BCJR implementation
WDM code added (Pol Cont., Opt MUX/DEMUX, Opt Atten, DP_Fiber) -> the codebase is not optimized to always work with dp signals!
This commit is contained in:
Silas Oettinghaus
@@ -4,30 +4,39 @@ dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sio
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db = DBHandler("dataBase", [dataBase], "type", database_type);
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fp = QueryFilter();
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% fp.where('Runs', 'run_id','EQUALS', 987);
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% fp.where('Runs', 'run_id','EQUALS', 987);
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M = 8;
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fp.where('Runs', 'pam_level','EQUALS', M);
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% fp.where('Runs', 'bitrate','LESS_THAN', 310e9);
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fp.where('Runs', 'fiber_length','EQUALS', 2);
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fp.where('Runs', 'is_mpi','EQUALS', 0);
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% fp.where('Runs', 'interference_path_length','EQUALS', 1000);
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% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
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% fp.where('Runs', 'sir','EQUALS',18);
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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', M);
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% fp.where('Runs', 'bitrate','LESS_THAN', 310e9);
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fp.where('Runs', 'fiber_length','EQUALS', 2);
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fp.where('Runs', 'is_mpi','EQUALS', 0);
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% fp.where('Runs', 'interference_path_length','EQUALS', 1000);
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% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
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% fp.where('Runs', 'sir','EQUALS',18);
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fp.where('Runs', 'wavelength','EQUALS', 1293);
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% fp.where('Runs', 'db_mode','EQUALS', 0);
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fp.where('Runs', 'rop_attenuation','EQUALS', 0);
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% [dataTable,~] = db.queryDB(fp, db.getTableFieldNames('dashboard'));
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fields = db.getTableFieldNames('power_state_info');
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fields = [fields; db.getTableFieldNames('dashboard_ungrouped_new')];
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[dataTable,~] = db.queryDB(fp, fields);
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eqstructures = unique(dataTable.equalizer_structure);
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% Create the figure
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figure(10);
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showFiltered = true;
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showPrecoded = false;
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show_bitrate = true;
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figure(5);
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hold on
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for pre_emph = [1]
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dbmode_filtered = dataTable(dataTable.db_mode == ~pre_emph,:);
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for eqs = [equalizer_structure.vnle, equalizer_structure.vnle_pf_mlse , equalizer_structure.vnle_db_mlse]
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for eqs = [equalizer_structure.vnle, equalizer_structure.vnle_pf_mlse]
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% figure('Name',string([char(eqs),'']));
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% hold on
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for pre_emph = [0,1]
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dbmode_filtered = dataTable(dataTable.db_mode == ~pre_emph,:);
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eq_choice = equalizer_structure(eqs);
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@@ -37,50 +46,238 @@ for pre_emph = [1]
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end
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eq_filtered = dbmode_filtered(dbmode_filtered.equalizer_structure == eq_choice,:);
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symbolrate_sorted = sortrows(eq_filtered,{'symbolrate','min_BER_precoded'}, 'ascend');
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[~, ia] = unique(symbolrate_sorted.symbolrate, 'first');
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% ===== NEW: compute averages + per-row keep masks (robust filtering) =====
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[Tav, keepMask, keepMaskP] = avgBerBySymbolrate(eq_filtered); % <= NEW
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% x-values (bitrate) for raw points (same mapping as your lines)
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M = unique(eq_filtered.pam_level); % (assumes single PAM per curve)
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if show_bitrate
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x_raw = eq_filtered.symbolrate.*1e-9 .* floor(log2(M)*10)/10;
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else
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x_raw = eq_filtered.symbolrate.*1e-9;
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end
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% ===== NEW: scatter kept raw BER points (hidden from legend) =====
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cols = cbrewer2('Paired',12);
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thisColor = cols((2*eqs)+1+pre_emph,:);
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scatter(x_raw(keepMask), ... % kept points
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eq_filtered.BER(keepMask), ...
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14, thisColor, 'filled', ...
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'MarkerFaceAlpha', 0.35, ...
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'MarkerEdgeAlpha', 0.35, ...
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'HandleVisibility','off');
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if showPrecoded
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scatter(x_raw(keepMaskP), ... % kept precoded points
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eq_filtered.BER_precoded(keepMaskP), ...
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14, thisColor, 'filled', ...
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'Marker', 'square', ...
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'MarkerFaceAlpha', 0.35, ...
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'MarkerEdgeAlpha', 0.35, ...
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'HandleVisibility','off');
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end
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% ===== NEW: optionally show filtered-out points in red =====
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if showFiltered
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bad = ~keepMask;
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if any(bad)
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scatter(x_raw(bad), eq_filtered.BER(bad), ...
