CLEANUP - changes to folder structure
This commit is contained in:
Silas Oettinghaus
60
projects/ECOC_2025_MPI/auswertung_algorithms/mpi_dsp_debug.m
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60
projects/ECOC_2025_MPI/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_MPI/auswertung_algorithms/run_offline_dsp.m
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118
projects/ECOC_2025_MPI/auswertung_algorithms/run_offline_dsp.m
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@@ -0,0 +1,118 @@
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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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68
projects/ECOC_2025_MPI/dsp_filtered_db.m
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68
projects/ECOC_2025_MPI/dsp_filtered_db.m
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@@ -0,0 +1,68 @@
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% === SETTINGS ===
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savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
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% databasePath = '\\ntserver.tf.uni-kiel.de\scratch\sioe\ECOC_2025\';
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databasePath = 'Z:\2025\ECOC Silas\';
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databasePath = 'C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
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database_name = 'ecoc2025_loops.db';
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db = DBHandler("pathToDB", [databasePath, database_name],"type","mysql");
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num_occ = 30;
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run_par = true;
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refreshInterval = 300; % seconds
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% In-memory list of currently processing run_ids
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% currentlyProcessing = [];
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alreadyProcessing = [];
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fprintf('--- DSP WATCHDOG STARTED ---\n');
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while true
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try
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% Query incomplete run_ids
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sql_query = [
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'SELECT ru.run_id ' ...
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'FROM Runs ru ' ...
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'LEFT JOIN Results r ON ru.run_id = r.run_id ' ...
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'LEFT JOIN EqualizerParameters e ON r.eqParam_id = e.eq_id ' ...
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'WHERE ru.loop_id IN ( ' ...
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' SELECT loop_id FROM Runs GROUP BY loop_id HAVING COUNT(*) > 2 ' ...
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') ' ...
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'GROUP BY ru.run_id ' ...
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'HAVING COUNT(CASE WHEN e.equalizer_structure = 1 THEN 1 END) < 5;'
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];
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runIdsFiltered = db.fetch(sql_query);
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newRunIds = setdiff(runIdsFiltered.run_id, alreadyProcessing);
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fprintf('[%s] Found %d run(s) needing processing, %d new.\n', ...
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datestr(now, 'yyyy-mm-dd HH:MM:SS'), numel(runIdsFiltered.run_id), numel(newRunIds));
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for i = 1:numel(newRunIds)
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cur_id = newRunIds(i);
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% Add to in-memory processing list
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alreadyProcessing(end+1) = cur_id;
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% Submit processing
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try
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[output, future] = submit_dsp(cur_id, databasePath, database_name, savePath, ...
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"parallel", run_par, "max_occurences", num_occ);
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% Add a listener for future completion (success or failure)
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catch err
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warning('Error processing run_id %d: %s', cur_id, err.message);
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% Remove from in-memory list even on error (optional: you could leave it for retry)
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alreadyProcessing(alreadyProcessing == cur_id) = [];
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end
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end
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catch err
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warning('Error in DSP queue watchdog: %s', err.message);
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end
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fprintf('Sleeping for %.0f seconds...\n\n', refreshInterval);
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pause(refreshInterval);
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end
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262
projects/ECOC_2025_MPI/dsp_run_id.m
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262
projects/ECOC_2025_MPI/dsp_run_id.m
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@@ -0,0 +1,262 @@
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function [output] = dsp_run_id(run_id,options)
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arguments
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run_id
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options.append_to_db = 0;
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options.max_occurences = 4;
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options.parameters = struct();
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options.database_path
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options.database_name
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options.storage_path
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end
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database = DBHandler("pathToDB",[options.database_path,options.database_name],"type","sqlite");
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% database = DBHandler("type","mysql");
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filterParams = database.tables;
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filterParams.Configurations = struct('run_id', run_id);
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selectedFields = {'Runs.run_id','Runs.tx_bits_path','Runs.tx_symbols_path','Runs.rx_sync_path','Runs.rx_raw_path',...
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'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
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'Configurations.interference_attenuation'};
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[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
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[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
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dataTable = dataTable(uniqueIdx,:); % Extract unique configurations for each run_id
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fsym = dataTable.symbolrate;
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M = double(dataTable.pam_level);
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try
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duob_mode = db_mode.(strrep(char(dataTable.db_mode),'"',''));
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catch
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duob_mode = db_mode(dataTable.db_mode);
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end
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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len_tr = 4096*2;
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vnle_order1 = 50;
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vnle_order2 = 5;
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vnle_order3 = 5;
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dfe_order = [0 0 0];
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pf_ncoeffs = 1;
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mu_ffe1 = 0.0001;
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mu_ffe2 = 0.0008;
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mu_ffe3 = 0.001;
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mu_dfe = 0.0004;
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mu_dc = 0.00;
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mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
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vnle_order=[vnle_order1,vnle_order2,vnle_order3];
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dc_buffer_len = 224;
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ffe_buffer_len = 1;
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smoothing_buffer_length = 4096;
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smoothing_buffer_update = 224;
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% Overwrite default parameters if given in options.parameters
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paramStruct = options.parameters;
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if ~isempty(paramStruct)
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paramNames = fieldnames(paramStruct);
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for i = 1:numel(paramNames)
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thisName = paramNames{i};
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thisValue = paramStruct.(thisName);
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eval([thisName ' = thisValue;']);
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end
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end
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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eq_ = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
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pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
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mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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mlse_db_ = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
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eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
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output = struct();
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vnle_pf_package = {};
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vnle_package = {};
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dbtgt_package = {};
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Tx_bits = load([options.storage_path, char(dataTable.tx_bits_path)]);
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Tx_bits = Tx_bits.Bits;
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Symbols_mapped = PAMmapper(M,0).map(Tx_bits);
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Symbols_mapped.fs = dataTable.symbolrate;
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Symbols = load([options.storage_path, char(dataTable.tx_symbols_path)]);
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Symbols = Symbols.Symbols;
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found_sync = 0;
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try
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Scpe_load = load([options.storage_path, char(dataTable.rx_sync_path)]);
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Scpe_cell = Scpe_load.S;
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[~,~,~,found_sync] = Scpe_cell{2}.tsynch("reference",Symbols,"fs_ref",dataTable.symbolrate,"debug_plots",1);
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end
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if ~found_sync
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Scpe_sig_raw = load([options.storage_path, char(dataTable.rx_raw_path(1))]);
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Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
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Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",Scpe_sig_raw.fs,"fs_out",2*fsym);
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[~,Scpe_cell,~,found_sync] =Scpe_sig_resampled.tsynch("reference",Symbols,"fs_ref",dataTable.symbolrate,"debug_plots",1);
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end
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if ~found_sync
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if length(Symbols_mapped.signal) == sum(Symbols_mapped.signal == Symbols.signal)
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warning('Could not synchronize the received signal with the stored symbols!')
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else
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[~,Scpe_cell,~,found_sync] =Scpe_sig_raw.tsynch("reference",Symbols_mapped,"fs_ref",dataTable.symbolrate,"debug_plots",0);
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end
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if ~found_sync
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warning('Could not synchronize the received signal with the stored symbols!')
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end
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end
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record_realizations = min(options.max_occurences,length(Scpe_cell));
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for occ = 1:record_realizations
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Scpe_sig = Scpe_cell{occ};
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%%%%%% Sample to 2x fsym %%%%%%
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Scpe_sig = Scpe_sig.resample("fs_out",2*fsym);
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%%%%%% Sync Rx signal with reference %%%%%%
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[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
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Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.6,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
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|
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Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
|
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if duob_mode ~= db_mode.db_encoded
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|
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vnle_pf = 1;
|
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dbtgt = 0;
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% %%%%% VNLE + DFE %%%%
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if 0
|
||||
|
||||
eq_ = FFE_DCremoval_adaptive_mu("epochs_tr",5,"epochs_dd",3,"len_tr",4096*2,"mu_dd",...
|
||||
0.0002,"mu_tr",0,"order",25,"sps",2,"decide",0,...
|
||||
"mu_dc",mu_dc,...
|
||||
"dc_buffer_len",dc_buffer_len, ...
|
||||
"ffe_buffer_len",ffe_buffer_len,...
|
||||
"smoothing_buffer_length",smoothing_buffer_length,...
|
||||
"smoothing_buffer_update",smoothing_buffer_update);
|
||||
|
||||
eq_ = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
|
||||
result = vnle(eq_,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[],"eth_style_symbol_mapping",0);
|
||||
vnle_package{occ} = result;
|
||||
fprintf("FFE Results: %.2e\n", result.ber_vnle);
|
||||
|
||||
|
||||
if options.append_to_db
|
||||
database.addProcessingResult(run_id, result.resultsVNLE, result.equalizerConfigVNLE);
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
%%%%% VNLE + PF + MLSE %%%%
|
||||
if vnle_pf
|
||||
|
||||
[result] = vnle_postfilter_mlse(eq_,pf_,mlse_,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[],"eth_style_symbol_mapping",0);
|
||||
vnle_pf_package{occ} = result;
|
||||
|
||||
if options.append_to_db
|
||||
database.addProcessingResult(run_id,result.resultsMLSE, result.equalizerConfigMLSE);
|
||||
database.addProcessingResult(run_id,result.resultsVNLE, result.equalizerConfigVNLE);
|
||||
end
|
||||
end
|
||||
|
||||
%%%%% Duobinary Targeting %%%%
|
||||
if dbtgt
|
||||
[result] = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, "precode_mode", duob_mode,'showAnalysis',0,"postFFE",[]);
|
||||
dbtgt_package{occ} = result;
|
||||
|
||||
if options.append_to_db
|
||||
database.addProcessingResult(run_id, result.resultsDBtgt, result.equalizerConfigDBtgt);
|
||||
end
|
||||
end
|
||||
|
||||
% fprintf("BER VNLE: %.2e | %.2e; BER MLSE: %.2e | %.2e; BER DB tgt: %.2e | %.2e \n",vnle_pf_package{occ}.resultsVNLE.BER,vnle_pf_package{occ}.resultsVNLE.BER_precoded ,vnle_pf_package{occ}.resultsMLSE.BER,vnle_pf_package{occ}.resultsMLSE.BER_precoded,dbtgt_package{occ}.resultsDBtgt.BER,dbtgt_package{occ}.resultsDBtgt.BER_precoded)
|
||||
% % fprintf("BER VNLE: %.2e | %.2e; BER MLSE: %.2e | %.2e \n",vnle_pf_package{occ}.resultsVNLE.BER,vnle_pf_package{occ}.resultsVNLE.BER_precoded ,vnle_pf_package{occ}.resultsMLSE.BER,vnle_pf_package{occ}.resultsMLSE.BER_precoded);
|
||||
|
||||
if vnle_pf
|
||||
% occ = 1; % or whatever your loop index is
|
||||
|
||||
|
||||
% Extract VNLE results for readability
|
||||
vnle_result = vnle_pf_package{occ}.resultsVNLE;
|
||||
mlse_result = vnle_pf_package{occ}.resultsMLSE;
|
||||
|
||||
|
||||
% Print header
|
||||
fprintf("==== EQUALIZATION RUN-ID %d | PAM-%d | %.2f GBd ====\n\n", run_id, M, Symbols.fs.*1e-9);
|
||||
|
||||
% VNLE Results
|
||||
fprintf(">> VNLE Results:\n");
|
||||
fprintf(" BER %.2e\n", vnle_result.BER);
|
||||
fprintf(" BER (pre-code) %.2e\n", vnle_result.BER_precoded);
|
||||
fprintf(" SNR: %.2f dB\n", vnle_result.SNR);
|
||||
fprintf(" GMI: %.4f\n", vnle_result.GMI);
|
||||
fprintf(" Linerate: %.2f Gbps\n", Symbols.fs .* floor(log2(M)*10)/10 .*1e-9);
|
||||
fprintf(" AIR: %.2f Gbps\n", vnle_result.AIR.*1e-9);
|
||||
fprintf("\n");
|
||||
|
||||
% MLSE Results
|
||||
fprintf(">> MLSE Results:\n");
|
||||
fprintf(" BER : %.2e\n", mlse_result.BER);
|
||||
fprintf(" BER (pre-code): %.2e\n", mlse_result.BER_precoded);
|
||||
fprintf(" Channel Alpha : %.2f\n", mlse_result.Alpha);
|
||||
fprintf("\n");
|
||||
end
|
||||
|
||||
if dbtgt
|
||||
dbtgt = dbtgt_package{occ}.resultsDBtgt;
|
||||
% DB Target Results
|
||||
fprintf(">> DB Target Results:\n");
|
||||
fprintf(" BER: %.2e\n", dbtgt.BER);
|
||||
fprintf(" BER (pre-code): %.2e\n", dbtgt.BER_precoded);
|
||||
fprintf("\n");
|
||||
end
|
||||
|
||||
else
|
||||
|
||||
%%%%%% %db signaling => db encoded %%%%%
|
||||
if 1
|
||||
mlse_db_enc = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
|
||||
eq_db_enc = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
[result] = duobinary_signaling(eq_db_enc, mlse_db_enc,M, Scpe_sig ,Symbols, Tx_bits);
|
||||
dbenc_package{occ} = result;
|
||||
|
||||
if options.append_to_db
|
||||
database.addProcessingResult(run_id, result.resultsDBsignaling, result.equalizerConfigDBsignaling);
|
||||
end
|
||||
end
|
||||
|
||||
fprintf("BER DB: %.2e \n",dbenc_package{occ}.resultsDBsignaling.BER);
|
||||
|
||||
end
|
||||
|
||||
|
||||
% autoArrangeFigures;
|
||||
disp('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ')
|
||||
fprintf('\n')
|
||||
|
||||
|
||||
end
|
||||
|
||||
|
||||
output.dataTable = dataTable;
|
||||
output.vnle_pf_package = vnle_pf_package;
|
||||
output.vnle_package = vnle_package;
|
||||
output.dbtgt_package = dbtgt_package;
|
||||
|
||||
end
|
||||
92
projects/ECOC_2025_MPI/dsp_standalone.m
Normal file
92
projects/ECOC_2025_MPI/dsp_standalone.m
Normal file
@@ -0,0 +1,92 @@
|
||||
|
||||
|
||||
|
||||
savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
|
||||
databasePath = 'C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
|
||||
database_name = 'ecoc2025_loops.db';
|
||||
db = DBHandler("type","mysql");
|
||||
% db = DBHandler("pathToDB", [databasePath, database_name],"type","sqlite");
|
||||
|
||||
filterParams = db.tables;
|
||||
% filterParams.Configurations = struct('run_id', 4001);
|
||||
filterParams.Configurations = struct( ...
|
||||
'symbolrate', 112e9, ... %[224,336,360,390,420,448]
|
||||
'fiber_length', 0, ...
|
||||
'db_mode', '"no_db"', ...
|
||||
'interference_attenuation', [], ...
|
||||
'interference_path_length', [], ...
|
||||
'is_mpi', 1, ...
|
||||
'pam_level', 4, ...
|
||||
'wavelength', 1310, ...
|
||||
'precomp_amp', [], ...
|
||||
'signal_attenuation', [], ...
|
||||
'v_awg', [], ...
|
||||
'v_bias', 2.65 ...
|
||||
);
|
||||
|
||||
selectedFields = {'Runs.run_id','Runs.loop_id','Runs.tx_bits_path','Runs.tx_signal_path','Runs.tx_symbols_path','Runs.rx_sync_path','Runs.rx_raw_path',...
