Many changes towards simulation of JLT and once again the evaluation of the Highspeed data from Lab experiments 2024
This commit is contained in:
Silas Oettinghaus
60
projects/ECOC_2025/auswertung_algorithms/mpi_dsp_debug.m
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60
projects/ECOC_2025/auswertung_algorithms/mpi_dsp_debug.m
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load("ffe_debug_snapshot.mat");
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dc_buffer_len = logspace(0,3,12);
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dc_buffer_len = 1024;
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mu_dc = logspace(-3,0,24);
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parfor d = 1:length(mu_dc)
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eq_lin = FFE_DCremoval_adaptive_mu("epochs_tr",5,"epochs_dd",3,"len_tr",4096*2,"mu_dd",...
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0.0002,"mu_tr",0,"order",25,"sps",2,"decide",0,...
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"mu_dc",mu_dc(d),...
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"dc_buffer_len",1024, ...
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"ffe_buffer_len",1,...
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"smoothing_buffer_length",0,...
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"smoothing_buffer_update",1,...
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"adaptive_mu_mode",0);
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%
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% eq_lin = FFE("epochs_tr",5,"epochs_dd",3,"len_tr",4096*2,"mu_dd",...
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% 0.0002,"mu_tr",0,"order",25,"sps",2,"decide",0);
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ffe_results = ffe(eq_lin,M,Scpe_sig,Symbols,Tx_bits,...
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"precode_mode",duob_mode,...
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'showAnalysis',0,...
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"postFFE",[],...
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"eth_style_symbol_mapping",0);
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ffe_results.metrics.print
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% % eq_lin = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"ffe_mu_dd",1e-4,"dfe_mu_dd",5e-4,"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",21,"dfe_order",2,"sps",2,"decide",0);
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%
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% eq_lin = FFE("epochs_tr",5,"epochs_dd",3,"len_tr",4096*2,"mu_dd",...
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% 0.0002,"mu_tr",0,"order",25,"sps",2,"decide",0);
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% pf_ = Postfilter("ncoeff",2,"useBurg",1);
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% mlse_ = MLSE_viterbi("duobinary_output",0,'M',4,'trellis_states',PAMmapper(4,0).levels);
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%
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% [ffe_results2, mlse_results] = vnle_postfilter_mlse(eq_lin, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
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% "precode_mode", duob_mode,...
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% 'showAnalysis', 1, ...
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% "postFFE", [],...
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% "eth_style_symbol_mapping", 0);
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ber(d) = ffe_results.metrics.BER;
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end
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figure(10)
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hold on
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plot(mu_dc,ber,'LineWidth',1,'DisplayName',sprintf('DC buffer len = 1024'),'Marker','.','MarkerSize',10);
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xlabel('BER');
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xlabel('MU DC');
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title('BER Optimization over dc\_buffer\_len');
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yline([4.85e-3,2e-2],'HandleVisibility', 'off','LineWidth',1,'LineStyle','--');
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ylim([9e-4, 0.5]);
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set(gca, 'YScale', 'log'); % BER is usually plotted log-scale
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legend('show', 'Location', 'best');
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grid on;
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118
projects/ECOC_2025/auswertung_algorithms/run_offline_dsp.m
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projects/ECOC_2025/auswertung_algorithms/run_offline_dsp.m
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% === SETTINGS ===
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dsp_options.append_to_db = 0;
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dsp_options.max_occurences = 15;
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dsp_options.database_path = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
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dsp_options.database_name = 'silas_labor_newdsp_newstructure.db';
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dsp_options.storage_path = 'Z:\2024\sioe_labor\';
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dsp_options.parameters = struct();
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dsp_options.parameters.mu_dc = [0.005];
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% === Get Run ID's ===
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db = DBHandler("pathToDB", [dsp_options.database_path, dsp_options.database_name], "type", "sqlite");
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fp = QueryFilter();
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% fp.where('Runs', 'run_id','EQUALS', 5108);
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fp.where('Runs', 'is_mpi','EQUALS', 0);
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fp.where('Runs', 'fiber_length','EQUALS', 1);
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fp.where('Runs', 'wavelength','EQUALS', 1310);
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fp.where('Runs', 'db_mode','EQUALS', 1);
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fp.where('Runs', 'rop_attenuation','EQUALS', 0);
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fp.where('Runs', 'pam_level','EQUALS', 4);
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fp.where('Runs', 'bitrate','EQUALS', 360e9);
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% fp.where('Runs', 'power_pd_in','GREATER_THAN', 7);
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[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
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% === Initialize DataStorage ===