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18, 'r', 'x', 'LineWidth', 1.2, ...
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'HandleVisibility','off');
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end
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badp = ~keepMaskP;
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if any(badp)
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scatter(x_raw(badp), eq_filtered.BER_precoded(badp), ...
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18, 'r', '+', 'LineWidth', 1.2, ...
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'HandleVisibility','off');
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end
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end
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% Keep your sorting and one-per-symbolrate behavior (using Tav)
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symbolrate_sorted = sortrows(Tav,{'symbolrate','avg_BER_calc'}, 'ascend');
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[~, ia] = unique(symbolrate_sorted.symbolrate, 'first');
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symbolrate_sorted = symbolrate_sorted(ia, :);
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% Example data (replace these with your real vectors)
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symbolrate = symbolrate_sorted.symbolrate.*1e-9; % in baud
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bitrate = symbolrate * floor(log2(M)*10)/10;
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ber = symbolrate_sorted.min_BER; % BER
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ber_precoded = symbolrate_sorted.min_BER_precoded; % BER
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cols = cbrewer2('Paired',12);
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cols = [0.4660 0.6740 0.1880 ; 0.9290 0.6940 0.1250 ; 0 0.4470 0.7410; 0.4940 0.1840 0.5560]; %VNLE; PF ; DFE ; DB tgt
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dname = [char(eq_choice)];
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if pre_emph
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if show_bitrate
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% Bitrate for the averaged curves (unchanged)
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xraw = symbolrate_sorted.symbolrate.*1e-9 .* floor(log2(M)*10)/10;
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else
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xraw = symbolrate_sorted.symbolrate.*1e-9;
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end
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% Use the MATLAB-averaged BERs
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ber = symbolrate_sorted.avg_BER_calc;
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ber_precoded = symbolrate_sorted.avg_BER_precoded_calc;
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dname = strrep([char(eq_choice)],'_',' ');
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if pre_emph
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dname = [dname,' with pre-emph.'];
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else
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dname = [dname,' w/o pre-emph.'];
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end
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plot(bitrate, ber, 'LineWidth', 1.5, 'MarkerSize', 5,'Marker','o','LineStyle','-','Color',cols((2*eqs)+1+pre_emph,:),'MarkerEdgeColor',cols((2*eqs)+1+pre_emph,:),'MarkerFaceColor',[1,1,1],'DisplayName',[dname]);
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plot(bitrate, ber_precoded, 'LineWidth', 1.5, 'MarkerSize', 5,'Marker','square','LineStyle',':','Color',cols((2*eqs)+1+pre_emph,:),'MarkerEdgeColor',cols((2*eqs)+1+pre_emph,:),'MarkerFaceColor',[1,1,1],'DisplayName',[dname,'; pre-coded']);
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grid on;
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% Axis labels and title
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xlabel('Baud Rate GBaud', 'FontSize', 12);
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ylabel('BER', 'FontSize', 12);
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title('BER vs. Baud Rate', 'FontSize', 14, 'FontWeight', 'bold');
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% Improve tick formatting
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set(gca, 'XScale', 'linear', ...
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'YScale', 'log', ...
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'TickLabelInterpreter', 'latex', ...
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'FontSize', 11);
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legend
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plot(xraw, ber, ...
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'LineWidth', 1.5, 'MarkerSize', 5, ...
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'Marker','o','LineStyle','-', ...
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'Color',thisColor,'MarkerEdgeColor',thisColor,'MarkerFaceColor',[1,1,1], ...
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'DisplayName', dname);
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xticks(bitrate);
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if showPrecoded
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plot(xraw, ber_precoded, ...
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'LineWidth', 1.5, 'MarkerSize', 5, ...
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'Marker','square','LineStyle',':', ...
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'Color',thisColor,'MarkerEdgeColor',thisColor,'MarkerFaceColor',[1,1,1], ...
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'DisplayName', [dname,'; pre-coded']);
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end
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grid on;
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if show_bitrate
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xlabel('Net bitrate [GBps]', 'FontSize', 12);
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else
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xlabel('Symbol rate [GBd]', 'FontSize', 12);
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end
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ylabel('BER', 'FontSize', 12);
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title('BER vs. Baud Rate','FontSize', 14, 'FontWeight', 'bold');
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set(gca, 'XScale', 'linear', ...
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'YScale', 'log', ...
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'TickLabelInterpreter', 'latex', ...
