halfway merged and pulled?!
This commit is contained in:
Silas Labor Zizou
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database_type = 'mysql';
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dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
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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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M = 6;
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fp.where('Runs', 'pam_level','EQUALS', M);
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baudrate = 162e9;
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fp.where('Runs', 'symbolrate','EQUALS', baudrate);
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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); % 0 == high preemphasis // 1 == low preemphasis
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fp.where('Runs', 'rop_attenuation','EQUALS', 0);
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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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fiber_len = unique(dataTable.fiber_length);
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cnt = 1;
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f=figure();
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clf
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hold on
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markers = {'o', 's', 'd', '^', 'v', '>', '<', 'p', 'h'}; % Define marker styles
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for fl = 1:numel(fiber_len)
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fl_filtered = dataTable(dataTable.fiber_length == fiber_len(fl),:);
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for eqs = [equalizer_structure.vnle_pf_mlse]
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eq_choice = equalizer_structure(eqs);
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if sum(eqstructures == eq_choice)~=1
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disp(eq_choice)
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continue
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end
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eq_filtered = fl_filtered(fl_filtered.equalizer_structure == eq_choice,:);
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dispersion_sorted = sortrows(eq_filtered, {'accumulated_dispersion'}, 'ascend');
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% dispersion_sorted = dispersion_sorted(dispersion_sorted.wavelength <= 1320,:);
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% dispersion_sorted = dispersion_sorted(dispersion_sorted.BER < 0.02,:);
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% pull out your vectors
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accumulated_dispersion = dispersion_sorted.accumulated_dispersion;
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ber = dispersion_sorted.BER;
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% ber = dispersion_sorted.BER_precoded;
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run_ids = dispersion_sorted.run_id; % <-- this is what we want in the datatip
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len = dispersion_sorted.fiber_length;
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lambda = dispersion_sorted.wavelength;
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cols = cbrewer2('Set1',8);
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% cols = flip(cbrewer2('RdYlGn',14));
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cols = linspecer(8);
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ber_wavelen_grouped = groupsummary( ...
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dispersion_sorted, ... % input table
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"wavelength", ... % grouping variable
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"min", ... % which summary statistic
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"BER_precoded");
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dname = sprintf('%s; %d km',eq_choice, fiber_len(fl));
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h1 = plot(ber_wavelen_grouped.wavelength, ber_wavelen_grouped.min_BER_precoded,'LineWidth', 2, 'MarkerSize', 5,'Marker',markers(cnt),'LineStyle','-','Color',cols(cnt,:),'MarkerEdgeColor','auto','MarkerFaceColor','white','DisplayName',dname);
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plotallscatters=0;
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if plotallscatters
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% plot the two curves and capture their Line handles
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dname = sprintf('%s; %d km',eq_choice, fiber_len(fl));
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h1 = plot(lambda, ber,'LineWidth', 1.5, 'MarkerSize', 5,'Marker','o','LineStyle','none','Color',cols(cnt,:),'MarkerFaceColor',cols(cnt,:),'DisplayName',dname);
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% —————— Add run_id as a datatip row ——————
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% For each line, tell the datatip template where to find the run_id:
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h1.DataTipTemplate.DataTipRows(end+1) = ...
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dataTipTextRow('run\_id', run_ids);
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h1.DataTipTemplate.DataTipRows(end+1) = ...
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dataTipTextRow('len', len);
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h1.DataTipTemplate.DataTipRows(end+1) = ...
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dataTipTextRow('lambda', lambda);
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end
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xticks(sort(unique(lambda)));
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xticklabels(sort(unique(lambda)));
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grid on;
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% Labels, scales, legend, etc.