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wh = DataStorage(dsp_options.parameters);
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wh.addStorage("ffe_package");
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wh.addStorage("mlse_package");
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wh.addStorage("vnle_package");
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wh.addStorage("dbtgt_package");
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wh.addStorage("dbenc_package");
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% === RUN IT ===
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% [results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "serial", 'wh', wh, 'waitbar', true);
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% wh.getStoValue('ffe_package',0.005);
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% wh.getStoValue('mlse_package',0.005);
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[dataTable,~] = db.queryDB(fp, [db.getTableFieldNames('Runs');db.getTableFieldNames('Results');db.getTableFieldNames('Equalizer')]);
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dataTable = cleanUpTable(dataTable);
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% === Look at it ===
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y_var = 'BER_precoded';
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x_var = 'bitrate';
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fixedVars = {'equalizer_structure', x_var};
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[dataTableClean, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var);
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% --- Group and aggregate ---
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dataTableGrpd_mean = groupIt(fixedVars, dataTableClean, @mean);
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dataTableGrpd_min = groupIt(fixedVars, dataTableClean, @min);
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dataTableGrpd_max = groupIt(fixedVars, dataTableClean, @max);
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% Choose a color map
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cols = linspecer(numel(unique(dataTableGrpd_mean.equalizer_structure)));
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figure;
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hold on;
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% Get unique equalizer structures for grouping
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unique_eq = unique(dataTableGrpd_mean.equalizer_structure);
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for i = 1:numel(unique_eq)
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eq_val = unique_eq(i);
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% Filter grouped data for this equalizer structure
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filt = dataTableGrpd_mean.equalizer_structure == eq_val;
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x = dataTableGrpd_mean.(x_var)(filt);
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y_mean = dataTableGrpd_mean.(y_var)(filt);
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y_min = dataTableGrpd_min.(y_var)(filt);
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y_max = dataTableGrpd_max.(y_var)(filt);
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% Bounds for boundedline (distance from mean)
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y_lower = y_mean - y_min;
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y_upper = y_max - y_mean;
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y_bounds = [y_lower, y_upper];
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% --- Bounded line (mean ± min/max) ---
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if exist('boundedline', 'file')
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[hl, hp] = boundedline(x, y_mean, y_bounds, ...
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'alpha', 'transparency', 0.1, ...
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'cmap', cols(i,:), ...
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'nan', 'fill', ...
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'orientation', 'vert');
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set(hl, 'LineWidth', 1.2, 'DisplayName', sprintf('Eq %s', eq_val));
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set(hp, 'HandleVisibility', 'off');
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else
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% If boundedline is not available, use errorbar
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errorbar(x, y_mean, y_lower, y_upper, ...
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'o-', 'Color', cols(i,:), 'LineWidth', 1.2, ...
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'DisplayName', sprintf('Eq %d', eq_val),'HandleVisibility', 'off');
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end
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% --- Normal line (mean only) ---
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plot(x, y_mean, '-', 'Color', cols(i,:), 'LineWidth', 1.5, ...
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'DisplayName', sprintf('Mean Eq %s', eq_val),'HandleVisibility', 'off');
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% --- Scatter plot for individual points (from original data) ---
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% Filter original data for this group
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orig_filt = dataTableClean.equalizer_structure == eq_val;
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x_scatter = dataTableClean.(x_var)(orig_filt);
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y_scatter = dataTableClean.(y_var)(orig_filt);
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scatter(x_scatter, y_scatter, 10,cols(i,:), 'filled', ...
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'MarkerFaceAlpha', 0.5, 'DisplayName', sprintf('Scatter Eq %s', eq_val),'HandleVisibility', 'off');
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end
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yline([2.2e-4,4.85e-3,2e-2],'HandleVisibility', 'off','LineWidth',1,'LineStyle','--');
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set(gca, 'YScale', 'log'); % BER is usually plotted log-scale
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xlabel(x_var, 'Interpreter', 'none');
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ylabel(y_var, 'Interpreter', 'none');
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legend('show', 'Location', 'best');
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grid on;
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title(sprintf('%s vs. %s', y_var, x_var), 'Interpreter', 'none');
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hold off;
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