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'FontSize', 11);
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% Optional: tighten axis limits
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xlim([min(bitrate), max(bitrate)]);
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ylim([5e-5, 0.5]);
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xticks(xraw);
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if show_bitrate
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xticks(200:25:500);
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xlim([350 500]);
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else
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xlim([min(xraw), max(xraw)]);
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end
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ylim([5e-4, 0.3]);
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end
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yline([2.2e-4, 4.85e-3, 2e-2],'LineWidth',1,'LineStyle','--','HandleVisibility','off');
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end
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function [Tav, keepAll, keepAllP] = avgBerBySymbolrate(T, ZT, MIN_G)
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% Minimal robust averaging of BER per symbolrate (+ masks for kept points).
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% Usage: [Tav, keepAll, keepAllP] = avgBerBySymbolrate(T, ZT, MIN_G)
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% Defaults: ZT=3 (MAD z-thresh in log10), MIN_G=2 (min points to filter)
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if nargin < 2, ZT = 5; end
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if nargin < 5, MIN_G = 0; end
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hasP = ismember('BER_precoded', T.Properties.VariableNames);
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hasNB = ismember('numBits', T.Properties.VariableNames);
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[G,~,idx] = unique(T.symbolrate);
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nG = numel(G);
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avgBER = nan(nG,1);
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avgBERp = nan(nG,1);
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keepAll = false(height(T),1);
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keepAllP = false(height(T),1);
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for gi = 1:nG
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r = idx==gi;
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x = T.BER(r);
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nb = hasNB * T.numBits(r) + ~hasNB; % if missing, nb==1 (scalar expansion ok)
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[avgBER(gi), keepAll(r)] = rmeanBer(x, nb, ZT, MIN_G);
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if hasP
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xp = T.BER_precoded(r);
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[avgBERp(gi), keepAllP(r)] = rmeanBer(xp, nb, ZT, MIN_G);
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end
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end
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Tav = table(G, avgBER, avgBERp, ...
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'VariableNames', {'symbolrate','avg_BER_calc','avg_BER_precoded_calc'});
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end
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function [mu, keep] = rmeanBer(x, nb, ZT, MIN_G, onlyHighOutliers, minKeepThreshold)
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% Robust arithmetic mean of BER with log-domain MAD filtering (returns keep mask)
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%
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% Params:
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% x : BER values
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% nb : numBits (for floor)
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% ZT : MAD z-threshold
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% MIN_G : min group size before filtering
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% onlyHighOutliers : (bool) if true, only discard values above mean
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% minKeepThreshold : values below this BER are always kept
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%
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% Returns:
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% mu : robust mean
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% keep : logical mask of kept samples
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if nargin < 5, onlyHighOutliers = false; end
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if nargin < 6, minKeepThreshold = 0; end
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x(~isfinite(x)) = NaN;
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if ~isscalar(nb), nb(~isfinite(nb)) = NaN; end
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if isscalar(nb) && ~isfinite(nb), nb = 1; end
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floorVal = realmin;
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if ~isscalar(nb) || (isscalar(nb) && isfinite(nb) && nb~=1)
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fv = 0.5 ./ max(nb, eps); % rule-of-three style floor
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if isscalar(fv), floorVal = fv; else, floorVal = fv; end
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end
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xAdj = x;
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bad = ~isfinite(xAdj) | xAdj <= 0;
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if isscalar(floorVal)
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xAdj(bad) = floorVal;
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else
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xAdj(bad) = floorVal(bad);
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end
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valid = isfinite(xAdj) & xAdj > 0;
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keep = false(size(xAdj));
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if nnz(valid)==0
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mu = NaN; return
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end
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if nnz(valid) < MIN_G
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mu = mean(xAdj(valid),'omitnan'); keep(valid)=true; return
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end
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lx = log10(xAdj(valid));
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med = median(lx,'omitnan');
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mad = median(abs(lx-med),'omitnan');
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if mad<=0 || ~isfinite(mad)
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keep(valid) = true;
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mu = mean(xAdj(valid),'omitnan');
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return
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end
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sigma = 1.4826*mad;
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ksel = abs(lx-med) <= ZT*sigma;
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% convert to linear indices
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vIdx = find(valid);
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% === Extension A: only drop high outliers ===
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if onlyHighOutliers
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logMean = mean(lx,'omitnan');
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highIdx = lx > logMean;
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ksel = ksel | ~highIdx; % always keep values below/equal to mean
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end
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% === Extension B: always keep values below minKeepThreshold ===
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belowThr = xAdj(valid) < minKeepThreshold;
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ksel = ksel | belowThr;
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keep(vIdx(ksel)) = true;
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if any(keep)
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mu = mean(xAdj(keep),'omitnan');
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else
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mu = mean(xAdj(valid),'omitnan');
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keep(valid) = true;
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end
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end
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yline([4.85e-3, 2e-2],'LineWidth',1,'LineStyle','--','HandleVisibility','off'); beautifyBERplot();
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