|
||||
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
|
||||
'Configurations.interference_attenuation', 'Configurations.interference_path_length'};
|
||||
|
||||
[dataTable,sql_query] = db.queryDB(filterParams, selectedFields);
|
||||
|
||||
% dataTable(dataTable.loop_id<200,:) = [];
|
||||
|
||||
num_occ = 15;
|
||||
run_par = true;
|
||||
run_id = dataTable.run_id;
|
||||
params = struct();
|
||||
|
||||
% slow DC tracking
|
||||
params.dc_buffer_len = 224;
|
||||
params.ffe_buffer_len = 1;
|
||||
params.smoothing_buffer_length = 0;
|
||||
params.smoothing_buffer_update = 0;
|
||||
params.mu_dc = 0.005;
|
||||
|
||||
futures_list = parallel.FevalFuture.empty();
|
||||
for id = 1:length(dataTable.run_id)
|
||||
run_id = dataTable.run_id(id);
|
||||
[out, futures_list(id)] = submit_dsp(run_id, databasePath, database_name, savePath,"parallel",run_par,'max_occurences',num_occ,'paramstruct',params);
|
||||
end
|
||||
|
||||
% ideal DC tracking
|
||||
params.dc_buffer_len = 1;
|
||||
params.ffe_buffer_len = 1;
|
||||
params.smoothing_buffer_length = 0;
|
||||
params.smoothing_buffer_update = 0;
|
||||
params.mu_dc = 0.005;
|
||||
|
||||
futures_list = parallel.FevalFuture.empty();
|
||||
for id = 1:length(dataTable.run_id)
|
||||
run_id = dataTable.run_id(id);
|
||||
[out, futures_list(id)] = submit_dsp(run_id, databasePath, database_name, savePath,"parallel",run_par,'max_occurences',num_occ,'paramstruct',params);
|
||||
end
|
||||
|
||||
% DC smoothing
|
||||
params.dc_buffer_len = 1;
|
||||
params.ffe_buffer_len = 1;
|
||||
params.smoothing_buffer_length = 4096;
|
||||
params.smoothing_buffer_update = 224;
|
||||
params.mu_dc = 0.00;
|
||||
|
||||
futures_list = parallel.FevalFuture.empty();
|
||||
for id = 1:length(dataTable.run_id)
|
||||
run_id = dataTable.run_id(id);
|
||||
[out, futures_list(id)] = submit_dsp(run_id, databasePath, database_name, savePath,"parallel",run_par,'max_occurences',num_occ,'paramstruct',params);
|
||||
end
|
||||
|
||||
% only FFE
|
||||
params.dc_buffer_len = 1;
|
||||
params.ffe_buffer_len = 1;
|
||||
params.smoothing_buffer_length = 0;
|
||||
params.smoothing_buffer_update = 0;
|
||||
params.mu_dc = 0.00;
|
||||
|
||||
futures_list = parallel.FevalFuture.empty();
|
||||
for id = 1:length(dataTable.run_id)
|
||||
run_id = dataTable.run_id(id);
|
||||
[out, futures_list(id)] = submit_dsp(run_id, databasePath, database_name, savePath,"parallel",run_par,'max_occurences',num_occ,'paramstruct',params);
|
||||
end
|
||||
|
||||
|
||||
63
projects/ECOC_2025_MPI/dsp_standalone_2.m
Normal file
63
projects/ECOC_2025_MPI/dsp_standalone_2.m
Normal file
@@ -0,0 +1,63 @@
|
||||
|
||||
|
||||
|
||||
savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
|
||||
databasePath = 'C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
|
||||
database_name = 'ecoc2025_loops.db';
|
||||
db = DBHandler("type","mysql");
|
||||
% db = DBHandler("pathToDB", [databasePath, database_name],"type","sqlite");
|
||||
|
||||
filterParams = db.tables;
|
||||
% filterParams.Configurations = struct('run_id', run_id);
|
||||
filterParams.Configurations = struct( ...
|
||||
'symbolrate', 112e9, ... %[224,336,360,390,420,448]
|
||||
'fiber_length', 0, ...
|
||||
'db_mode', '"no_db"', ...
|
||||
'interference_attenuation', [], ...
|
||||
'interference_path_length', 0, ...
|
||||
'is_mpi', 1, ...
|
||||
'pam_level', 4, ...
|
||||
'wavelength', 1310, ...
|
||||
'precomp_amp', [], ...
|
||||
'signal_attenuation', [], ...
|
||||
'v_awg', [], ...
|
||||
'v_bias', 2.65 ...
|
||||
);
|
||||
|
||||
selectedFields = {'Runs.run_id','Runs.loop_id','Runs.tx_bits_path','Runs.tx_signal_path','Runs.tx_symbols_path','Runs.rx_sync_path','Runs.rx_raw_path',...
|
||||
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
|
||||
'Configurations.interference_attenuation', 'Configurations.interference_path_length'};
|
||||
|
||||
[dataTable,sql_query] = db.queryDB(filterParams, selectedFields);
|
||||
|
||||
dataTable(dataTable.loop_id~=217,:) = [];
|
||||
|
||||
num_occ = 10;
|
||||
run_par = true;
|
||||
run_id = dataTable.run_id;
|
||||
|
||||
params.dc_buffer_len = 224;
|
||||
params.ffe_buffer_len = 1;
|
||||
params.smoothing_buffer_length = 0;
|
||||
params.smoothing_buffer_update = 0;
|
||||
params.mu_dc = 0.005;
|
||||
|
||||
futures_list = parallel.FevalFuture.empty();
|
||||
for id = 1:length(dataTable.run_id)
|
||||
run_id = dataTable.run_id(id);
|
||||
[out, futures_list(id)] = submit_dsp(run_id, databasePath, database_name, savePath,"parallel",run_par,'max_occurences',num_occ,'paramstruct',params);
|
||||
end
|
||||
|
||||
% Extract all ber_mlse values from the vnle_pf_package using cellfun
|
||||
ber_mlse = cellfun(@(pkg) pkg.ber_mlse, future.OutputArguments{1,1}.vnle_pf_package);
|
||||
ber_vnle = cellfun(@(pkg) pkg.ber_vnle, future.OutputArguments{1,1}.vnle_pf_package);
|
||||
|
||||
figure(101)
|
||||
hold on;
|
||||
scatter(1:num_occ,ber_mlse,15,'Marker','*');
|
||||
scatter(1:num_occ,ber_vnle,15,'Marker','*');
|
||||
legend('Interpreter', 'latex');
|
||||
xlabel('Occurences');
|
||||
ylabel('BER');
|
||||
grid on;
|
||||
beautifyBERplot;
|
||||
58
projects/ECOC_2025_MPI/dsp_test/hyperparam_tuning.m
Normal file
58
projects/ECOC_2025_MPI/dsp_test/hyperparam_tuning.m
Normal file
@@ -0,0 +1,58 @@
|
||||
|
||||
|
||||
Scpe_sig = load("imdd_simulation\projects\ECOC_2025\dsp_test\pam4_scopesignal.mat");Scpe_sig = Scpe_sig.Scpe_sig;
|
||||
Tx_bits = load("imdd_simulation\projects\ECOC_2025\dsp_test\pam4_bits.mat");Tx_bits = Tx_bits.Tx_bits;
|
||||
Symbols = load("imdd_simulation\projects\ECOC_2025\dsp_test\pam4_symbols.mat");Symbols = Symbols.Symbols;
|
||||
|
||||
eq_ = FFE_DCremoval("epochs_tr",5,"epochs_dd",3,"len_tr",4096*2,"mu_dd",1e-5,"mu_tr",0,"order",50,"sps",2,"decide",0,"mu_dc",0.005,"dc_buffer_len",1);
|
||||
|
||||
% savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
|
||||
% databasePath = 'C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
|
||||
% database_name = 'ecoc2025_loops.db';
|
||||
% db = DBHandler("type","mysql");
|
||||
|
||||
params = (logspace(-6,-2,20));
|
||||
params = floor((logspace(2,3,20)));
|
||||
params = 224;
|
||||
|
||||
for i = 1:length(params)
|
||||
|
||||
eq_ = FFE_DCremoval_adaptive_mu("epochs_tr",5,"epochs_dd",3,"len_tr",4096*2,"mu_dd",...
|
||||
0.0002,"mu_tr",0,"order",25,"sps",2,"decide",0,...
|
||||
"mu_dc",0.005,"dc_buffer_len",1, ...
|
||||
"ffe_buffer_len",1,...
|
||||
"smoothing_buffer_length",0,...
|
||||
"smoothing_buffer_update",1);
|
||||
|
||||
result = ffe(eq_,4,Scpe_sig,Symbols,Tx_bits,"precode_mode",db_mode.no_db,'showAnalysis',1,"postFFE",[],"eth_style_symbol_mapping",0);
|
||||
ber_ffe(i) = result.metrics;
|
||||
fprintf(" FFE Results: %.2e\n", ber_ffe(i));
|
||||
|
||||
% db.addProcessingResult(run_id, result.resultsVNLE, result.equalizerConfigVNLE);
|
||||
|
||||
% eq_ = FFE_adaptive_decision("epochs_tr",5,"epochs_dd",3,"len_tr",4096*2,"mu_dd",...
|
||||
% 0.0003,"mu_tr",0,"order",50,"sps",2,"decide",1,"buffer_length",params(i));
|
||||
%
|
||||
% result = vnle(eq_,4,Scpe_sig,Symbols,Tx_bits,"precode_mode",db_mode.no_db,'showAnalysis',1,"postFFE",[],"eth_style_symbol_mapping",0);
|
||||
% ber_dc(i) = result.ber_vnle;
|
||||
%
|
||||
% fprintf(" FFE+dc tr. Results: %.2e\n", ber_dc(i));
|
||||
|
||||
end
|
||||
|
||||
figure(103435)
|
||||
hold on;
|
||||
% scatter(params,ber_dc,15,'Marker','o','LineWidth',1,'DisplayName','DC tracking');
|
||||
% [a,b]=min(ber_dc);
|
||||
% scatter(params(b),a,45,'Marker','x','MarkerEdgeColor','r','LineWidth',1);
|
||||
|
||||
scatter(params,ber_ffe,15,'Marker','square','LineWidth',1);
|
||||
[a,b]=min(ber_ffe);
|
||||
scatter(params(b),a,45,'Marker','x','MarkerEdgeColor','r','LineWidth',1,'DisplayName','FFE');
|
||||
|
||||
legend('Interpreter', 'latex');
|
||||
xlabel('Occurences');
|
||||
ylabel('BER');
|
||||
grid on;
|
||||
beautifyBERplot;
|
||||
ylim([1e-4,0.1 ])
|
||||
BIN
projects/ECOC_2025_MPI/ecoc2025.db
Normal file
BIN
projects/ECOC_2025_MPI/ecoc2025.db
Normal file
Binary file not shown.
1
projects/ECOC_2025_MPI/ecoc2025.sqbpro
Normal file
1
projects/ECOC_2025_MPI/ecoc2025.sqbpro
Normal file
File diff suppressed because one or more lines are too long
BIN
projects/ECOC_2025_MPI/ecoc2025_einmessung.db
Normal file
BIN
projects/ECOC_2025_MPI/ecoc2025_einmessung.db
Normal file
Binary file not shown.
BIN
projects/ECOC_2025_MPI/ecoc2025_fail.db
Normal file
BIN
projects/ECOC_2025_MPI/ecoc2025_fail.db
Normal file
Binary file not shown.
BIN
projects/ECOC_2025_MPI/ecoc2025_loops - Kopie.db
Normal file
BIN
projects/ECOC_2025_MPI/ecoc2025_loops - Kopie.db
Normal file
Binary file not shown.
BIN
projects/ECOC_2025_MPI/ecoc2025_loops.db
Normal file
BIN
projects/ECOC_2025_MPI/ecoc2025_loops.db
Normal file
Binary file not shown.
