQUAL',3153);
|
||||||
fp.where('mpi_superview', 'symbolrate','EQUALS', 112e9);
|
fp.where('mpi_superview', 'symbolrate','EQUALS', 112e9);
|
||||||
fp.where('mpi_superview', 'pam_level','EQUALS', 4);
|
fp.where('mpi_superview', 'pam_level','EQUALS', 4);
|
||||||
fn = [db.getTableFieldNames('mpi_superview')];
|
fn = [db.getTableFieldNames('mpi_superview')];
|
||||||
@@ -67,7 +28,7 @@ figure()
|
|||||||
tiledlayout(1, 1, 'TileSpacing', 'compact', 'Padding', 'compact');
|
tiledlayout(1, 1, 'TileSpacing', 'compact', 'Padding', 'compact');
|
||||||
y_here = 0;
|
y_here = 0;
|
||||||
figcnt = 0;
|
figcnt = 0;
|
||||||
for int_len = 1000%[0,50,300,1000]
|
for int_len = 1%[0,50,300,1000]
|
||||||
figcnt = figcnt+1;
|
figcnt = figcnt+1;
|
||||||
% figure(int_len+1);
|
% figure(int_len+1);
|
||||||
nexttile;
|
nexttile;
|
||||||
@@ -139,7 +100,7 @@ for int_len = 1000%[0,50,300,1000]
|
|||||||
% Modify values in 'interference_path_length' where the condition is met
|
% Modify values in 'interference_path_length' where the condition is met
|
||||||
dataTable.interference_path_length(dataTable.interference_path_length < 101 & dataTable.interference_path_length > 1) = 50;
|
dataTable.interference_path_length(dataTable.interference_path_length < 101 & dataTable.interference_path_length > 1) = 50;
|
||||||
|
|
||||||
dataTable.interference_path_length(dataTable.interference_path_length == 1) = 0;
|
% dataTable.interference_path_length(dataTable.interference_path_length == 1) = 0;
|
||||||
dataTable = dataTable(dataTable.interference_path_length == int_len, :);
|
dataTable = dataTable(dataTable.interference_path_length == int_len, :);
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
BIN
workerError.mat
BIN
workerError.mat
Binary file not shown.
Reference in New Issue
Block a user