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xlabel('Wavelength in nm','FontSize',12);
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ylabel('BER','FontSize',12);
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tit = sprintf('%d GBd PAM-%d',baudrate.*1e-9, M);
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title(tit,'FontSize',14,'FontWeight','bold');
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set(gca, 'XScale','linear','YScale','log','FontSize',11);
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legend
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xlim([min(lambda)-2, max(lambda)+2]);
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ylim([1e-4, 0.2]);
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cnt = cnt+1;
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end
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end
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yline([4.85e-3, 2e-2],'--','LineWidth',1,'HandleVisibility','off');
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posH = get(f, 'Position'); % [left, bottom, width, height]
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newPos = [posH(1), posH(2), 750, 300];
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set(f, 'Position', newPos);
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284
projects/HighSpeedExperiment_2024/Auswertung_JLT/ber_vs_rate.m
Normal file
284
projects/HighSpeedExperiment_2024/Auswertung_JLT/ber_vs_rate.m
Normal file
@@ -0,0 +1,284 @@
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database_type = 'mysql';
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dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
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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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M = 6;
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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', 0);
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fp.where('Runs', 'rop_attenuation','EQUALS', 0);
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fields = db.getTableFieldNames('power_state_info');
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fields = [fields; db.getTableFieldNames('dashboard_ungrouped_aug_nov_2025')];
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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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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 eqs = [equalizer_structure.vnle]
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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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if sum(eqstructures == eq_choice)~=1
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disp(eq_choice)
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continue
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end
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eq_filtered = dbmode_filtered(dbmode_filtered.equalizer_structure == eq_choice,:);
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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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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(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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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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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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xlim([min(xraw), max(xraw)]);
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else
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xlim([min(xraw), max(xraw)]);
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end
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ylim([1e-4, 0.5]);
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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
|
||||
end
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@@ -0,0 +1,117 @@
|
||||
|
||||
database_type = 'mysql';
|
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dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
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db = DBHandler("dataBase", [dataBase], "type", database_type);
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|
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fp = QueryFilter();
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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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|
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[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('dashboard_ungrouped_after_nov_2025'));
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|
||||
eqstructures = unique(dataTable.equalizer_structure);
|
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|
||||
% Create the figure