44
projects/ECOC_2025_MPI/estimate_linewidth.m
Normal file
44
projects/ECOC_2025_MPI/estimate_linewidth.m
Normal file
@@ -0,0 +1,44 @@
|
||||
% Parameters
|
||||
|
||||
reclen = 133169152;
|
||||
Scp_long = ScopeKeysight("model","DSAZ634A",'autoscale',0,"fadc","GSa_160","channel",[0,0,1,0],"recordLen",reclen,"removeDC",1,"extRef",1);
|
||||
Scpe_sig_raw = Scp_long.read("channel",[0,0,1,0]);
|
||||
Scpe_sig_raw = Scpe_sig_raw{3};
|
||||
|
||||
signal = Scpe_sig_raw.signal;
|
||||
Fs = Scpe_sig_raw.fs; % Sampling rate [Hz]
|
||||
N = length(signal); % Number of samples
|
||||
f_center = 20e9; % Frequency of interest (~20 GHz)
|
||||
|
||||
% Create time vector
|
||||
t = (0:N-1)' / Fs;
|
||||
|
||||
% Mix down to baseband
|
||||
signal_mix = signal .* exp(-1j * 2 * pi * f_center * t);
|
||||
|
||||
% Low-pass filter (optional but recommended)
|
||||
% Design a filter with bandwidth ~ linewidth * safety margin
|
||||
BW = 1000e6; % 100 MHz bandwidth for example
|
||||
|
||||
% Decimate (reduce sampling rate)
|
||||
decimation_factor = 10;%floor(Fs / (2 * BW));
|
||||
signal_decim = downsample(signal_mix, decimation_factor);
|
||||
Fs_decim = Fs / decimation_factor;
|
||||
|
||||
% FFT of downmixed signal
|
||||
Nfft = 2^nextpow2(length(signal_decim) * 4); % Zero-pad
|
||||
spectrum = fft(signal_decim .* blackmanharris(length(signal_decim)), Nfft);
|
||||
freq = (0:Nfft-1) * (Fs_decim / Nfft);
|
||||
|
||||
% Only positive frequencies
|
||||
halfIdx = 1:floor(Nfft/2);
|
||||
spectrum_half = abs(spectrum(halfIdx));
|
||||
freq_half = freq(halfIdx);
|
||||
|
||||
% Plot
|
||||
figure;
|
||||
plot(freq_half / 1e6, 20*log10(spectrum_half));
|
||||
xlabel('Frequency (MHz)');
|
||||
ylabel('Magnitude (dB)');
|
||||
title('Zoomed Spectrum around 20 GHz');
|
||||
grid on;
|
||||
89
projects/ECOC_2025_MPI/load_signal_standalone.m
Normal file
89
projects/ECOC_2025_MPI/load_signal_standalone.m
Normal file
@@ -0,0 +1,89 @@
|
||||
|
||||
db = DBHandler("type","mysql","dataBase",'labor');
|
||||
|
||||
|
||||
fp = QueryFilter();
|
||||
% fp.where('Runs', 'run_id','EQUALS', 987);
|
||||
M = 4;
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'symbolrate','EQUALS', 112e9);
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 0);
|
||||
fp.where('Runs', 'is_mpi','EQUALS', 1);
|
||||
fp.where('Runs', 'interference_path_length','EQUALS', 70);
|
||||
% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
|
||||
fp.where('Runs', 'sir','EQUALS',20);
|
||||
|
||||
|
||||
[dataTable,sql_query] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||
|
||||
[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
|
||||
|
||||
dataTable.SIR = -7 - round(dataTable.power_mpi_interference);
|
||||
|
||||
%%
|
||||
for i = 1:size(dataTable,1)
|
||||
dataTable_ = dataTable(i,:); % Extract unique configurations for each run_id
|
||||
|
||||
fsym = dataTable_.symbolrate;
|
||||
M = double(dataTable_.pam_level);
|
||||
duob_mode = db_mode.(strrep(char(dataTable_.db_mode),'"',''));
|
||||
|
||||
Tx_signal = load([savePath, char(dataTable_.tx_signal_path)]);
|
||||
Tx_signal = Tx_signal.Digi_sig;
|
||||
|
||||
Tx_bits = load([savePath, char(dataTable_.tx_bits_path)]);
|
||||
Tx_bits = Tx_bits.Bits;
|
||||
Symbols_mapped = PAMmapper(M,0).map(Tx_bits);
|
||||
Symbols_mapped.fs = dataTable_.symbolrate;
|
||||
|
||||
Symbols = load([savePath, char(dataTable_.tx_symbols_path)]);
|
||||
Symbols = Symbols.Symbols;
|
||||
|
||||
Scpe_sig_raw = load([savePath, char(dataTable_.rx_raw_path(1))]);
|
||||
Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
|
||||
Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
|
||||
|
||||
|
||||
Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",Scpe_sig_raw.fs,"fs_out",fsym);
|
||||
[~,Scpe_cell,found_sync,test,shifts] = Scpe_sig_resampled.tsynch("reference",Symbols,"fs_ref",dataTable_.symbolrate,"debug_plots",0);
|
||||
|
||||
shifts_mus = shifts./Scpe_sig_resampled.fs .*1e6;
|
||||
Scpe_sig_resampled = Scpe_sig_resampled.normalize("mode","rms");
|
||||
% Scpe_sig_resampled.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
|
||||
hold on;
|
||||
% xline(shifts_mus,'HandleVisibility','off');
|
||||
|
||||
shifts = shifts-shifts(1)+1;
|
||||
sep_sig = NaN(M,length(Scpe_sig_resampled));
|
||||
avg_sig = NaN(M,length(Scpe_sig_resampled));
|
||||
for j = 1:size(Scpe_cell,1)
|
||||
|
||||
[sep_sig_,avg_sig_]=showLevelScatter(Scpe_cell{j}.resample("fs_out",Symbols.fs),Symbols,"fignum",400);
|
||||
s = shifts(j);
|
||||
sep_sig(:,s+1:s+length(sep_sig_)) = sep_sig_;
|
||||
avg_sig(:,s+1:s+length(avg_sig_)) = avg_sig_;
|
||||
|
||||
end
|
||||
|
||||
disp(num2str(filterParams.Configurations.interference_path_length));
|
||||
var(sep_sig,0,2,'omitnan')
|
||||
%%
|
||||
xax_in_sec = ((1:length(avg_sig)) / fsym) * 1e6;
|
||||
figure();hold on;
|
||||
cols = cbrewer2('Paired',8);
|
||||
% for p = 1:size(avg_sig,1)
|
||||
% sc=scatter(xax_in_sec,sep_sig(p,:),1,'.','MarkerEdgeColor',cols((2*p)-1,:),'MarkerEdgeAlpha',0.1);
|
||||
% end
|
||||
for p = 1:size(avg_sig,1)
|
||||
sc=plot(xax_in_sec,avg_sig(p,:),'LineWidth',1,'Color',cols((2*p),:));
|
||||
end
|
||||
|
||||
yline(unique(Symbols.signal),'HandleVisibility','off');
|
||||
% xline(shifts./ fsym .*1e6,'HandleVisibility','off');
|
||||
xlabel('time in $\mu$s');
|
||||
ylabel('Normalized Amplitude');
|
||||
xlim([0 25]);
|
||||
ylim([-2 2]);
|
||||
|
||||
drawnow;
|
||||
end
|
||||
207
projects/ECOC_2025_MPI/measure_run.m
Normal file
207
projects/ECOC_2025_MPI/measure_run.m
Normal file
@@ -0,0 +1,207 @@
|
||||
|
||||
function measure_run(options)
|
||||
|
||||
arguments
|
||||
options.parallel (1,1) logical = false
|
||||
options.max_occurences = 1;
|
||||
options.config = [];
|
||||
options.only_record = 0;
|
||||
end
|
||||
|
||||
savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
|
||||
|
||||
current_folder = 'precomp_optimization_friday';
|
||||
fullFolderPath = fullfile(savePath, current_folder);
|
||||
if ~exist(fullFolderPath, 'dir')
|
||||
mkdir(fullFolderPath);
|
||||
end
|
||||
|
||||
basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
|
||||
database_name='ecoc2025.db';
|
||||
db = DBHandler("pathToDB",[basePath,database_name]);
|
||||
|
||||
precomp_path = "C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\";
|
||||
precomp_fn = "precomp_1km_1mm_cable_70ghz_pd_shf_t850_2p8_bias_shot2";
|
||||
precomp_mode = 2; %0=do nothing ; 1= measure; 2=precomp active
|
||||
run_lab_automation = 1;
|
||||
|
||||
%% Configuration
|
||||
|
||||
conf = db.tables.Configurations;
|
||||
referenceFields = fieldnames(conf);
|
||||
|
||||
conf.unique_elab_id = "20250408-842b0a3078172b48dd032795226fbe683190afc4";
|
||||
conf.symbolrate = 112e9;
|
||||
conf.bitrate = conf.symbolrate .* floor(log2(6)*10)/10;
|
||||
conf.pam_level = 4;
|
||||
conf.db_mode = db_mode.no_db;
|
||||
conf.pulsef_alpha = 0.3;
|
||||
conf.v_bias = 2.8;
|
||||
conf.v_awg = 0.8;
|
||||
conf.precomp_amp = -64;
|
||||
conf.wavelength = 1310;
|
||||
conf.laser_power = 10;
|
||||
conf.fiber_length = 0;
|
||||
conf.pd_in_desired = 7;
|
||||
conf.is_mpi = 1;
|
||||
conf.signal_attenuation = 0;
|
||||
conf.interference_path_length = 0.001; %m
|
||||
conf.interference_attenuation = 40;
|
||||
conf.pam_source = [];
|
||||
|
||||
|
||||
% === Optional Override of Conf ===
|
||||
if ~isempty(options.config)
|
||||
paramStruct = options.config;
|
||||
paramNames = fieldnames(paramStruct);
|
||||
for i = 1:numel(paramNames)
|
||||
thisName = paramNames{i};
|
||||
thisValue = paramStruct.(thisName);
|
||||
if isfield(conf, thisName)
|
||||
conf.(thisName) = thisValue;
|
||||
else
|
||||
warning('Unknown parameter: %s', thisName);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
assert(isequal(fieldnames(conf), referenceFields), 'Fieldnames do not match the reference structure!');
|
||||
|
||||
currentTime = datetime('now', 'Format', 'yyyyMMdd_HHmmss');
|
||||
timeStr = char(currentTime);
|
||||
filename = sprintf('%s_PAM_%d_R_%d',timeStr,conf.pam_level,conf.symbolrate.*1e-9);
|
||||
|
||||
%% Lab Automation
|
||||
|
||||
if run_lab_automation
|
||||
|
||||
% DC Source
|
||||
dcs = DC_supply("active",[1,1],"voltage",[conf.v_bias, 5]);
|
||||
dcs.set("voltage",[conf.v_bias, 5]);
|
||||
[v_bias_meas,i_bias_meas]=dcs.readVals();
|
||||
|
||||
% Laser
|
||||
laser = Exfo_laser("mainframe_channel",1,"safety_mode",0,"connection_id",'10','lab_interface','gpib');
|
||||
laser.setWavelength(conf.wavelength);
|
||||
laser.setPower(conf.laser_power);
|
||||
laser.enableLaser();
|
||||
|
||||
% Optical Attenuator
|
||||
voa = OptAtten("active",[0,2,1,1],"value",[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation],"wavelength",[conf.wavelength,conf.wavelength,conf.wavelength,conf.wavelength]);
|
||||
voa.set('value',[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation]);
|
||||
|
||||
% PDFA
|
||||
pdfa = Thor_PDFA("safety_mode",0,"serialport_number",'COM15');
|
||||
|
||||
% Construct AWG and Scope Modules %%%%%%
|
||||
fdac = 256e9;
|
||||
fadc = 160e9;
|
||||
Scp = ScopeKeysight("model","DSAZ634A",'autoscale',1,"fadc","GSa_160","channel",[0,0,1,0],"recordLen",2000000,"removeDC",1,"extRef",1);
|
||||
Awg = AwgKeysight("model","M8199A_ILV","fdac",fdac,"scaletodac",[1,1],"skews",[0,0],"voltages",[0,conf.v_awg]);
|
||||
A2S = Awg2Scope(Awg,Scp,[0,0,3,0],"waitUntilClick",0);
|
||||
|
||||
end
|
||||
|
||||
|
||||
%% Signal generation and transmission
|
||||
|
||||
%%%%% Symbol Generation %%%%%%
|
||||
Pform = Pulseformer("fsym",conf.symbolrate,"fdac",8*conf.symbolrate,"pulse","rc","pulselength",16,"alpha",conf.pulsef_alpha);
|
||||
|
||||
Pamsource = PAMsource(...
|
||||
"fsym",conf.symbolrate,"M",conf.pam_level,"order",18,"useprbs",1,...
|
||||
"fs_out",fdac,...
|
||||
"applyclipping",0,...
|
||||
"clipfactor",4,...
|
||||
"applypulseform",1,...
|
||||
"pulseformer",Pform,...
|
||||
"randkey",1,...
|
||||
"duobinary_mode", conf.db_mode);
|
||||
|
||||
conf.pam_source = Pamsource;
|
||||
|
||||
[Digi_sig,Symbols,Bits] = Pamsource.process();
|
||||
|
||||
|
||||
%%%%% Precompensation Routine %%%%%%
|
||||
if precomp_mode == 1 % measure channel
|
||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',fdac);
|
||||
Digi_sig = precomp_est.buildOFDM();
|
||||
elseif precomp_mode == 2 % apply precomp
|
||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
|
||||
Digi_sig = precomp_est.precomp(Digi_sig,'maxampdb',conf.precomp_amp,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
end
|
||||
|
||||
Digi_sig.spectrum("displayname",'Tx Spectrum','fignum',10,'normalizeTo0dB',0);
|
||||
|
||||
%%%%% Resample to DAC rate %%%%%%
|
||||
Digi_sig = Digi_sig.resample("fs_out",Awg.fdac);
|
||||
|
||||
[~,~,Scpe_sig_raw,~] = A2S.process("signal2",Digi_sig);
|
||||
|
||||
% Awg.upload("signal2",Digi_sig);
|
||||
% Scpe_sig_raw = Scp.read("channel",[0,0,1,0]);
|
||||
% Scpe_sig_raw = Scpe_sig_raw{[0,0,1,0]==1};
|
||||
|
||||
%%%%% Precompensation Routine %%%%%%
|
||||
if precomp_mode == 1
|
||||
% Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",fadc,"fs_out",2*fsym);
|
||||
precomp_est.estimate(Scpe_sig_raw,"save",true,"savePath",precomp_path,"fileName",precomp_fn);
|
||||
precomp_est.plot();
|
||||
return;
|
||||
end
|
||||
|
||||
|
||||
Scpe_sig_raw.spectrum("displayname",'Rx Spectrum','fignum',10,'normalizeTo0dB',0);
|
||||
Scpe_sig_raw.plot("clear",1,"displayname",'Rx Signal','fignum',11);
|
||||
|
||||
%% %%%%% Save Routine %%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
tx_bits_path=[filesep,current_folder,filesep,filename,'_bits'];
|
||||
save([savePath,tx_bits_path],"Bits");
|
||||
tx_symbols_path=[filesep,current_folder,filesep,filename,'_symbols'];
|
||||
save([savePath,tx_symbols_path],"Symbols");
|
||||
tx_signal_path=[filesep,current_folder,filesep,filename,'_signal'];
|
||||
save([savePath,tx_signal_path],"Digi_sig");
|
||||
rx_raw_path = [filesep,current_folder,filesep,filename,'_rx_signal_raw'];
|
||||
save([savePath,rx_raw_path],"Scpe_sig_raw");
|
||||
|
||||
|
||||
% Table 1: Append to Runs
|
||||
newRun = db.tables.Runs;
|
||||
newRun.run_id = NaN;
|
||||
newRun.date_of_run = datetime(currentTime, 'InputFormat', 'yyyyMMdd_HHmmss');
|
||||
newRun.tx_bits_path = tx_bits_path;
|
||||
newRun.tx_symbols_path = tx_symbols_path;
|
||||
newRun.rx_sync_path = [];
|
||||
newRun.rx_raw_path = rx_raw_path;
|
||||
newRun.filename = filename;
|
||||
newRun.tx_signal_path = tx_signal_path;
|
||||
run_id = db.appendToTable('Runs', newRun);
|
||||
|
||||
% Table 2: Append to Configurations
|
||||
conf.configuration_id = NaN;
|
||||
conf.run_id = run_id;
|
||||
db.appendToTable('Configurations', conf);
|
||||
|
||||
|
||||
% Table 3: Append to Measurements
|
||||
meas = db.tables.Measurements;
|
||||
meas.measurement_id = NaN; % Auto-increment, leave empty
|
||||
meas.run_id = run_id;
|
||||
meas.power_laser = laser.cur_power;
|
||||
meas.power_rop = [];
|
||||
meas.power_pd_in = voa.power_state(2);
|
||||
meas.power_mpi_interference = voa.power_state(4);
|
||||
meas.power_mpi_signal = voa.power_state(3);
|
||||
meas.voa_class = voa;
|
||||
meas.pdfa_class = pdfa;
|
||||
meas.laser_class = laser;
|
||||
assert(isequal(fieldnames(meas), fieldnames(db.tables.Measurements)), 'Fieldnames of "meas" do not match the reference structure!');
|
||||
db.appendToTable('Measurements', meas);
|
||||
|
||||
%% %%%%% DSP Routine %%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
[out, ~] = submit_dsp(run_id, basePath, database_name, savePath,"parallel",options.parallel,'max_occurences',options.max_occurences);
|
||||
|
||||
end
|
||||
272
projects/ECOC_2025_MPI/measure_run_script.m
Normal file
272
projects/ECOC_2025_MPI/measure_run_script.m
Normal file
@@ -0,0 +1,272 @@
|
||||
|
||||
|
||||
fullFolderPath = fullfile(savePath, current_folder);
|
||||
if ~exist(fullFolderPath, 'dir')
|
||||
mkdir(fullFolderPath);
|
||||
end
|
||||
|
||||
precomp_path = "C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\";
|
||||
precomp_fn = "precomp_1km_1mm_cable_70ghz_pd_shf_t850_2p8_bias_shot2";
|
||||
precomp_mode = 2; %0=do nothing ; 1= measure; 2=precomp active
|
||||
run_lab_automation = 0;
|
||||
|
||||
%% Configuration
|
||||
|
||||
conf = confRequest;
|
||||
referenceFields = fieldnames(db.tables.Configurations);
|
||||
|
||||
% assert(isequal(fieldnames(conf), referenceFields), 'Fieldnames do not match the reference structure!');
|
||||
|
||||
%% Lab Automation
|
||||
|
||||
if run_lab_automation
|
||||
|
||||
% DC Source
|
||||
if (any(confPrev.v_bias ~= conf.v_bias)) || (~exist('dcs','var')) || (isempty(confPrev.v_bias))
|
||||
dcs = DC_supply("active",[1,1],"voltage",[conf.v_bias, 5]);
|
||||
dcs.set("voltage",[conf.v_bias, 5]);
|
||||
[v_bias_meas,i_bias_meas]=dcs.readVals();
|
||||
end
|
||||
% Laser
|
||||
if (any(confPrev.laser_power ~= conf.laser_power)) || (~exist('laser','var')) || (isempty(confPrev.laser_power))
|
||||
laser = Exfo_laser("mainframe_channel",1,"safety_mode",0,"connection_id",'10','lab_interface','gpib');
|
||||
laser.setWavelength(conf.wavelength);
|
||||
laser.setPower(conf.laser_power);
|
||||
laser.enableLaser();
|
||||
end
|
||||
|
||||
% Optical Attenuator
|
||||
if ~exist('voa','var')
|
||||
voa = OptAtten("active",[1,2,1,1],"value",[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation],"wavelength",[conf.wavelength,conf.wavelength,conf.wavelength,conf.wavelength]);
|
||||
end
|
||||
voa.set('value',[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation]);
|
||||
|
||||
if voa.power_state(3) > -24 || voa.power_state(1)+ voa.atten_state(4) > -37
|
||||
holdAndShowValue
|
||||
end
|
||||
|
||||
% PDFA
|
||||
if ~exist('pdfa','var')
|
||||
pdfa = Thor_PDFA("safety_mode",0,"serialport_number",'COM15');
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
% if conf.symbolrate == 72e9
|
||||
% if conf.pam_level == 2
|
||||
% conf.v_awg = 0.55;
|
||||
% conf.pulsef_alpha = 0.6;
|
||||
% elseif conf.pam_level == 4
|
||||
% conf.v_awg = 0.55;
|
||||
% conf.pulsef_alpha =0.6;
|
||||
% elseif conf.pam_level == 6
|
||||
% conf.v_awg = 0.55;
|
||||
% conf.pulsef_alpha = 0.6;
|
||||
% elseif conf.pam_level == 8
|
||||
% conf.v_awg = 0.55;
|
||||
% conf.pulsef_alpha = 0.6;
|
||||
% end
|
||||
% elseif conf.symbolrate == 96e9
|
||||
% if conf.pam_level == 2
|
||||
% conf.v_awg = 0.8;
|
||||
% conf.pulsef_alpha = 0.2;
|
||||
% elseif conf.pam_level == 4
|
||||
% conf.v_awg = 0.8;
|
||||
% conf.pulsef_alpha = 0.2;
|
||||
% elseif conf.pam_level == 6
|
||||
% conf.v_awg = 0.8;
|
||||
% conf.pulsef_alpha = 0.2;
|
||||
% elseif conf.pam_level == 8
|
||||
% conf.v_awg = 0.8;
|
||||
% conf.pulsef_alpha = 0.2;
|
||||
% end
|
||||
% elseif conf.symbolrate == 112e9
|
||||
% if conf.pam_level == 2
|
||||
% conf.v_awg = 0.85;
|
||||
% conf.pulsef_alpha = 0.2;
|
||||
% elseif conf.pam_level == 4
|
||||
% conf.v_awg = 0.85;
|
||||
% conf.pulsef_alpha = 0.2;
|
||||
% elseif conf.pam_level == 6
|
||||
% conf.v_awg = 0.85;
|
||||
% conf.pulsef_alpha = 0.2;
|
||||
% elseif conf.pam_level == 8
|
||||
% conf.v_awg = 0.85;
|
||||
% conf.pulsef_alpha = 0.2;
|
||||
% end
|
||||
% end
|
||||
|
||||
awg_upload_required = 1;
|
||||
if any(confPrev.pam_level == conf.pam_level) && any(confPrev.symbolrate == conf.symbolrate)
|
||||
awg_upload_required = 0;
|
||||
end
|
||||
|
||||
%% Signal generation and transmission
|
||||
if awg_upload_required || isempty(loop_id)
|
||||
%%%%% Symbol Generation %%%%%%
|
||||
Pform = Pulseformer("fsym",conf.symbolrate,"fdac",8*conf.symbolrate,"pulse","rc","pulselength",16,"alpha",conf.pulsef_alpha);
|
||||
|
||||
if conf.symbolrate < 64e9
|
||||
sequence_order = min(18,sequence_order);
|
||||
end
|
||||
|
||||
% Construct AWG and Scope Modules %%%%%%
|
||||
fdac = 224e9;
|
||||
fadc = 160e9;
|
||||
Scp = ScopeKeysight("model","DSAZ634A",'autoscale',0,"fadc","GSa_160","channel",[0,0,1,0],"recordLen",recordlen,"removeDC",1,"extRef",1);
|
||||
Awg = AwgKeysight("model","M8199A_ILV","fdac",fdac,"scaletodac",[1,1],"skews",[0,0],"voltages",[0,conf.v_awg]);
|
||||
A2S = Awg2Scope(Awg,Scp,[0,0,3,0],"waitUntilClick",0);
|
||||
|
||||
Pamsource = PAMsource(...
