CLEANUP - changes to folder structure
This commit is contained in:
Silas Oettinghaus
163
projects/MLSE_ML_based/experimental_data.m
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163
projects/MLSE_ML_based/experimental_data.m
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dsp_options.storage_path = 'Z:\2024\sioe_labor\';
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dsp_options.max_occurences = 1;
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database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
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run_id = 2776;
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dataTable = queryRunid(run_id, database);
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fsym = dataTable.symbolrate;
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M = double(dataTable.pam_level);
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duob_mode = db_mode(strrep(dataTable.db_mode,'"',''));
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% if database.checkIfRunExists('Results','run_id',run_id)
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% disp(['Already got at least one reulst for run id: ',num2str(run_id),' '])
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% return
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% end
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% Load and Sync signal data from DB
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[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
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% Preprocess signal
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Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
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Scpe_sig.spectrum("fignum",1,"displayname",'Rx')
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%%
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ffe_order = [50, 5, 5];
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mu_ffe = [0.0001, 0.0008, 0.001];
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mu_dfe = 0.0004;
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eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^14,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
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mlse_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',3);
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%Duobinary Targeting
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db_ref_sequence = Duobinary().encode(Symbols);
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db_ref_constellation = unique(db_ref_sequence.signal);
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[eq_signal, eq_noise] = eq_.process(Scpe_sig,db_ref_sequence);
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%%
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if 1
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[mlse_sig_sd,LLR,GMI_MLSE] = mlse_.process(eq_signal,Symbols);
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else
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% Ml MLSE
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ml_mlse_equalizer = ML_MLSE("epochs_tr",20,"epochs_dd",1,"len_tr",length(eq_signal),...
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"mu_dd",0.01,"mu_tr",0.01,"order",11,"sps",2,...
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"traceback_depth",128,"L",1,"delta",4,'adaptive_mu',0);
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[mlse_sig_sd,ref_sig] = ml_mlse_equalizer.process(Scpe_sig,db_ref_sequence);
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end
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%%
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mlse_sig_sd_decoded = Duobinary().decode(mlse_sig_sd,"M",M);
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ref_sig_decoded = Duobinary().decode(db_ref_sequence,"M",M);
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mlse_sig_bits = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_sd_decoded);
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ref_sig_bits = PAMmapper(M,0,"eth_style",0).demap(ref_sig_decoded);
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err = sum(ref_sig_decoded.signal ~= mlse_sig_sd_decoded.signal);
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[bits_db,errors_db,ber_db,a] = calc_ber(mlse_sig_bits.signal,ref_sig_bits.signal,"skip_front",0,"skip_end",0,"returnErrorLocation",1);
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%%
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switch duob_mode
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case db_mode.no_db
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% TX Data is not precoded:
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% A) Emulate diff precoding
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mlse_sig_hd_precoded = Duobinary().encode(mlse_sig_hd,"M",M);
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mlse_sig_hd_precoded = Duobinary().decode(mlse_sig_hd_precoded,"M",M);
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tx_symbols_precoded = Duobinary().encode(Symbols);
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tx_symbols_precoded = Duobinary().decode(tx_symbols_precoded);
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tx_bits_precoded = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(tx_symbols_precoded);
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rx_bits_mlse = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(mlse_sig_hd_precoded);
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[~,errors_db_diff_precoded,ber_db_diff_precoded,~] = calc_ber(rx_bits_mlse.signal,tx_bits_precoded.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
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%B) Just determine BER
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rx_bits_mlse = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(mlse_sig_hd);
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[bits_mlse,errors_mlse,ber_db,~] = calc_ber(rx_bits_mlse.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
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case db_mode.db_precoded
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% Daten SIND TATSÄCHLICH precoded auf TX Seite:
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% A) Decode at Rx if no DB targeting was applied (we are in VNLE or MLSE EQ structure here!
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mlse_sig_hd_decoded = Duobinary().encode(mlse_sig_hd,"M",M);
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mlse_sig_hd_decoded = Duobinary().decode(mlse_sig_hd_decoded,"M",M);
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rx_bits_mlse_decoded = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_hd_decoded);
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[~,errors_db_diff_precoded,ber_db_diff_precoded,a] = calc_ber(rx_bits_mlse_decoded.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
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burst_db_precoded = count_error_bursts(a, 40);
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% B) Omit the Coding by comparing with demapped TX symbol sequence
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Tx_bits_ = PAMmapper(M,0,"eth_style",0).demap(Symbols);
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rx_bits_mlse = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_hd);
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[bits_db,errors_db,ber_db,a] = calc_ber(rx_bits_mlse.signal,Tx_bits_.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
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burst_db = count_error_bursts(a, 40);
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cols = linspecer(8);
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figure();hold on;
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stem(1:40,burst_db,'LineWidth',1,'Color',cols(4,:),'Marker','_','DisplayName','w/o diff. precoder');
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stem(1:40,burst_db_precoded,'LineWidth',1,'Color',cols(3,:),'Marker','.','LineStyle','-','DisplayName','w diff. precoder');
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xlabel('Bit Error Burst Length')
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ylabel('Occurence')
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set(gca, 'yscale', 'log');
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end
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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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ml_mlse_equalizer = ML_MLSE("epochs_tr",200,"epochs_dd",1,"len_tr",length(Scpe_sig),...
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"mu_dd",mu(i),"mu_tr",mu(i),"order",11,"sps",2,...
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"traceback_depth",128,"L",2,"delta",4,'adaptive_mu',0);
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[y_ml_mlse,y_ref] = ml_mlse_equalizer.process(Scpe_sig,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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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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