114 lines
3.3 KiB
Matlab
114 lines
3.3 KiB
Matlab
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M = 4;
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order = 19;
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randkey = 1;
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bitpattern = [];
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s = RandStream('twister','Seed',randkey);
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for i = 1:log2(M)
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N = 2^(order-1); %length of prbs
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bitpattern(:,i) = randi(s,[0 1], N, 1);
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end
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if M == 6
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bitpattern = reshape(bitpattern',[],1);
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bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
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end
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Bits = Informationsignal(bitpattern);
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Symbols = PAMmapper(M,0).map(Bits);
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Symbols.fs = 200e9;
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Bits_ = PAMmapper(M, 0, "eth_style", 0).demap(Symbols);
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% --- Channel: minimal ISI response + AWGN ---
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h = [0.3 0.9 0.3]; % impulse response (normalized later if desired)
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h = h / norm(h); % optional normalization for unit energy
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symbols_filt = Symbols.filter(h,1);
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%% SHOW Loss during training
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mu = logspace(-3,-0.8,12);
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ber_ml_mlse = zeros(size(mu));
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ber_training = [];
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ce_training = [];
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parfor i = 1:numel(mu)
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symbols_noi = symbols_filt;
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SNR_dB = 20;
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symbols_noi.signal = awgn(symbols_filt.signal, SNR_dB, 'measured'); % AWGN with given SNR
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ml_mlse_equalizer = ML_MLSE("epochs_tr",200,"epochs_dd",1,"len_tr",2^15,...
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"mu_dd",mu(i),"mu_tr",mu(i),"order",5,"sps",1,...
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"traceback_depth",128,"L",3,"delta",0,'adaptive_mu',0);
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[y_ml_mlse,y_ref] = ml_mlse_equalizer.process(symbols_noi,Symbols);
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ref_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ref);
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ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
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[~, errors, ber_ml_mlse(i), errpos] = calc_ber(ml_mlse_bits.signal, ref_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
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fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse(i));
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ber_training(i,:) = ml_mlse_equalizer.ber;
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ce_training(i,:) = ml_mlse_equalizer.ce;
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end
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%%
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symbols_noi = symbols_filt;
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SNR_dB = 20;
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symbols_noi.signal = awgn(symbols_filt.signal, SNR_dB, 'measured'); % AWGN with given SNR
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ml_mlse_equalizer_adap = ML_MLSE("epochs_tr",200,"epochs_dd",1,"len_tr",2^16,...
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"mu_dd",1,"mu_tr",1,"order",5,"sps",1,...
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"traceback_depth",128,"L",3,"delta",0,"adaptive_mu",1);
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[y_ml_mlse,y_ref] = ml_mlse_equalizer_adap.process(symbols_noi,Symbols);
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ref_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ref);
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ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
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[~, errors, ber_ml_mlse_, errpos] = calc_ber(ml_mlse_bits.signal, ref_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
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fprintf('ML MLSE BER: %.2e \n',ber_ml_mlse_);
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%%
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figure();hold on
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plot(mu,ber_ml_mlse,'DisplayName','ML-based MLSE');
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beautifyBERplot;
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xlim([mu(1), mu(end)]);
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xlabel('mu');
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ylabel('BER');
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title('PAM-4; M=3; AWGN Channel');
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ylim([1e-5 0.1]);
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%%
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figure()
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hold on;
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cols = cbrewer2('Spectral',12);
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for i = 1:12
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plot(1:200,ber_training(i,:),'DisplayName', sprintf('mu=%.3f ',mu(i)),'Color',cols(i,:));
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end
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set(gca,'YScale','log');
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xlabel('Epoch');
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ylabel('BER');
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title('PAM-4; L=3; SNR=20; AWGN Channel');
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plot(1:200,ml_mlse_equalizer_adap.ber,'DisplayName','Adaptive mu');
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%%
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figure()
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hold on;
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cols = cbrewer2('Spectral',12);
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for i = 1:12
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plot(1:200,ce_training(i,:),'DisplayName', sprintf('mu=%.3f ',mu(i)),'Color',cols(i,:));
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end
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set(gca,'YScale','log');
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xlabel('Epoch');
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ylabel('Cross-Entropy');
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title('PAM-4; L=3; SNR=20; AWGN Channel');
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plot(1:200,ml_mlse_equalizer_adap.ce,'DisplayName','Adaptive mu');
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%% SUPER LONG EPOCHS
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