|
||||
"fsym",conf.symbolrate,"M",conf.pam_level,"order",sequence_order,"useprbs",1,...
|
||||
"fs_out",fdac,...
|
||||
"applyclipping",0,...
|
||||
"clipfactor",4,...
|
||||
"applypulseform",1,...
|
||||
"pulseformer",Pform,...
|
||||
"randkey",1,...
|
||||
"duobinary_mode", conf.db_mode);
|
||||
|
||||
[Digi_sig,Symbols,Bits] = Pamsource.process();
|
||||
|
||||
figure(10);clf;
|
||||
Digi_sig.spectrum("displayname",'Tx Pulse Shaped','fignum',10,'normalizeTo0dB',1);
|
||||
|
||||
|
||||
%%%%% Precompensation Routine %%%%%%
|
||||
if precomp_mode == 1 % measure channel
|
||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',fdac);
|
||||
Digi_sig = precomp_est.buildOFDM();
|
||||
elseif precomp_mode == 2 % apply precomp
|
||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
|
||||
Digi_sig = precomp_est.precomp(Digi_sig,'maxampdb',conf.precomp_amp,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
end
|
||||
|
||||
Digi_sig.spectrum("displayname",'Tx Pre-Emphasized','fignum',10,'normalizeTo0dB',1);
|
||||
|
||||
%%%%% Resample to DAC rate %%%%%%
|
||||
Digi_sig = Digi_sig.resample("fs_out",Awg.fdac);
|
||||
|
||||
Digi_sig.spectrum("displayname",'Tx Resampled','fignum',10,'normalizeTo0dB',1);
|
||||
|
||||
% [~,~,Scpe_sig_raw,~] = A2S.process("signal2",Digi_sig);
|
||||
|
||||
Awg.upload("signal2",Digi_sig);
|
||||
end
|
||||
|
||||
% Precompensation Routine (optional)
|
||||
if precomp_mode == 1
|
||||
Scpe_sig_raw = Scp.read("channel",[0,0,1,0]);
|
||||
Scpe_sig_raw = Scpe_sig_raw{[0,0,1,0]==1};
|
||||
precomp_est.estimate(Scpe_sig_raw, "save", true, "savePath", precomp_path, "fileName", precomp_fn);
|
||||
precomp_est.plot();
|
||||
return; % End routine if precomp mode is active
|
||||
end
|
||||
|
||||
confPrev = conf;
|
||||
|
||||
%% Save static TX data (only once)
|
||||
currentTime = datetime('now', 'Format', 'yyyyMMdd_HHmmss');
|
||||
timeStr = char(currentTime);
|
||||
base_filename = sprintf('%s_PAM_%d_R_%d', timeStr, conf.pam_level, round(conf.symbolrate.*1e-9));
|
||||
|
||||
tx_bits_path = [filesep, current_folder, filesep, base_filename, '_bits'];
|
||||
save([savePath, tx_bits_path], "Bits");
|
||||
|
||||
tx_symbols_path = [filesep, current_folder, filesep, base_filename, '_symbols'];
|
||||
save([savePath, tx_symbols_path], "Symbols");
|
||||
|
||||
tx_signal_path = [filesep, current_folder, filesep, base_filename, '_signal'];
|
||||
save([savePath, tx_signal_path], "Digi_sig");
|
||||
|
||||
for recIdx = 1:n_recording
|
||||
fprintf('Recording %d / %d\n', recIdx, n_recording);
|
||||
|
||||
%% Acquire Scope Signal
|
||||
Scpe_sig_raw = Scp.read("channel",[0,0,1,0]);
|
||||
Scpe_sig_raw = Scpe_sig_raw{[0,0,1,0]==1};
|
||||
|
||||
% Precompensation Routine (optional)
|
||||
if precomp_mode == 1
|
||||
precomp_est.estimate(Scpe_sig_raw, "save", true, "savePath", precomp_path, "fileName", precomp_fn);
|
||||
precomp_est.plot();
|
||||
return; % End routine if precomp mode is active
|
||||
end
|
||||
|
||||
% Plot spectrum and time domain (optional)
|
||||
Scpe_sig_raw.spectrum("displayname", 'Rx Spectrum', 'fignum', 10, 'normalizeTo0dB', 0);
|
||||
Scpe_sig_raw.plot("clear", 1, "displayname", 'Rx Signal', 'fignum', 11);
|
||||
|
||||
%% Save Routine
|
||||
currentTime = datetime('now', 'Format', 'yyyyMMdd_HHmmss');
|
||||
timeStr = char(currentTime);
|
||||
filename = sprintf('%s_PAM_%d_R_%d_rec%02d', timeStr, conf.pam_level, round(conf.symbolrate.*1e-9), recIdx); % Added recIdx
|
||||
|
||||
rx_raw_path = [filesep, current_folder, filesep, filename, '_rx_signal_raw'];
|
||||
save([savePath, rx_raw_path], "Scpe_sig_raw");
|
||||
|
||||
% Check if loop_id is already created
|
||||
if isempty(loop_id)
|
||||
newLoop = db.tables.LoopControl;
|
||||
newLoop.loop_id = NaN;
|
||||
newLoop.date_of_loop = datetime('now', 'Format', 'yyyy-MM-dd HH:mm:ss');
|
||||
newLoop.description = 'Automated parameter sweep';
|
||||
loop_id = db.appendToTable('LoopControl', newLoop);
|
||||
|
||||
fprintf('LoopControl entry created: loop_id = %d\n', loop_id);
|
||||
end
|
||||
|
||||
%% Append to Runs Table
|
||||
newRun = db.tables.Runs;
|
||||
newRun.run_id = NaN;
|
||||
newRun.loop_id = loop_id;
|
||||
newRun.date_of_run = datetime(currentTime, 'InputFormat', 'yyyyMMdd_HHmmss');
|
||||
newRun.tx_bits_path = tx_bits_path;
|
||||
newRun.tx_symbols_path = tx_symbols_path;
|
||||
newRun.rx_sync_path = ""; % Leave empty for now
|
||||
newRun.rx_raw_path = rx_raw_path;
|
||||
newRun.filename = filename;
|
||||
newRun.tx_signal_path = tx_signal_path;
|
||||
|
||||
run_id = db.appendToTable('Runs', newRun);
|
||||
|
||||
%% Append to Configurations Table
|
||||
conf.configuration_id = NaN;
|
||||
conf.run_id = run_id;
|
||||
conf.bitrate = conf.symbolrate .* floor(log2(6)*10)/10;
|
||||
conf.pam_source = Pamsource;
|
||||
db.appendToTable('Configurations', conf);
|
||||
|
||||
%% Append to Measurements Table
|
||||
laser.getLaserInfo;
|
||||
meas = db.tables.Measurements;
|
||||
meas.measurement_id = NaN;
|
||||
meas.run_id = run_id;
|
||||
meas.power_laser = laser.cur_power;
|
||||
meas.power_rop = [];
|
||||
meas.power_pd_in = voa.power_state(2);
|
||||
meas.power_mpi_interference = voa.power_state(4);
|
||||
meas.power_mpi_signal = voa.power_state(3);
|
||||
meas.voa_class = voa;
|
||||
meas.pdfa_class = pdfa;
|
||||
meas.laser_class = laser;
|
||||
|
||||
% Ensure consistency
|
||||
assert(isequal(fieldnames(meas), fieldnames(db.tables.Measurements)), 'Fieldnames of "meas" do not match the reference structure!');
|
||||
|
||||
db.appendToTable('Measurements', meas);
|
||||
|
||||
%% Submit DSP Routine
|
||||
if recIdx == 1
|
||||
if subimt_DSP
|
||||
[out, a] = submit_dsp(run_id, databasePath, database_name, savePath, ...
|
||||
"parallel", parallel_dsp, "max_occurences", max_occurences);
|
||||
end
|
||||
if awg_upload_required
|
||||
[out, a] = submit_dsp(run_id, databasePath, database_name, savePath, ...
|
||||
"parallel", 0, "max_occurences", 1);
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
269
projects/ECOC_2025_MPI/plots_from_database/bias_vs_ber.m
Normal file
269
projects/ECOC_2025_MPI/plots_from_database/bias_vs_ber.m
Normal file
@@ -0,0 +1,269 @@
|
||||
|
||||
|
||||
basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
|
||||
database_name = 'ecoc2025.db';
|
||||
database = DBHandler("pathToDB", [basePath, database_name],"type",'mysql');
|
||||
|
||||
filterParams = database.tables;
|
||||
filterParams.Configurations = struct( ...
|
||||
'symbolrate', 112e9, ... %[224,336,360,390,420,448]
|
||||
'fiber_length', 0, ...
|
||||
'db_mode', '"no_db"', ...
|
||||
'interference_attenuation', [], ...
|
||||
'interference_path_length', [], ...
|
||||
'is_mpi', 1, ...
|
||||
'pam_level', 4, ...
|
||||
'wavelength', 1310, ...
|
||||
'precomp_amp', -64, ...
|
||||
'signal_attenuation', '0', ...
|
||||
'v_awg', [], ...
|
||||
'v_bias', [] ...
|
||||
);
|
||||
|
||||
% filterParams.EqualizerParameters.diff_precode = int32(db_mode.db_encoded);
|
||||
% filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
|
||||
|
||||
selectedFields = {'Configurations.run_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Configurations.bitrate' 'Configurations.v_bias' 'Configurations.v_awg' 'Configurations.precomp_amp' 'Configurations.symbolrate' 'Configurations.pam_level'...
|
||||
'Configurations.db_mode' 'Configurations.rop_attenuation' 'Configurations.is_mpi' 'Configurations.interference_attenuation' 'Configurations.signal_attenuation' ...
|
||||
'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'Measurements.power_pd_in' ...
|
||||
'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'};
|
||||
|
||||
[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
|
||||
|
||||
dataTable = cleanUpTable(dataTable);
|
||||
|
||||
% Filter by time
|
||||
startTime = datetime('2025-04-11 13:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
stopTime = datetime('2025-04-11 14:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
dataTable.date_of_run = datetime(dataTable.date_of_run, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable.date_of_processing = datetime(dataTable.date_of_processing, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable = dataTable(dataTable.date_of_processing > startTime, :);
|
||||
dataTable = dataTable(dataTable.date_of_processing < stopTime, :);
|
||||
|
||||
% Group by smth
|
||||
y_var = 'SNR';
|
||||
x_var = 'v_bias';
|
||||
|
||||
loop_var = 'v_awg';
|
||||
fixedVars = {'eq_id',loop_var};
|
||||
dataTableGrpd = groupIt(fixedVars,dataTable);
|
||||
|
||||
plotRealizations = 1;
|
||||
|
||||
% Create a new figure
|
||||
mkr = 'x';
|
||||
|
||||
figure(10);
|
||||
hold on
|
||||
|
||||
unique_loop_var = unique(dataTable.(loop_var));
|
||||
cols = linspecer(8);
|
||||
|
||||
for i = 1:numel(unique_loop_var)
|
||||
|
||||
idx = find(dataTable.(loop_var)== unique_loop_var(i), 1, 'first');
|
||||
% dispname = equalizer_structure(dataTable.equalizer_structure(idx));
|
||||
dispname = num2str(unique_loop_var(i));
|
||||
|
||||
% Plot SCATTERS: timestamp vs. interference_attenuation
|
||||
loop_filt_2 = dataTable.(loop_var)==unique_loop_var(i);
|
||||
if plotRealizations
|
||||
|
||||
y_values = dataTable.(y_var)(loop_filt_2,:);
|
||||
x_values = dataTable.(x_var)(loop_filt_2,:);
|
||||
y_values = double(y_values);
|
||||
x_values = double(x_values);
|
||||
|
||||
sc = scatter(x_values, y_values, 'LineWidth', 0.5,'Marker',mkr,'MarkerEdgeColor',cols(i,:),'HandleVisibility','on','DisplayName',string(dispname));
|
||||
|
||||
pair_one = {'Run ID', dataTable.run_id(loop_filt_2,:)};
|
||||
pair_two = {'Rate', dataTable.bitrate(loop_filt_2,:).*1e-9};
|
||||
pair_three = {'PD in', round(dataTable.power_pd_in(loop_filt_2,:),2)};
|
||||
addDatatips(sc, pair_one, pair_two,pair_three);
|
||||
xticks(unique(x_values));
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
% Label the axes and add a title
|
||||
legend('Interpreter','latex');
|
||||
xlabel(x_var);
|
||||
ylabel(y_var);
|
||||
title([x_var,' vs. ',y_var]);
|
||||
if y_var == 'BER'
|
||||
yline(3.8e-3,'LineWidth',1,'LineStyle','--','HandleVisibility','off');
|
||||
ylim([1e-4 0.5]);
|
||||
end
|
||||
% Enable grid for better readability
|
||||
grid on;
|
||||
|
||||
beautifyBERplot;
|
||||
|
||||
|
||||
|
||||
|
||||
function resultTable = groupIt(fixedVars,dataTable)
|
||||
|
||||
% Group by run_id and eq_id (adjust grouping keys as needed)
|
||||
|
||||
[G, groupKeys] = findgroups(dataTable(:, fixedVars));
|
||||
|
||||
% Preallocate a cell array for aggregated data.
