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während Diss, 400G plots

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This commit is contained in:
Silas Oettinghaus
2026-08-13 10:22:01 +02:00
parent 7a9deaeb0c
commit c77327d39f
27 changed files with 1769 additions and 248 deletions
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4
projects/MLSE_ML_based/minimal_example_huawei/ml_mlse_pam.m
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@@ -210,8 +210,8 @@ classdef ml_mlse_pam < handle
% ==============================================================
% ML-Based Branch Metric Estimation + Viterbi
% ==============================================================
% debug = 1;
% showPlots = 1;
debug = 0;
showPlots = 0;
nSymbols = ceil(N/obj.sps);

72
projects/MLSE_ML_based/theoretic_channel_evaluation.m
View File

@@ -47,7 +47,7 @@ ber_ml_mlse_l4 = zeros(size(SNR_dB));
epochs_training = 100;
parfor i = 1:numel(SNR_dB)
for i = 1:numel(SNR_dB)
symbols_noi = symbols_filt;
symbols_noi.signal = awgn(symbols_filt.signal, SNR_dB(i), 'measured'); % AWGN with given SNR
@@ -73,46 +73,46 @@ parfor i = 1:numel(SNR_dB)
[~, ~, ber_ffe(i), ~] = calc_ber(Eq_bits.signal, Bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
fprintf('FFE: %.2e \n',ber_ffe(i));
% Postfilter
[y_white,~] = pf_.process(y_ffe, ffe_noise);
% Sequence Est
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',0,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0,'DIR',pf_.coefficients);
[y_mlse] = mlse_.process(y_white,Symbols);
mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_mlse);
[~, errors, ber_nwf_mlse_l2(i), errpos] = calc_ber(mlse_bits.signal, Bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
fprintf('MLSE: %.2e \n',ber_nwf_mlse_l2(i));
% ML-base MLSE L=2
adaptive_mu = 0;
mu_lms = 0.15;
ml_mlse_equalizer = ML_MLSE("epochs_tr",epochs_training,"epochs_dd",1,"len_tr",2^15,...
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",11,"sps",1,...
"traceback_depth",128,"L",2,"delta",4,"adaptive_mu",adaptive_mu);
[y_ml_mlse,~] = ml_mlse_equalizer.process(symbols_noi,Symbols);
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
[~, errors, ber_ml_mlse_l2(i), errpos] = calc_ber(ml_mlse_bits.signal, Bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse_l2(i));
% ML-base MLSE L=3
mu_lms = 0.15;
ml_mlse_equalizer = ML_MLSE("epochs_tr",epochs_training,"epochs_dd",1,"len_tr",2^16,...
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",11,"sps",1,...
"traceback_depth",128,"L",3,"delta",4,"adaptive_mu",adaptive_mu);
[y_ml_mlse,~] = ml_mlse_equalizer.process(symbols_noi,Symbols);
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
[~, errors, ber_ml_mlse_l3(i), errpos] = calc_ber(ml_mlse_bits.signal, Bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse_l3(i));
% % ML-base MLSE L=5
% % Postfilter
% [y_white,~] = pf_.process(y_ffe, ffe_noise);
%
% % Sequence Est
% mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',0,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0,'DIR',pf_.coefficients);
% [y_mlse] = mlse_.process(y_white,Symbols);
% mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_mlse);
% [~, errors, ber_nwf_mlse_l2(i), errpos] = calc_ber(mlse_bits.signal, Bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
% fprintf('MLSE: %.2e \n',ber_nwf_mlse_l2(i));
%
% % ML-base MLSE L=2
% adaptive_mu = 0;
% mu_lms = 0.15;
% ml_mlse_equalizer = ML_MLSE("epochs_tr",epochs_training,"epochs_dd",1,"len_tr",2^15,...
% "mu_dd",mu_lms,"mu_tr",mu_lms,"order",11,"sps",1,...
% "traceback_depth",128,"L",5,"delta",4);
% "traceback_depth",128,"L",2,"delta",4,"adaptive_mu",adaptive_mu);
% [y_ml_mlse,~] = ml_mlse_equalizer.process(symbols_noi,Symbols);
% ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
% [~, errors, ber_ml_mlse_l5(i), errpos] = calc_ber(ml_mlse_bits.signal, Bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
% fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse_l5(i));
% [~, errors, ber_ml_mlse_l2(i), errpos] = calc_ber(ml_mlse_bits.signal, Bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
% fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse_l2(i));
%
% % ML-base MLSE L=3
% mu_lms = 0.15;
% ml_mlse_equalizer = ML_MLSE("epochs_tr",epochs_training,"epochs_dd",1,"len_tr",2^16,...
% "mu_dd",mu_lms,"mu_tr",mu_lms,"order",11,"sps",1,...
% "traceback_depth",128,"L",3,"delta",4,"adaptive_mu",adaptive_mu);
% [y_ml_mlse,~] = ml_mlse_equalizer.process(symbols_noi,Symbols);
% ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
% [~, errors, ber_ml_mlse_l3(i), errpos] = calc_ber(ml_mlse_bits.signal, Bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
% fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse_l3(i));
% ML-base MLSE L=5
mu_lms = 0.15;
ml_mlse_equalizer = ML_MLSE("epochs_tr",epochs_training,"epochs_dd",1,"len_tr",2^15,...
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",11,"sps",1,...
"traceback_depth",128,"L",5,"delta",4);
[y_ml_mlse,~] = ml_mlse_equalizer.process(symbols_noi,Symbols);
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
[~, errors, ber_ml_mlse_l5(i), errpos] = calc_ber(ml_mlse_bits.signal, Bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse_l5(i));
end