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ML Equalizer works now.

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Not yet perfectly integrated into all the routines
This commit is contained in:
Silas Oettinghaus
2025-11-12 09:24:02 +01:00
parent 39bc8243fc
commit 0080cb2264
44 changed files with 5289 additions and 2868 deletions
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113
projects/ML_based_MLSE/analyze_mu.m Normal file
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M = 4;
order = 19;
randkey = 1;
bitpattern = [];
s = RandStream('twister','Seed',randkey);
for i = 1:log2(M)
N = 2^(order-1); %length of prbs
bitpattern(:,i) = randi(s,[0 1], N, 1);
end
if M == 6
bitpattern = reshape(bitpattern',[],1);
bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
end
Bits = Informationsignal(bitpattern);
Symbols = PAMmapper(M,0).map(Bits);
Symbols.fs = 200e9;
Bits_ = PAMmapper(M, 0, "eth_style", 0).demap(Symbols);
% --- Channel: minimal ISI response + AWGN ---
h = [0.3 0.9 0.3]; % impulse response (normalized later if desired)
h = h / norm(h); % optional normalization for unit energy
symbols_filt = Symbols.filter(h,1);
%% SHOW Loss during training
mu = logspace(-3,-0.8,12);
ber_ml_mlse = zeros(size(mu));
ber_training = [];
ce_training = [];
parfor i = 1:numel(mu)
symbols_noi = symbols_filt;
SNR_dB = 20;
symbols_noi.signal = awgn(symbols_filt.signal, SNR_dB, 'measured'); % AWGN with given SNR
ml_mlse_equalizer = ML_MLSE("epochs_tr",200,"epochs_dd",1,"len_tr",2^15,...
"mu_dd",mu(i),"mu_tr",mu(i),"order",5,"sps",1,...
"traceback_depth",128,"L",3,"delta",0,'adaptive_mu',0);
[y_ml_mlse,y_ref] = ml_mlse_equalizer.process(symbols_noi,Symbols);
ref_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ref);
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
[~, errors, ber_ml_mlse(i), errpos] = calc_ber(ml_mlse_bits.signal, ref_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse(i));
ber_training(i,:) = ml_mlse_equalizer.ber;
ce_training(i,:) = ml_mlse_equalizer.ce;
end
%%
symbols_noi = symbols_filt;
SNR_dB = 20;
symbols_noi.signal = awgn(symbols_filt.signal, SNR_dB, 'measured'); % AWGN with given SNR
ml_mlse_equalizer_adap = ML_MLSE("epochs_tr",200,"epochs_dd",1,"len_tr",2^16,...
"mu_dd",1,"mu_tr",1,"order",5,"sps",1,...
"traceback_depth",128,"L",3,"delta",0,"adaptive_mu",1);
[y_ml_mlse,y_ref] = ml_mlse_equalizer_adap.process(symbols_noi,Symbols);
ref_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ref);
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
[~, errors, ber_ml_mlse_, errpos] = calc_ber(ml_mlse_bits.signal, ref_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse_);
%%
figure();hold on
plot(mu,ber_ml_mlse,'DisplayName','ML-based MLSE');
beautifyBERplot;
xlim([mu(1), mu(end)]);
xlabel('mu');
ylabel('BER');
title('PAM-4; M=3; AWGN Channel');
ylim([1e-5 0.1]);
%%
figure()
hold on;
cols = cbrewer2('Spectral',12);
for i = 1:12
plot(1:200,ber_training(i,:),'DisplayName', sprintf('mu=%.3f ',mu(i)),'Color',cols(i,:));
end
set(gca,'YScale','log');
xlabel('Epoch');
ylabel('BER');
title('PAM-4; L=3; SNR=20; AWGN Channel');
plot(1:200,ml_mlse_equalizer_adap.ber,'DisplayName','Adaptive mu');
%%
figure()
hold on;
cols = cbrewer2('Spectral',12);
for i = 1:12
plot(1:200,ce_training(i,:),'DisplayName', sprintf('mu=%.3f ',mu(i)),'Color',cols(i,:));
end
set(gca,'YScale','log');
xlabel('Epoch');
ylabel('Cross-Entropy');
title('PAM-4; L=3; SNR=20; AWGN Channel');
plot(1:200,ml_mlse_equalizer_adap.ce,'DisplayName','Adaptive mu');
%% SUPER LONG EPOCHS