|
||||
varNames = dataTable.Properties.VariableNames;
|
||||
nVars = numel(varNames);
|
||||
aggData = cell(height(groupKeys), nVars);
|
||||
groupCount = zeros(height(groupKeys), 1); % To store the size of each group
|
||||
|
||||
% Loop over each group.
|
||||
for i = 1:height(groupKeys)
|
||||
idx = (G == i); % Logical index for group i
|
||||
groupCount(i) = sum(idx); % Count number of rows in this group
|
||||
% For each variable in the table:
|
||||
for j = 1:nVars
|
||||
colData = dataTable.(varNames{j});
|
||||
if isnumeric(colData)
|
||||
% For numeric data, compute the mean.
|
||||
aggData{i, j} = min(colData(idx));
|
||||
else
|
||||
% For non-numeric data, take the first entry.
|
||||
if iscell(colData)
|
||||
aggData{i, j} = colData{find(idx, 1)};
|
||||
else
|
||||
aggData{i, j} = colData(find(idx, 1));
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% Convert the aggregated cell array into a table.
|
||||
resultTable = cell2table(aggData, 'VariableNames', varNames);
|
||||
|
||||
% Append the group count as a new column.
|
||||
resultTable.nRows = groupCount;
|
||||
|
||||
end
|
||||
|
||||
|
||||
function addDatatips(sc, varargin)
|
||||
% addDatatips Adds custom data tip rows to a scatter plot.
|
||||
%
|
||||
% addDatatips(sc, pair1, pair2, ...) adds one or more custom rows to the
|
||||
% data tip display of the scatter plot identified by sc.
|
||||
%
|
||||
% Each pair should be provided as a 1x2 cell array: {label, value}.
|
||||
% The value can be a scalar or a vector. If a vector is provided, its length
|
||||
% must match the number of scatter plot points.
|
||||
%
|
||||
% Example:
|
||||
% sc = scatter(x, y, 'LineWidth', 1.5, 'Marker', 'o');
|
||||
% pair_one = {'Attenuation', attenuationVector};
|
||||
% addDatatips(sc, pair_one);
|
||||
|
||||
numPoints = numel(sc.XData);
|
||||
|
||||
for k = 1:length(varargin)
|
||||
pair = varargin{k};
|
||||
|
||||
if ~iscell(pair) || numel(pair) ~= 2
|
||||
error('Each pair must be a 1x2 cell array: {label, value}.');
|
||||
end
|
||||
|
||||
label = pair{1};
|
||||
value = pair{2};
|
||||
|
||||
% If value is a vector, ensure its length is either 1 or equal to the number of scatter points.
|
||||
if isvector(value) && numel(value) ~= 1 && numel(value) ~= numPoints
|
||||
error('The vector for "%s" must be a scalar or have %d elements matching the scatter data points.', label, numPoints);
|
||||
end
|
||||
|
||||
% Create a new data tip row using the provided label and vector.
|
||||
newRow = dataTipTextRow(label, value);
|
||||
sc.DataTipTemplate.DataTipRows(end+1) = newRow;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
|
||||
function cleanedTable = cleanUpTable(inputTable)
|
||||
% cleanUpTable Cleans a MATLAB table where numbers and NaNs are stored as strings or structs.
|
||||
%
|
||||
% cleanedTable = cleanUpTable(inputTable)
|
||||
%
|
||||
% This function goes through all columns of the input table:
|
||||
% - Converts strings of numbers to numeric values
|
||||
% - Converts string 'NaN' and struct NaNs to real NaN
|
||||
% - Converts date strings to datetime (if possible)
|
||||
%
|
||||
% Input:
|
||||
% inputTable - MATLAB table with mixed types
|
||||
%
|
||||
% Output:
|
||||
% cleanedTable - Cleaned MATLAB table with proper numeric types
|
||||
|
||||
cleanedTable = inputTable;
|
||||
varNames = cleanedTable.Properties.VariableNames;
|
||||
|
||||
for i = 1:numel(varNames)
|
||||
col = cleanedTable.(varNames{i});
|
||||
|
||||
% Case 1: If it's a cell array (likely mixed strings/struct)
|
||||
if iscell(col)
|
||||
% Convert struct 'NaN' entries to string 'NaN'
|
||||
col = cellfun(@(x) convertStructToString(x), col, 'UniformOutput', false);
|
||||
|
||||
% Try to convert string numbers to actual numbers
|
||||
numericCol = str2double(col);
|
||||
|
||||
if all(isnan(numericCol) == strcmpi(col, 'NaN') | cellfun(@isempty, col))
|
||||
% If conversion is successful (NaNs correspond to 'NaN' strings), use it
|
||||
cleanedTable.(varNames{i}) = numericCol;
|
||||
else
|
||||
% Else, try to convert to datetime
|
||||
try
|
||||
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
catch
|
||||
% If it fails, leave as cell array of strings
|
||||
cleanedTable.(varNames{i}) = string(col);
|
||||
end
|
||||
end
|
||||
|
||||
% Case 2: If it's already a string array
|
||||
elseif isstring(col)
|
||||
numericCol = str2double(col);
|
||||
if all(isnan(numericCol) == strcmpi(col, "NaN"))
|
||||
cleanedTable.(varNames{i}) = numericCol;
|
||||
else
|
||||
% Try convert to datetime
|
||||
try
|
||||
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
catch
|
||||
% Leave as string
|
||||
end
|
||||
end
|
||||
|
||||
% Case 3: If it's already numeric, keep as is
|
||||
elseif isnumeric(col)
|
||||
continue;
|
||||
|
||||
% Case 4: If it's datetime, keep as is
|
||||
elseif isdatetime(col)
|
||||
continue;
|
||||
|
||||
else
|
||||
% Catch-all for unexpected types, convert to string
|
||||
cleanedTable.(varNames{i}) = string(col);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function out = convertStructToString(x)
|
||||
% Helper function to convert struct NaN to string 'NaN'
|
||||
if isstruct(x)
|
||||
out = "NaN";
|
||||
elseif isstring(x) || ischar(x)
|
||||
out = string(x);
|
||||
else
|
||||
out = x;
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,271 @@
|
||||
|
||||
|
||||
basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
|
||||
database_name = 'ecoc2025.db';
|
||||
database = DBHandler("pathToDB", [basePath, database_name]);
|
||||
|
||||
filterParams = database.tables;
|
||||
filterParams.Configurations = struct( ...
|
||||
'symbolrate', 112e9, ... %[224,336,360,390,420,448]
|
||||
'fiber_length', 0, ...
|
||||
'db_mode', '"no_db"', ...
|
||||
'interference_attenuation', [], ...
|
||||
'interference_path_length', [], ...
|
||||
'is_mpi', 0, ...
|
||||
'pam_level', 4, ...
|
||||
'wavelength', 1310, ...
|
||||
'precomp_amp', [], ...
|
||||
'signal_attenuation', [], ...
|
||||
'v_awg', 0.8, ...
|
||||
'v_bias', 2.8 ...
|
||||
);
|
||||
|
||||
% filterParams.EqualizerParameters.diff_precode = int32(db_mode.db_encoded);
|
||||
% filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
|
||||
|
||||
selectedFields = {'Configurations.run_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Configurations.bitrate' 'Configurations.v_bias' 'Configurations.v_awg' 'Configurations.precomp_amp' 'Configurations.symbolrate' 'Configurations.pam_level'...
|
||||
'Configurations.db_mode' 'Configurations.rop_attenuation' 'Configurations.is_mpi' 'Configurations.interference_attenuation' 'Configurations.signal_attenuation' ...
|
||||
'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'Measurements.power_pd_in' ...
|
||||
'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'};
|
||||
|
||||
[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
|
||||
|
||||
dataTable = cleanUpTable(dataTable);
|
||||
|
||||
% Filter by time
|
||||
startTime = datetime('2025-04-11 13:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
stopTime = datetime('2025-04-11 14:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
dataTable.date_of_run = datetime(dataTable.date_of_run, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable.date_of_processing = datetime(dataTable.date_of_processing, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable = dataTable(dataTable.date_of_processing > startTime, :);
|
||||
dataTable = dataTable(dataTable.date_of_processing < stopTime, :);
|
||||
|
||||
% Group by smth
|
||||
y_var = 'BER';
|
||||
x_var = 'interference_attenuation';
|
||||
|
||||
loop_var = 'eq_id';
|
||||
fixedVars = {'eq_id',loop_var};
|
||||
dataTableGrpd = groupIt(fixedVars,dataTable);
|
||||
|
||||
plotRealizations = 1;
|
||||
|
||||
% Create a new figure
|
||||
mkr = '*';
|
||||
|
||||
figure(10);
|
||||
hold on
|
||||
|
||||
unique_loop_var = unique(dataTable.(loop_var));
|
||||
cols = linspecer(8);
|
||||
|
||||
for i = 1:numel(unique_loop_var)
|
||||
|
||||
idx = find(dataTable.(loop_var)== unique_loop_var(i), 1, 'first');
|
||||
|
||||
dispname = equalizer_structure(dataTable.equalizer_structure(idx));
|
||||
|
||||
% dispname = num2str(unique_loop_var(i));
|
||||
|
||||
% Plot SCATTERS: timestamp vs. interference_attenuation
|
||||
loop_filt_2 = dataTable.(loop_var)==unique_loop_var(i);
|
||||
if plotRealizations
|
||||
|
||||
y_values = dataTable.(y_var)(loop_filt_2,:);
|
||||
x_values = dataTable.(x_var)(loop_filt_2,:);
|
||||
y_values = double(y_values);
|
||||
x_values = double(x_values);
|
||||
|
||||
sc = scatter(x_values, y_values, 'LineWidth', 0.5,'Marker',mkr,'MarkerEdgeColor',cols(i,:),'HandleVisibility','on','DisplayName',string(dispname));
|
||||
|
||||
pair_one = {'Run ID', dataTable.run_id(loop_filt_2,:)};
|
||||
pair_two = {'Rate', dataTable.bitrate(loop_filt_2,:).*1e-9};
|
||||
pair_three = {'PD in', round(dataTable.power_pd_in(loop_filt_2,:),2)};
|
||||
addDatatips(sc, pair_one, pair_two,pair_three);
|
||||
xticks(unique(x_values));
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
% Label the axes and add a title
|
||||
legend('Interpreter','latex');
|
||||
xlabel(x_var);
|
||||
ylabel(y_var);
|
||||
title([x_var,' vs. ',y_var]);
|
||||
if y_var == 'BER'
|
||||
yline(3.8e-3,'LineWidth',1,'LineStyle','--','HandleVisibility','off');
|
||||
ylim([1e-5 0.1]);
|
||||
end
|
||||
% Enable grid for better readability
|
||||
grid on;
|
||||
|
||||
beautifyBERplot;
|
||||
|
||||
|
||||
|
||||
|
||||
function resultTable = groupIt(fixedVars,dataTable)
|
||||
|
||||
% Group by run_id and eq_id (adjust grouping keys as needed)
|
||||
|
||||
[G, groupKeys] = findgroups(dataTable(:, fixedVars));
|
||||
|
||||
% Preallocate a cell array for aggregated data.
|
||||
varNames = dataTable.Properties.VariableNames;
|
||||
nVars = numel(varNames);
|
||||
aggData = cell(height(groupKeys), nVars);
|
||||
groupCount = zeros(height(groupKeys), 1); % To store the size of each group
|
||||
|
||||
% Loop over each group.
|
||||
for i = 1:height(groupKeys)
|
||||
idx = (G == i); % Logical index for group i
|
||||
groupCount(i) = sum(idx); % Count number of rows in this group
|
||||
% For each variable in the table:
|
||||
for j = 1:nVars
|
||||
colData = dataTable.(varNames{j});
|
||||
if isnumeric(colData)
|
||||
% For numeric data, compute the mean.
|
||||
aggData{i, j} = min(colData(idx));
|
||||
else
|
||||
% For non-numeric data, take the first entry.
|
||||
if iscell(colData)
|
||||
aggData{i, j} = colData{find(idx, 1)};
|
||||
else
|
||||
aggData{i, j} = colData(find(idx, 1));
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% Convert the aggregated cell array into a table.
|
||||
resultTable = cell2table(aggData, 'VariableNames', varNames);
|
||||
|
||||
% Append the group count as a new column.
|
||||
resultTable.nRows = groupCount;
|
||||
|
||||
end
|
||||
|
||||
|
||||
function addDatatips(sc, varargin)
|
||||
% addDatatips Adds custom data tip rows to a scatter plot.
|
||||
%
|
||||
% addDatatips(sc, pair1, pair2, ...) adds one or more custom rows to the
|
||||
% data tip display of the scatter plot identified by sc.
|
||||
%
|
||||
% Each pair should be provided as a 1x2 cell array: {label, value}.
|
||||
% The value can be a scalar or a vector. If a vector is provided, its length
|
||||
% must match the number of scatter plot points.
|
||||
%
|
||||
% Example:
|
||||
% sc = scatter(x, y, 'LineWidth', 1.5, 'Marker', 'o');
|
||||
% pair_one = {'Attenuation', attenuationVector};
|
||||
% addDatatips(sc, pair_one);
|
||||
|
||||
numPoints = numel(sc.XData);
|
||||
|
||||
for k = 1:length(varargin)
|
||||
pair = varargin{k};
|
||||
|
||||
if ~iscell(pair) || numel(pair) ~= 2
|
||||
error('Each pair must be a 1x2 cell array: {label, value}.');
|
||||
end
|
||||
|
||||
label = pair{1};
|
||||
value = pair{2};
|
||||
|
||||
% If value is a vector, ensure its length is either 1 or equal to the number of scatter points.
|
||||
if isvector(value) && numel(value) ~= 1 && numel(value) ~= numPoints
|
||||
error('The vector for "%s" must be a scalar or have %d elements matching the scatter data points.', label, numPoints);
|
||||
end
|
||||
|
||||
% Create a new data tip row using the provided label and vector.
|
||||
newRow = dataTipTextRow(label, value);
|
||||
sc.DataTipTemplate.DataTipRows(end+1) = newRow;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
|
||||
function cleanedTable = cleanUpTable(inputTable)
|
||||
% cleanUpTable Cleans a MATLAB table where numbers and NaNs are stored as strings or structs.
|
||||
%
|
||||
% cleanedTable = cleanUpTable(inputTable)
|
||||
%
|
||||
% This function goes through all columns of the input table:
|
||||
% - Converts strings of numbers to numeric values
|
||||
% - Converts string 'NaN' and struct NaNs to real NaN
|
||||
% - Converts date strings to datetime (if possible)
|
||||
%
|
||||
% Input:
|
||||
% inputTable - MATLAB table with mixed types
|
||||
%
|
||||
% Output:
|
||||
% cleanedTable - Cleaned MATLAB table with proper numeric types
|
||||
|
||||
cleanedTable = inputTable;
|
||||
varNames = cleanedTable.Properties.VariableNames;
|
||||
|
||||
for i = 1:numel(varNames)
|
||||
col = cleanedTable.(varNames{i});
|
||||
|
||||
% Case 1: If it's a cell array (likely mixed strings/struct)
|
||||
if iscell(col)
|
||||
% Convert struct 'NaN' entries to string 'NaN'
|
||||
col = cellfun(@(x) convertStructToString(x), col, 'UniformOutput', false);
|
||||
|
||||
% Try to convert string numbers to actual numbers
|
||||
numericCol = str2double(col);
|
||||
|
||||
if all(isnan(numericCol) == strcmpi(col, 'NaN') | cellfun(@isempty, col))
|
||||
% If conversion is successful (NaNs correspond to 'NaN' strings), use it
|
||||
cleanedTable.(varNames{i}) = numericCol;
|
||||
else
|
||||
% Else, try to convert to datetime
|
||||
try
|
||||
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
catch
|
||||
% If it fails, leave as cell array of strings
|
||||
cleanedTable.(varNames{i}) = string(col);
|
||||
end
|
||||
end
|
||||
|
||||
% Case 2: If it's already a string array
|
||||
elseif isstring(col)
|
||||
numericCol = str2double(col);
|
||||
if all(isnan(numericCol) == strcmpi(col, "NaN"))
|
||||
cleanedTable.(varNames{i}) = numericCol;
|
||||
else
|
||||
% Try convert to datetime
|
||||
try
|
||||
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
catch
|
||||
% Leave as string
|
||||
end
|
||||
end
|
||||
|
||||
% Case 3: If it's already numeric, keep as is
|
||||
elseif isnumeric(col)
|
||||
continue;
|
||||
|
||||
% Case 4: If it's datetime, keep as is
|
||||
elseif isdatetime(col)
|
||||
continue;
|
||||
|
||||
else
|
||||
% Catch-all for unexpected types, convert to string
|
||||
cleanedTable.(varNames{i}) = string(col);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function out = convertStructToString(x)
|
||||
% Helper function to convert struct NaN to string 'NaN'
|
||||
if isstruct(x)
|
||||
out = "NaN";
|
||||
elseif isstring(x) || ischar(x)
|
||||
out = string(x);
|
||||
else
|
||||
out = x;
|
||||
end
|
||||
end
|
||||
599
projects/ECOC_2025_MPI/plots_from_database/plot_mpi_trial.m
Normal file
599
projects/ECOC_2025_MPI/plots_from_database/plot_mpi_trial.m
Normal file
@@ -0,0 +1,599 @@
|
||||
|
||||
|
||||
% dsp_options.database_type = 'mysql';
|
||||
% dsp_options.dataBase = 'labor';
|
||||
% dsp_options.storage_path = 'Z:\2025\ECOC Silas\ecoc_2025\';
|
||||
% database = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
|
||||
% filterParams = database.tables;
|
||||
% filterParams.Runs.loop_id = 209;
|
||||
% % filterParams.Configurations = struct( ...
|
||||
% % 'symbolrate', 112e9, ... %[224,336,360,390,420,448]
|
||||
% % 'fiber_length', 0, ...
|
||||
% % 'db_mode', '"no_db"', ...
|
||||
% % 'interference_attenuation', [], ...
|
||||
% % 'interference_path_length', 1000, ...
|
||||
% % 'is_mpi', 1, ...
|
||||
% % 'pam_level', 4, ...
|
||||
% % 'wavelength', 1310, ...
|
||||
% % 'precomp_amp', [], ...
|
||||
% % 'signal_attenuation', [], ...
|
||||
% % 'v_awg', [], ...
|
||||
% % 'v_bias', [] ...
|
||||
% % );
|
||||
%
|
||||
% % if 1
|
||||
% % % filterParams.EqualizerParameters.dc_buffer_len = 1;
|
||||
% % filterParams.EqualizerParameters.ffe_buffer_len = 1;
|
||||
% % filterParams.EqualizerParameters.smoothing_buffer_len = 4096;
|
||||
% % filterParams.EqualizerParameters.smoothing_buffer_update = 224;
|
||||
% % filterParams.EqualizerParameters.DCmu = 0;
|
||||
% % end
|
||||
% a = database.getTableFieldNames('Runs');
|
||||
% b = database.getTableFieldNames('Results');
|
||||
% c = database.getTableFieldNames('EqualizerParameters');
|
||||
% d = [a;b;c];
|
||||
%
|
||||
% [dataTable,~] = database.queryDB(filterParams, d);
|
||||
%
|
||||
% selectedFields = {'Configurations.run_id' 'Runs.loop_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Runs.bitrate' 'Runs.v_bias' 'Runs.v_awg' 'Runs.precomp_amp' 'Runs.symbolrate' 'Runs.pam_level'...
|
||||
% 'Runs.db_mode' 'Runs.rop_attenuation' 'Runs.is_mpi' 'Runs.interference_attenuation' 'Runs.interference_path_length' 'Runs.signal_attenuation' ...
|
||||
% 'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'EqualizerParameters.dc_buffer_len' 'EqualizerParameters.ffe_buffer_len' 'EqualizerParameters.smoothing_buffer_len' 'EqualizerParameters.smoothing_buffer_update' 'EqualizerParameters.DCmu' 'Measurements.power_pd_in' ...
|
||||
% 'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.EVM' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'};
|
||||
|
||||
db = DBHandler("type","mysql","dataBase",'labor');
|
||||
|
||||
fp = QueryFilter();
|
||||
% fp.where('mpi_superview', 'loop_id','EQUALS', 209);
|
||||
fp.where('mpi_superview', 'symbolrate','EQUALS', 112e9);
|
||||
fp.where('mpi_superview', 'pam_level','EQUALS', 4);
|
||||
fn = [db.getTableFieldNames('mpi_superview')];
|
||||
[dataTable,sql_query] = db.queryDB(fp,fn);
|
||||
|
||||
|
||||
%%
|
||||
|
||||
dataTable_clean = dataTable;
|
||||
dataTable_clean.SIR = -7 - round(dataTable_clean.power_mpi_interference);
|
||||
dataTable_clean.NGMI = dataTable_clean.GMI ./ log2(dataTable_clean.pam_level);
|
||||
dataTable_clean = cleanUpTable(dataTable_clean);
|
||||
|
||||
dataTable_clean(dataTable_clean.BER>0.2,:) = [];
|
||||
|
||||
%%
|
||||
|
||||
|
||||
cols = linspecer(8); % Ensure color count matches
|
||||
figure()
|
||||
tiledlayout(1, 4, 'TileSpacing', 'compact', 'Padding', 'compact');
|
||||
y_here = 0;
|
||||
figcnt = 0;
|
||||
for int_len = [0,50,300,1000]
|
||||
figcnt = figcnt+1;
|
||||
% figure(int_len+1);
|
||||
nexttile;
|
||||
hold on
|
||||
mode = 4;
|
||||
|
||||
|
||||
for mode = [1,2]
|
||||
|
||||
hold on;
|
||||
dataTable = dataTable_clean;
|
||||
|
||||
|
||||
plotBoundaries = 1;
|
||||
plotRealizations = 1;
|
||||
cols = linspecer(8); % Ensure color count matches
|
||||
|
||||
if mode == 1
|
||||
% No compensation method
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0, :);
|
||||
cols = cols(1:1+1,:);
|
||||
method = 'ffe only';
|
||||
|
||||
% slow DC smoothing
|
||||
elseif mode == 2
|
||||
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 4096, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 224, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0, :);
|
||||
|
||||
cols = cols(4:4+1,:);
|
||||
method = 'dc smoothing';
|
||||
|
||||
|
||||
% slow DC tracking
|
||||
elseif mode == 3
|
||||
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 224, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0.005, :);
|
||||
|
||||
cols = cols(3:3+1,:);
|
||||
method = 'parallelized dc tracking';
|
||||
|
||||
elseif mode == 4
|
||||
|
||||
% ideal DC tracking
|
||||
dataTable = dataTable(dataTable.dc_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.ffe_buffer_len == 1, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_len == 0, :);
|
||||
dataTable = dataTable(dataTable.smoothing_buffer_update == 0, :);
|
||||
dataTable = dataTable(dataTable.DCmu == 0.005, :);
|
||||
|
||||
cols = cols(2:2+1,:);
|
||||
method = 'ideal dc tracking';
|
||||
end
|
||||
|
||||
% dataTable(dataTable.eq_id==0,:) = [];
|
||||
dataTable(dataTable.equalizer_structure~=1,:) = [];
|
||||
|
||||
% Modify values in 'interference_path_length' where the condition is met
|
||||
dataTable.interference_path_length(dataTable.interference_path_length < 101 & dataTable.interference_path_length > 1) = 50;
|
||||
|
||||
dataTable.interference_path_length(dataTable.interference_path_length == 1) = 0;
|
||||
dataTable = dataTable(dataTable.interference_path_length == int_len, :);
|
||||
|
||||
|
||||
dataTable(dataTable.run_id == 3866, :) = [];
|
||||
dataTable(dataTable.run_id == 3865, :) = [];
|
||||
dataTable(dataTable.run_id == 3796, :) = [];
|
||||
dataTable(dataTable.run_id == 3797, :) = [];
|
||||
dataTable(dataTable.run_id == 3798, :) = [];
|
||||
dataTable(dataTable.run_id == 4002, :) = [];
|
||||
dataTable(dataTable.run_id == 4200, :) = [];
|
||||
dataTable(dataTable.run_id == 4199, :) = [];
|
||||
|
||||
% 0
|
||||
% 1
|
||||
% 10
|
||||
% 15
|
||||
% 20
|
||||
% 50
|
||||
% 100
|
||||
% 300
|
||||
% 1000
|
||||
|
||||
% dataTable(dataTable.interference_path_length ~= 50, :) = [];
|
||||
% dataTable = dataTable(dataTable.interference_path_length < 51, :);
|
||||
|
||||
% dataTable(dataTable.loop_id<200,:) = [];
|
||||
|
||||
% Filter by time
|
||||
filter_by_time = 0;
|
||||
if filter_by_time
|
||||
startTime = datetime('2025-04-20 18:00:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
stopTime = datetime('2025-04-30 19:30:00', 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
dataTable.date_of_run = datetime(dataTable.date_of_run, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable.date_of_processing = datetime(dataTable.date_of_processing, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
dataTable = dataTable(dataTable.date_of_processing > startTime, :);
|
||||
dataTable = dataTable(dataTable.date_of_processing < stopTime, :);
|
||||
end
|
||||
|
||||
% Group by smth
|
||||
y_var = 'BER';
|
||||
x_var = 'SIR';
|
||||
|
||||
loop_var = 'interference_path_length';
|
||||
fixedVars = {'equalizer_structure','interference_path_length',x_var};
|
||||
|
||||
[dataTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var);
|
||||
|
||||
dataTableGrpd_mean = groupIt(fixedVars, dataTable, @mean);
|
||||
dataTableGrpd_min = groupIt(fixedVars, dataTable, @min);
|
||||
dataTableGrpd_max = groupIt(fixedVars, dataTable, @max);
|
||||
|
||||
% dataTableGrpd_mean(dataTableGrpd_mean.nRows<50,:) = [];
|
||||
% dataTableGrpd_min(dataTableGrpd_min.nRows<50,:) = [];
|
||||
% dataTableGrpd_max(dataTableGrpd_max.nRows<50,:) = [];
|
||||
|
||||
% Create a new figure
|
||||
|
||||
hold on
|
||||
|
||||
unique_loop_var = unique(dataTable.(loop_var));
|
||||
|
||||
for i = 1:numel(unique_loop_var)
|
||||
|
||||
% Prepare filtered data for this loop variable
|
||||
loopValue = unique_loop_var(i);
|
||||
|
||||
loopFiltGrpd = dataTableGrpd_mean.(loop_var) == loopValue;
|
||||
if ~any(loopFiltGrpd)
|
||||
continue; % Skip if no data for this loop var
|
||||
end
|
||||
|
||||
% Extract values
|
||||
x_values = dataTableGrpd_mean.(x_var)(loopFiltGrpd, :);
|
||||
y_mean = dataTableGrpd_mean.(y_var)(loopFiltGrpd, :);
|
||||
y_min = dataTableGrpd_min.(y_var)(loopFiltGrpd, :);
|
||||
y_max = dataTableGrpd_max.(y_var)(loopFiltGrpd, :);
|
||||
|
||||
% Compute bounds: distance from mean
|
||||
y_lower = y_mean - y_min;
|
||||
y_upper = y_max - y_mean;
|
||||
y_bounds = [y_lower, y_upper];
|
||||
|
||||
% Display name (optional)
|
||||
try
|
||||
idx = find(dataTable.(loop_var) == loopValue, 1, 'first');
|
||||
% dispname = char(equalizer_structure(dataTable.equalizer_structure(idx)));
|
||||
dispname = [method];
|
||||
dispname = [dispname, '/ ',num2str(unique_loop_var(i)) ,' m'];
|
||||
% dispname = [dispname,'; ',num2str(unique(dataTable.interference_path_length)),' m'];
|
||||
% dispname = [dispname, '/ PAM ', num2str(filterParams.Configurations.pam_level)];
|
||||
% dispname = [dispname, '/ ', num2str(filterParams.Configurations.symbolrate.*1e-9),' GBd'];
|
||||
end
|
||||
|
||||
if plotBoundaries
|
||||
% Plot bounded line
|
||||
[hl, hp] = boundedline(x_values, y_mean, y_bounds, ...
|
||||
'alpha', 'transparency', 0.1, ...
|
||||
'cmap', cols(i,:), ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
|
||||
% % Style the main line: thinnest, dotted, no marker
|
||||
set(hl, 'LineWidth', 0.5, 'LineStyle', ':', 'Marker', 'none', ...
|
||||
'Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
|
||||
plt = errorbar(x_values,y_mean,y_lower,y_upper,'LineWidth', 0.9, 'LineStyle', 'none', 'Marker', 'none','Color', cols(i,:), 'DisplayName', string(dispname),'HandleVisibility','off');
|
||||
|
||||
% Hide patch (shaded area) from legend
|
||||
set(hp, 'HandleVisibility', 'off','LineStyle',':','LineWidth',0.5,'Marker','none');
|
||||
|
||||
|
||||
|
||||
% Fit a 4th-order polynomial to log10(BER)
|
||||
p = polyfit(x_values, log10(y_mean), 3); % 4 is fitting order, adjust as needed
|
||||
|
||||
% Evaluate the fitted polynomial
|
||||
x_fit = linspace(min(x_values), max(x_values), 300); % Fine points
|
||||
y_fit_log = polyval(p, x_fit); % Still in log10 domain
|
||||
y_fit = 10.^y_fit_log; % Back to BER domain
|
||||
|
||||
|
||||
|
||||
plot(x_fit,y_fit,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none', ...
|
||||
'Color', cols(i,:), 'DisplayName', string(dispname),'HandleVisibility','off');
|
||||
|
||||
% % Add invisible scatter for DataTips
|
||||
% plt = scatter(x_values, y_mean, ...
|
||||
% 'Marker', 'o', 'MarkerEdgeColor', 'none', 'MarkerFaceColor', 'none', ...
|
||||
% 'HandleVisibility', 'off', 'PickableParts', 'all');
|
||||
else
|
||||
|
||||
plt= plot(x_values,y_mean,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none', ...
|
||||
'Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
% plt= errorbar(x_values,y_mean,y_lower,y_upper,'LineWidth', 1, 'LineStyle', '-', 'Marker', 'none','Color', cols(i,:), 'DisplayName', string(dispname));
|
||||
|
||||
end
|
||||
% Add data tips to the invisible scatter
|
||||
pair_one = {'Run ID', dataTableGrpd_mean.run_id(loopFiltGrpd, :)};
|
||||
pair_two = {'Rate', dataTableGrpd_mean.bitrate(loopFiltGrpd, :) * 1e-9};
|
||||
pair_three = {'PD in', round(dataTableGrpd_mean.power_pd_in(loopFiltGrpd, :), 2)};
|
||||
addDatatips(plt, pair_one, pair_two, pair_three);
|
||||
|
||||
|
||||
|
||||
% Optionally: outline bounds for better visibility (optional)
|
||||
% hnew = outlinebounds(hl, hp);
|
||||
|
||||
% Tick marks (x-axis)
|
||||
|
||||
|
||||
% Optional: scatter realizations
|
||||
if plotRealizations
|
||||
loopFiltSingle = dataTable.(loop_var) == loopValue;
|
||||
x_single = double(dataTable.(x_var)(loopFiltSingle, :));
|
||||
y_single = double(dataTable.(y_var)(loopFiltSingle, :));
|
||||
|
||||
mkr = '.';
|
||||
sc = scatter(x_single+(mode*0.1)-0.2, y_single,15, 'Marker', mkr, 'MarkerEdgeColor', cols(i, :), ...
|
||||
'LineWidth', 0.5, 'HandleVisibility', 'off', 'DisplayName', string(dispname));
|
||||
|
||||
pair_one = {'Run ID', dataTable.run_id(loopFiltSingle, :)};
|
||||
pair_two = {'Baud', dataTable.symbolrate(loopFiltSingle, :) * 1e-9};
|
||||
pair_three = {'PD in', round(dataTable.power_pd_in(loopFiltSingle, :), 2)};
|
||||
pair_four = {'#bits', round(dataTable.numBits(loopFiltSingle, :), 2)};
|
||||
addDatatips(sc, pair_one, pair_two, pair_three,pair_four);
|
||||
end
|
||||
end
|
||||
|
||||
% Label axes and title
|
||||
legend('Interpreter', 'latex');
|
||||
xlabel(x_var);
|
||||
if ~y_here
|
||||
ylabel(y_var);
|
||||
yticklabels = [];
|
||||
|
||||
y_here = 1;
|
||||
end
|
||||
if int_len ~= 0
|
||||
set(gca, 'YTick', []);
|
||||
end
|
||||
% title([x_var, ' vs. ', y_var]);
|
||||
|
||||
if string(y_var) == "BER"
|
||||
yline(2.2e-4, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
yline(3.8e-3, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
yline(2e-2, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
ylim([1e-5, 0.1]);
|
||||
end
|
||||
|
||||
xlim([15,35]);
|
||||
ylim([9e-5 0.1 ]);
|
||||
xticks([13:2:35]);
|
||||
|
||||
% Enable grid and beautify
|
||||
grid on;
|
||||
beautifyBERplot;
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function resultTable = groupIt(fixedVars, dataTable, aggregationFunction)
|
||||
% groupIt Groups data in a table based on fixedVars and applies aggregationFunction to numeric data.
|
||||
%
|
||||
% resultTable = groupIt(fixedVars, dataTable, aggregationFunction)
|
||||
%
|
||||
% Inputs:
|
||||
% fixedVars - Cell array of variable names to group by
|
||||
% dataTable - Input MATLAB table
|
||||
% aggregationFunction - Function handle (e.g., @mean, @min, @max)
|
||||
%
|
||||
% Output:
|
||||
% resultTable - Grouped and aggregated table
|
||||
|
||||
% Group data
|
||||
[G, groupKeys] = findgroups(dataTable(:, fixedVars));
|
||||
|
||||
% Prepare aggregation
|
||||
varNames = dataTable.Properties.VariableNames;
|
||||
nVars = numel(varNames);
|
||||
aggData = cell(height(groupKeys), nVars);
|
||||
groupCount = zeros(height(groupKeys), 1); % Store number of rows in each group
|
||||
|
||||
% Loop over groups
|
||||
for i = 1:height(groupKeys)
|
||||
idx = (G == i); % Logical index for group i
|
||||
groupCount(i) = sum(idx); % Count rows in group
|
||||
|
||||
% Loop over each variable
|
||||
for j = 1:nVars
|
||||
colData = dataTable.(varNames{j});
|
||||
|
||||
if isnumeric(colData)
|
||||
% Numeric: apply aggregation function (skip empty groups safely)
|
||||
if any(idx)
|
||||
% aggData{i, j} = rmoutliers(double(colData(idx)));
|
||||
aggData{i, j} = aggregationFunction(colData(idx));
|
||||
else
|
||||
aggData{i, j} = NaN;
|
||||
end
|
||||
else
|
||||
% Non-numeric: take first non-empty value
|
||||
if iscell(colData)
|
||||
nonEmptyIdx = find(idx & ~cellfun(@isempty, colData), 1);
|
||||
if ~isempty(nonEmptyIdx)
|
||||
aggData{i, j} = colData{nonEmptyIdx};
|
||||
else
|
||||
aggData{i, j} = [];
|
||||
end
|
||||
else
|
||||
nonEmptyIdx = find(idx, 1);
|
||||
if ~isempty(nonEmptyIdx)
|
||||
aggData{i, j} = colData(nonEmptyIdx);
|
||||
else
|
||||
aggData{i, j} = [];
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% Convert aggregated data to table
|
||||
resultTable = cell2table(aggData, 'VariableNames', varNames);
|
||||
|
||||
% Add group size as new column
|
||||
resultTable.nRows = groupCount;
|
||||
end
|
||||
|
||||
|
||||
function addDatatips(sc, varargin)
|
||||
% addDatatips Adds custom data tip rows to a scatter plot.
|
||||
%
|
||||
% addDatatips(sc, pair1, pair2, ...) adds one or more custom rows to the
|
||||
% data tip display of the scatter plot identified by sc.
|
||||
%
|
||||
% Each pair should be provided as a 1x2 cell array: {label, value}.
|
||||
% The value can be a scalar or a vector. If a vector is provided, its length
|
||||
% must match the number of scatter plot points.
|
||||
%
|
||||
% Example:
|
||||
% sc = scatter(x, y, 'LineWidth', 1.5, 'Marker', 'o');
|
||||
% pair_one = {'Attenuation', attenuationVector};
|
||||
% addDatatips(sc, pair_one);
|
||||
|
||||
numPoints = numel(sc.XData);
|
||||
|
||||
for k = 1:length(varargin)
|
||||
pair = varargin{k};
|
||||
|
||||
if ~iscell(pair) || numel(pair) ~= 2
|
||||
error('Each pair must be a 1x2 cell array: {label, value}.');
|
||||
end
|
||||
|
||||
label = pair{1};
|
||||
value = pair{2};
|
||||
|
||||
% If value is a vector, ensure its length is either 1 or equal to the number of scatter points.
|
||||
if isvector(value) && numel(value) ~= 1 && numel(value) ~= numPoints
|
||||
error('The vector for "%s" must be a scalar or have %d elements matching the scatter data points.', label, numPoints);
|
||||
end
|
||||
|
||||
% Create a new data tip row using the provided label and vector.
|
||||
newRow = dataTipTextRow(label, value);
|
||||
sc.DataTipTemplate.DataTipRows(end+1) = newRow;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function cleanedTable = cleanUpTable(inputTable)
|
||||
% cleanUpTable Cleans a MATLAB table where numbers and NaNs are stored as strings or structs.
|
||||
%
|
||||
% cleanedTable = cleanUpTable(inputTable)
|
||||
%
|
||||
% This function goes through all columns of the input table:
|
||||
% - Converts strings of numbers to numeric values
|
||||
% - Converts string 'NaN' and struct NaNs to real NaN
|
||||
% - Converts date strings to datetime (if possible)
|
||||
%
|
||||
% Input:
|
||||
% inputTable - MATLAB table with mixed types
|
||||
%
|
||||
% Output:
|
||||
% cleanedTable - Cleaned MATLAB table with proper numeric types
|
||||
|
||||
cleanedTable = inputTable;
|
||||
varNames = cleanedTable.Properties.VariableNames;
|
||||
|
||||
for i = 1:numel(varNames)
|
||||
col = cleanedTable.(varNames{i});
|
||||
|
||||
% Case 1: If it's a cell array (likely mixed strings/struct)
|
||||
if iscell(col)
|
||||
% Convert struct 'NaN' entries to string 'NaN'
|
||||
col = cellfun(@(x) convertStructToString(x), col, 'UniformOutput', false);
|
||||
|
||||
% Try to convert string numbers to actual numbers
|
||||
numericCol = str2double(col);
|
||||
|
||||
if all(isnan(numericCol) == strcmpi(col, 'NaN') | cellfun(@isempty, col))
|
||||
% If conversion is successful (NaNs correspond to 'NaN' strings), use it
|
||||
cleanedTable.(varNames{i}) = numericCol;
|
||||
else
|
||||
% Else, try to convert to datetime
|
||||
try
|
||||
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
|
||||
catch
|
||||
% If it fails, leave as cell array of strings
|
||||
cleanedTable.(varNames{i}) = string(col);
|
||||
end
|
||||
end
|
||||
|
||||
% Case 2: If it's already a string array
|
||||
elseif isstring(col)
|
||||
numericCol = str2double(col);
|
||||
if all(isnan(numericCol) == strcmpi(col, "NaN"))
|
||||
cleanedTable.(varNames{i}) = numericCol;
|
||||
else
|
||||
% Try convert to datetime
|
||||
try
|
||||
cleanedTable.(varNames{i}) = datetime(col, 'InputFormat', 'yyyy-MM-dd HH:mm:ss');
|
||||
catch
|
||||
% Leave as string
|
||||
end
|
||||
end
|
||||
|
||||
% Case 3: If it's already numeric, keep as is
|
||||
elseif isnumeric(col)
|
||||
continue;
|
||||
|
||||
% Case 4: If it's datetime, keep as is
|
||||
elseif isdatetime(col)
|
||||
continue;
|
||||
|
||||
else
|
||||
% Catch-all for unexpected types, convert to string
|
||||
cleanedTable.(varNames{i}) = string(col);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function out = convertStructToString(x)
|
||||
% Helper function to convert struct NaN to string 'NaN'
|
||||
if isstruct(x)
|
||||
out = "NaN";
|
||||
elseif isstring(x) || ischar(x)
|
||||
out = string(x);
|
||||
else
|
||||
out = x;
|
||||
end
|
||||
end
|
||||
|
||||
function [cleanedTable, outliersTable] = removeGroupOutliers(dataTable, fixedVars, y_var)
|
||||
|
||||
% Group the data
|
||||
[G, groupKeys] = findgroups(dataTable(:, fixedVars));
|
||||
|
||||
% Initialize logical index to keep rows
|
||||
keepIdx = true(height(dataTable), 1);
|
||||
|
||||
% Prepare storage for outliers
|
||||
outlierRecords = [];
|
||||
|
||||
% Loop over each group
|
||||
for groupIdx = 1:height(groupKeys)
|
||||
% Find indices of current group
|
||||
groupRows = (G == groupIdx);
|
||||
|
||||
% Extract y-values of this group
|
||||
y_values = dataTable.(y_var)(groupRows);
|
||||
|
||||
% Skip groups with fewer than 3 points (optional)
|
||||
if sum(groupRows) < 3
|
||||
continue;
|
||||
end
|
||||
|
||||
% Detect outliers in log space
|
||||
y_log = log10(y_values);
|
||||
outlierMask = isoutlier(y_log, 'quartiles',1); % or 'median', 'grubbs', etc.
|
||||
|
||||
% If any outliers found, collect their data
|
||||
if any(outlierMask)
|
||||
groupData = dataTable(groupRows, :);
|
||||
|
||||
% Prepare table for current group outliers
|
||||
outlierGroupTable = groupData(outlierMask, :);
|
||||
|
||||
% Add group key values for traceability
|
||||
for k = 1:numel(fixedVars)
|
||||
outlierGroupTable.(['Group_', fixedVars{k}]) = repmat(groupKeys{groupIdx, k}, height(outlierGroupTable), 1);
|
||||
end
|
||||
|
||||
% Append to collection
|
||||
outlierRecords = [outlierRecords; outlierGroupTable]; %#ok<AGROW>
|
||||
end
|
||||
|
||||
% Mark outliers for removal
|
||||
groupRowIdx = find(groupRows);
|
||||
keepIdx(groupRowIdx(outlierMask)) = false;
|
||||
end
|
||||
|
||||
% Apply mask to dataTable
|
||||
cleanedTable = dataTable(keepIdx, :);
|
||||
|
||||
% Prepare output: if no outliers, return empty table
|
||||
if isempty(outlierRecords)
|
||||
outliersTable = table();
|
||||
else
|
||||
outliersTable = outlierRecords;
|
||||
end
|
||||
|
||||
nRemoved = sum(~keepIdx);
|
||||
nTotalOriginal = height(dataTable) + nRemoved;
|
||||
percentageRemoved = (nRemoved / nTotalOriginal) * 100;
|
||||
|
||||
fprintf('Removed %d outliers from the data table (%.2f%% of total %d entries).\n', ...
|
||||
nRemoved, percentageRemoved, nTotalOriginal);
|
||||
end
|
||||
BIN
projects/ECOC_2025_MPI/precomp_1km.mat
Normal file
BIN
projects/ECOC_2025_MPI/precomp_1km.mat
Normal file
Binary file not shown.
BIN
projects/ECOC_2025_MPI/precomp_1km_1mm_cable.mat
Normal file
BIN
projects/ECOC_2025_MPI/precomp_1km_1mm_cable.mat
Normal file
Binary file not shown.
BIN
projects/ECOC_2025_MPI/precomp_1km_1mm_cable_100ghz_pd.mat
Normal file
BIN
projects/ECOC_2025_MPI/precomp_1km_1mm_cable_100ghz_pd.mat
Normal file
Binary file not shown.
BIN
projects/ECOC_2025_MPI/precomp_1km_1mm_cable_70ghz_pd.mat
Normal file
BIN
projects/ECOC_2025_MPI/precomp_1km_1mm_cable_70ghz_pd.mat
Normal file
Binary file not shown.
Binary file not shown.
Binary file not shown.
BIN
projects/ECOC_2025_MPI/precomp_1km_1mm_cable_70ghz_pd_shot2.mat
Normal file
BIN
projects/ECOC_2025_MPI/precomp_1km_1mm_cable_70ghz_pd_shot2.mat
Normal file
Binary file not shown.
163
projects/ECOC_2025_MPI/sqlite_sequence.sql
Normal file
163
projects/ECOC_2025_MPI/sqlite_sequence.sql
Normal file
@@ -0,0 +1,163 @@
|
||||
BEGIN TRANSACTION;
|
||||
CREATE TABLE IF NOT EXISTS "Configurations" (
|
||||
"configuration_id" INTEGER,
|
||||
"run_id" INTEGER,
|
||||
"unique_elab_id" TEXT,
|
||||
"bitrate" REAL,
|
||||
"symbolrate" REAL,
|
||||
"pam_level" INTEGER,
|
||||
"db_mode" TEXT,
|
||||
"pulsef_alpha" INTEGER,
|
||||
"v_bias" REAL,
|
||||
"v_awg" REAL,
|
||||
"precomp_amp" REAL,
|
||||
"rop_attenuation" REAL,
|
||||
"wavelength" REAL,
|
||||
"laser_power" REAL,
|
||||
"fiber_length" REAL,
|
||||
"pd_in_desired" REAL,
|
||||
"is_mpi" BIT,
|
||||
"signal_attenuation" REAL,
|
||||
"interference_path_length" REAL,
|
||||
"interference_attenuation" REAL,
|
||||
"pam_source" TEXT,
|
||||
PRIMARY KEY("configuration_id" AUTOINCREMENT),
|
||||
FOREIGN KEY("run_id") REFERENCES "Runs"("run_id")
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS "EqualizerParameters" (
|
||||
"eq_id" INTEGER,
|
||||
"equalizer_structure" REAL,
|
||||
"M" INTEGER,
|
||||
"target_constellation" TEXT,
|
||||
"db_target" INTEGER,
|
||||
"diff_precode" INTEGER,
|
||||
"postFFE" INTEGER,
|
||||
"NpostFFE" INTEGER,
|
||||
"Ne1" INTEGER,
|
||||
"Ne2" INTEGER,
|
||||
"Ne3" INTEGER,
|
||||
"Nb1" INTEGER,
|
||||
"Nb2" INTEGER,
|
||||
"Nb3" INTEGER,
|
||||
"K" INTEGER,
|
||||
"DCmu" REAL,
|
||||
"ideal_dfe" INTEGER,
|
||||
"training_length" INTEGER,
|
||||
"training_loops" INTEGER,
|
||||
"TRmu1" REAL,
|
||||
"TRmu2" REAL,
|
||||
"TRmu3" REAL,
|
||||
"TRmuDFE" REAL,
|
||||
"dd_loops" INTEGER,
|
||||
"DDmu1" REAL,
|
||||
"DDmu2" REAL,
|
||||
"DDmu3" REAL,
|
||||
"DDmuDFE" REAL,
|
||||
"MLSE_mode" TEXT,
|
||||
"MLSE_trellis_states" TEXT,
|
||||
"comment" TEXT,
|
||||
"config_hash" TEXT,
|
||||
UNIQUE("config_hash"),
|
||||
PRIMARY KEY("eq_id" AUTOINCREMENT)
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS "Measurements" (
|
||||
"measurement_id" INTEGER,
|
||||
"run_id" INTEGER,
|
||||
"power_laser" REAL,
|
||||
"power_rop" REAL,
|
||||
"power_pd_in" REAL,
|
||||
"power_mpi_interference" REAL,
|
||||
"power_mpi_signal" REAL,
|
||||
"voa_class" TEXT,
|
||||
"pdfa_class" TEXT,
|
||||
"laser_class" TEXT,
|
||||
PRIMARY KEY("measurement_id" AUTOINCREMENT),
|
||||
FOREIGN KEY("run_id") REFERENCES "Runs"("run_id")
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS "Results" (
|
||||
"result_id" INTEGER,
|
||||
"run_id" INTEGER,
|
||||
"eqParam_id" INTEGER,
|
||||
"date_of_processing" DATETIME DEFAULT (datetime('now', 'localtime')),
|
||||
"numBits" INTEGER,
|
||||
"numBitErr" INTEGER,
|
||||
"BER" REAL,
|
||||
"numBitErr_precoded" REAL,
|
||||
"BER_precoded" REAL,
|
||||
"SNR" REAL,
|
||||
"SNR_level" TEXT,
|
||||
"GMI" REAL,
|
||||
"AIR" REAL,
|
||||
"EVM" REAL,
|
||||
"EVM_level" TEXT,
|
||||
"Alpha" REAL,
|
||||
"result_hash" TEXT UNIQUE,
|
||||
"MLSE_dir" INTEGER,
|
||||
PRIMARY KEY("result_id" AUTOINCREMENT),
|
||||
FOREIGN KEY("eqParam_id") REFERENCES "EqualizerParameters"("eq_id"),
|
||||
FOREIGN KEY("run_id") REFERENCES "Runs"("run_id")
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS "Runs" (
|
||||
"run_id" INTEGER,
|
||||
"date_of_run" DATETIME DEFAULT (datetime('now', 'localtime')),
|
||||
"tx_bits_path" TEXT,
|
||||
"tx_symbols_path" TEXT,
|
||||
"rx_sync_path" TEXT,
|
||||
"rx_raw_path" TEXT,
|
||||
"filename" TEXT,
|
||||
"tx_signal_path" TEXT,
|
||||
PRIMARY KEY("run_id" AUTOINCREMENT)
|
||||
);
|
||||
CREATE VIEW "View_ResultOverview" AS
|
||||
SELECT
|
||||
-- Run info
|
||||
Runs.run_id,
|
||||
Runs.date_of_run,
|
||||
|
||||
Results.BER,
|
||||
Results.SNR,
|
||||
Results.GMI,
|
||||
Results.AIR,
|
||||
Results.EVM,
|
||||
Results.Alpha,
|
||||
|
||||
-- Configurations
|
||||
Configurations.symbolrate,
|
||||
Configurations.pam_level,
|
||||
Configurations.db_mode,
|
||||
Configurations.pulsef_alpha,
|
||||
Configurations.v_bias,
|
||||
Configurations.v_awg,
|
||||
Configurations.precomp_amp,
|
||||
Configurations.is_mpi,
|
||||
Configurations.signal_attenuation,
|
||||
Configurations.interference_path_length,
|
||||
Configurations.interference_attenuation,
|
||||
|
||||
-- Measurement data
|
||||
Measurements.power_laser,
|
||||
Measurements.power_rop,
|
||||
Measurements.power_pd_in,
|
||||
Measurements.power_mpi_interference,
|
||||
Measurements.power_mpi_signal,
|
||||
|
||||
-- Equalizer parameters
|
||||
EqualizerParameters.equalizer_structure,
|
||||
EqualizerParameters.db_target,
|
||||
EqualizerParameters.diff_precode
|
||||
|
||||
|
||||
FROM Results
|
||||
|
||||
-- Join related tables
|
||||
LEFT JOIN Runs ON Results.run_id = Runs.run_id
|
||||
LEFT JOIN Configurations ON Configurations.run_id = Runs.run_id
|
||||
LEFT JOIN Measurements ON Measurements.run_id = Runs.run_id
|
||||
LEFT JOIN EqualizerParameters ON Results.eqParam_id = EqualizerParameters.eq_id;
|
||||
CREATE INDEX IF NOT EXISTS "idx_run_id_on_Configurations" ON "Configurations" (
|
||||
"run_id"
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS "idx_run_id_on_Measurements" ON "Measurements" (
|
||||
"run_id"
|
||||
);
|
||||
COMMIT;
|
||||
57
projects/ECOC_2025_MPI/submit_dsp.m
Normal file
57
projects/ECOC_2025_MPI/submit_dsp.m
Normal file
@@ -0,0 +1,57 @@
|
||||
function [output, future] = submit_dsp(run_id, basePath, database_name, savePath, options)
|
||||
% runDSPInBackground Runs or submits DSP processing based on 'parallel' flag.
|
||||
%
|
||||
% [output, future] = runDSPInBackground(..., options)
|
||||
% - output: result from dsp_run_id (only immediately available in serial mode)
|
||||
% - future: FevalFuture object if run in parallel, otherwise []
|
||||
|
||||
arguments
|
||||
run_id
|
||||
basePath
|
||||
database_name
|
||||
savePath
|
||||
options.parallel (1,1) logical = true
|
||||
options.max_occurences = 1;
|
||||
options.paramstruct = struct();
|
||||
end
|
||||
|
||||
if options.parallel
|
||||
% Check if a pool exists
|
||||
pool = gcp('nocreate');
|
||||
if isempty(pool)
|
||||
parpool(4);
|
||||
end
|
||||
|
||||
% Submit the DSP function asynchronously
|
||||
future = parfeval( ...
|
||||
@dsp_run_id, 1, ... % One output
|
||||
run_id, ...
|
||||
"database_path", basePath, ...
|
||||
"database_name", database_name, ...
|
||||
'storage_path', savePath, ...
|
||||
'append_to_db', 1, ...
|
||||
'max_occurences', options.max_occurences, ...
|
||||
'parameters', options.paramstruct ...
|
||||
);
|
||||
|
||||
output = [];
|
||||
fprintf('DSP task for run_id %d submitted to the pool.\n', run_id);
|
||||
|
||||
|
||||
else
|
||||
% Run synchronously (debug mode), capture output
|
||||
fprintf('Running DSP task for run_id %d in main thread (debug mode).\n', run_id);
|
||||
|
||||
output = dsp_run_id( ...
|
||||
run_id, ...
|
||||
"database_path", basePath, ...
|
||||
"database_name", database_name, ...
|
||||
'storage_path', savePath, ...
|
||||
'append_to_db', 1, ...
|
||||
'max_occurences', options.max_occurences,...
|
||||
'parameters', options.paramstruct ...
|
||||
);
|
||||
|
||||
future = []; % No future since it's synchronous
|
||||
end
|
||||
end
|
||||
89
projects/ECOC_2025_MPI/theory/analytic_mpi_evaluation.m
Normal file
89
projects/ECOC_2025_MPI/theory/analytic_mpi_evaluation.m
Normal file
@@ -0,0 +1,89 @@
|
||||
% This script is used to evaluate Fig. 1b) in the paper "Adaptive Removal of Multipath Interference in Short Reach 112 GBd PAM-4 IM/DD Systems"
|
||||
|
||||
%% Parameters
|
||||
df = 1e6; % Laser linewidth [Hz]
|
||||
SIR_dB = 20; % Interference attenuation [dB]
|
||||
alpha = 10^(-SIR_dB/20); % Interference attenuation [linear]
|
||||
n_fiber = 1.467; % Refractive index
|
||||
c = physconst('lightspeed'); % [m/s]
|
||||
|
||||
L = linspace(0,250,50); % Interference delay [m]
|
||||
tau = n_fiber./c.*L; % Interference time (= tau) [s]
|
||||
|
||||
tau_c = 1/(pi*df); % laser coherence time [s]
|
||||
L_c = (c/n_fiber)*tau_c; % laser coherence length [m]
|
||||
|
||||
var_sat = 2*alpha^2; % Analytical saturation of variance
|
||||
|
||||
%% Monte–Carlo Simulation
|
||||
fs = 100e9; % sampling rate [Hz]
|
||||
Tsim = 50e-6; % sim duration [s]
|
||||
N = round(Tsim*fs); % number of samples for each realization
|
||||
max_delay_samples = round(max(tau)*fs); % largest delay that is evaluated (based on max. Interference delay)
|
||||
phase_noise_std = sqrt(2*pi*df/fs); % standard dev. phase noise
|
||||
|
||||
num_realizations = 50; % number of parallel runs
|
||||
monte_carlo_variance = zeros(num_realizations, length(L));
|
||||
parfor r = 1:num_realizations
|
||||
|
||||
% generate a realization of phase noise random walk
|
||||
dphi = phase_noise_std * randn(1, N + max_delay_samples); % matlab randn process has std = 1
|
||||
phi = cumsum(dphi);
|
||||
phi_direct = phi(max_delay_samples+1 : max_delay_samples+N);
|
||||
var_k = zeros(1, length(L));
|
||||
for t = 1:length(tau)
|
||||
|
||||
nd = round( tau(t)*fs ); % delay in samples for current interference time
|
||||
phi_delayed = phi(max_delay_samples+1-nd : max_delay_samples+N-nd); %cut out interfering signal part (was earlier)
|
||||
|
||||
E = exp(1j*phi_direct) + alpha*exp(1j*phi_delayed); % E-fields combined
|
||||
I = abs(E).^2; % photo current as magnitude square of E-field
|
||||
var_k(t) = var(I);
|
||||
|
||||
end
|
||||
monte_carlo_variance(r, :) = var_k;
|
||||
end
|
||||
|
||||
avg_of_mc_variances = mean(monte_carlo_variance, 1);
|
||||
std_of_mc_variances = std(monte_carlo_variance, 0, 1);
|
||||
|
||||
%% Analytic variance
|
||||
L_ = linspace(0,250,500); % Interference delay [m]
|
||||
tau_ = n_fiber./c.*L_;
|
||||
analytic_variance = 2*alpha^2 * (1 - exp(-2*pi*df.*tau_)).^2;
|
||||
|
||||
%% Plot
|
||||
cols = [0.3467 0.5360 0.6907
|
||||
0.9153 0.2816 0.2878
|
||||
0.4416 0.7490 0.4322];
|
||||
|
||||
coherence_length_multiples = 0.5:0.5:ceil(L(end)/L_c);
|
||||
|
||||
figure();
|
||||
hold on;
|
||||
[hl, hp] = boundedline(L, avg_of_mc_variances, std_of_mc_variances, 'alpha', 'cmap', cols(1,:));
|
||||
set(hl, 'LineWidth', 2, 'DisplayName', 'Simulation');
|
||||
set(hp, 'HandleVisibility', 'off', 'FaceAlpha', 0.8); % Hide patch from legend to match original behavior
|
||||
|
||||
|
||||
plot(L_, analytic_variance, 'LineWidth',2, 'DisplayName','Analytic','Color',cols(2,:),'LineStyle','-');
|
||||
xticks(coherence_length_multiples.*L_c);
|
||||
xticklabels(round(coherence_length_multiples.*L_c,1));
|
||||
|
||||
norm_to_coherence_len = 1;
|
||||
if norm_to_coherence_len
|
||||
xticklabels(coherence_length_multiples);
|
||||
xlabel('$n \cdot L_c$', 'FontSize',12);
|
||||
else
|
||||
xlabel('Interference Delay [m]', 'FontSize',12);
|
||||
end
|
||||
|
||||
%xline(L_c.*coherence_length_multiples, 'LineWidth',1.5,'HandleVisibility','off','Color',[0.7,0.7,0.7],'LineStyle','-');
|
||||
xlim([0,L(end)]);
|
||||
yline(var_sat, '-.k','LineWidth',1.5, 'DisplayName','Saturation: 2$\alpha ^2$');
|
||||
grid on;
|
||||
ylabel('Intensity Variance', 'FontSize',12);
|
||||
title(sprintf('MPI Variance; %d MHz; SIR: %d dB',df.*1e-6,SIR_dB), 'FontSize',14);
|
||||
legend('Location','southeast');
|
||||
|
||||
% mat2tikz_improved("C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mpi\analytical_mpi_variance2.tikz");
|
||||
33
projects/ECOC_2025_MPI/theory/coherence_length_plot.m
Normal file
33
projects/ECOC_2025_MPI/theory/coherence_length_plot.m
Normal file
@@ -0,0 +1,33 @@
|
||||
%% Parameters
|
||||
df = linspace(1,50e6,10000); % Laser FWHM linewidth [Hz]
|
||||
n_fiber = 1.467; % Fiber group index
|
||||
c = 3e8; % Speed of light [m/s]
|
||||
|
||||
% Compute coherence length (1/e of mean-fringe decay)
|
||||
tau_c = 1./(pi*df);
|
||||
L_c = (c.* tau_c/n_fiber) ; % Coherence length [m]
|
||||
|
||||
%% Plot
|
||||
figure('Color','w');
|
||||
loglog(df/1e6, L_c, 'LineWidth',2,'LineStyle','-'); % linewidth in MHz
|
||||
% xticks([0.1, 1, 10, 50]);
|
||||
% yticks([1, 10, 100, 1000]);
|
||||
% yticklabels({'1','10','100','1000'})
|
||||
grid on; box on;
|
||||
xlabel('Laser linewidth [MHz]','FontSize',12,'Interpreter','latex');
|
||||
ylabel('Coherence length [m]','FontSize',12,'Interpreter','latex');
|
||||
title('Coherence Length vs. Laser Linewidth','FontSize',14,'Interpreter','latex');
|
||||
|
||||
mat2tikz_improved("C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mpi\laser_linewidth_vs_coherence.tikz");
|
||||
%% Annotate some key points
|
||||
% hold on;
|
||||
% freqs = [150e3, 1e6, 10e6, 50e6]; % [Hz]
|
||||
% for f = freqs
|
||||
% x = f/1e6;
|
||||
% y = (c/n_fiber) * (1/(pi*f));
|
||||
% scatter(x,y,'Marker','x','LineWidth',1,'MarkerEdgeColor','black');
|
||||
%
|
||||
% text(x*1.1,y, sprintf('%.2f MHz', f/1e6), ...
|
||||
% 'FontSize',10,'HorizontalAlignment','left');
|
||||
%
|
||||
% end
|
||||
Reference in New Issue
Block a user