|
||||
f=figure(4);
|
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hold on
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||||
|
||||
eqs = [equalizer_structure.vnle, equalizer_structure.vnle_pf_mlse , equalizer_structure.vnle_db_mlse];
|
||||
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
|
||||
|
||||
eq_choice = equalizer_structure.vnle;
|
||||
pre_emph = 1;
|
||||
dbmode_filtered = dataTable(dataTable.db_mode == ~pre_emph,:);
|
||||
eq_filtered = dbmode_filtered(dbmode_filtered.equalizer_structure == eq_choice,:);
|
||||
symbolrate_sorted = sortrows(eq_filtered,{'symbolrate','min_BER_precoded'}, 'ascend');
|
||||
[~, ia] = unique(symbolrate_sorted.symbolrate, 'first');
|
||||
symbolrate_sorted = symbolrate_sorted(ia, :);
|
||||
symbolrate = symbolrate_sorted.symbolrate.*1e-9; % in baud
|
||||
bitrate = symbolrate * floor(log2(M)*10)/10;
|
||||
ber = symbolrate_sorted.min_BER; % BER
|
||||
ber_precoded = symbolrate_sorted.min_BER_precoded; % BER
|
||||
plot(bitrate, ber, 'LineWidth', 1.5, 'MarkerSize', 5,'Marker','v','LineStyle',':','Color',cols(1,:),'MarkerEdgeColor',cols(1,:),'MarkerFaceColor',cols(1,:),'DisplayName',['Tx pre-emphasis + VNLE']);
|
||||
|
||||
eq_choice = equalizer_structure.vnle_pf_mlse;
|
||||
pre_emph = 0;
|
||||
dbmode_filtered = dataTable(dataTable.db_mode == ~pre_emph,:);
|
||||
eq_filtered = dbmode_filtered(dbmode_filtered.equalizer_structure == eq_choice,:);
|
||||
symbolrate_sorted = sortrows(eq_filtered,{'symbolrate','min_BER_precoded'}, 'ascend');
|
||||
[~, ia] = unique(symbolrate_sorted.symbolrate, 'first');
|
||||
symbolrate_sorted = symbolrate_sorted(ia, :);
|
||||
symbolrate = symbolrate_sorted.symbolrate.*1e-9; % in baud
|
||||
bitrate = symbolrate * floor(log2(M)*10)/10;
|
||||
ber = symbolrate_sorted.min_BER; % BER
|
||||
ber_precoded = symbolrate_sorted.min_BER_precoded; % BER
|
||||
plot(bitrate, ber, 'LineWidth', 1.5, 'MarkerSize', 5,'Marker','diamond','LineStyle',':','Color',cols(2,:),'MarkerEdgeColor',cols(2,:),'MarkerFaceColor',cols(2,:),'DisplayName',['VNLE+2-tap post-filter+MLSE']);
|
||||
|
||||
% eq_choice = equalizer_structure.dfe;
|
||||
% pre_emph = 1;
|
||||
% dbmode_filtered = dataTable(dataTable.db_mode == ~pre_emph,:);
|
||||
% eq_filtered = dbmode_filtered(dbmode_filtered.equalizer_structure == eq_choice,:);
|
||||
% symbolrate_sorted = sortrows(eq_filtered,{'symbolrate','min_BER_precoded'}, 'ascend');
|
||||
% [~, ia] = unique(symbolrate_sorted.symbolrate, 'first');
|
||||
% symbolrate_sorted = symbolrate_sorted(ia, :);
|
||||
% symbolrate = symbolrate_sorted.symbolrate.*1e-9; % in baud
|
||||
% bitrate = symbolrate * floor(log2(M)*10)/10;
|
||||
% ber = symbolrate_sorted.min_BER; % BER
|
||||
% ber_precoded = symbolrate_sorted.min_BER_precoded; % BER
|
||||
% plot(bitrate, ber, 'LineWidth', 1.5, 'MarkerSize', 5,'Marker','o','LineStyle','-','Color',cols(3,:),'MarkerEdgeColor',cols(3,:),'MarkerFaceColor',cols(3,:),'DisplayName',[char(eq_choice)]);
|
||||
|
||||
eq_choice = equalizer_structure.vnle_db_mlse;
|
||||
pre_emph = 0;
|
||||
dbmode_filtered = dataTable(dataTable.db_mode == ~pre_emph,:);
|
||||
eq_filtered = dbmode_filtered(dbmode_filtered.equalizer_structure == eq_choice,:);
|
||||
symbolrate_sorted = sortrows(eq_filtered,{'symbolrate','min_BER_precoded'}, 'ascend');
|
||||
[~, ia] = unique(symbolrate_sorted.symbolrate, 'first');
|
||||
symbolrate_sorted = symbolrate_sorted(ia, :);
|
||||
symbolrate = symbolrate_sorted.symbolrate.*1e-9; % in baud
|
||||
bitrate = symbolrate * floor(log2(M)*10)/10;
|
||||
ber = symbolrate_sorted.min_BER; % BER
|
||||
ber_precoded = symbolrate_sorted.min_BER_precoded; % BER
|
||||
plot(bitrate, ber_precoded, 'LineWidth', 1.5, 'MarkerSize', 5,'Marker','square','LineStyle',':','Color',cols(4,:),'MarkerEdgeColor',cols(4,:),'MarkerFaceColor',cols(4,:),'DisplayName',['DB precoding + DB tgt. + MLSE']);
|
||||
|
||||
|
||||
% Axis labels and title with Arial font
|
||||
xlabel('Gross bitrate [Gb/s]', 'FontSize', 12, 'FontName', 'Arial', 'Interpreter', 'none');
|
||||
ylabel('BER', 'FontSize', 12, 'FontName', 'Arial', 'Interpreter', 'none');
|
||||
title('', 'FontSize', 14, 'FontWeight', 'bold', 'FontName', 'Arial', 'Interpreter', 'none');
|
||||
|
||||
% Improve tick formatting
|
||||
set(gca, 'XScale', 'linear', ...
|
||||
'YScale', 'log', ...
|
||||
'TickLabelInterpreter', 'none', ...
|
||||
'FontSize', 11, ...
|
||||
'FontName', 'Arial');
|
||||
|
||||
% Legend with Arial font
|
||||
% legend('FontName', 'Arial', 'Interpreter', 'none','Location','best');
|
||||
|
||||
xticks(bitrate);
|
||||
|
||||
% Optional: tighten axis limits
|
||||
xlim([min(bitrate), max(bitrate)]);
|
||||
ylim([5e-4, 0.05]);
|
||||
|
||||
yline([3.8e-3], 'LineWidth', 2, 'LineStyle', '--', ...
|
||||
'HandleVisibility', 'off', 'LabelHorizontalAlignment', 'left');
|
||||
|
||||
posH = get(f, 'Position'); % [left, bottom, width, height]
|
||||
newPos = [posH(1), posH(2), 350, 200];
|
||||
set(f, 'Position', newPos);
|
||||
|
||||
annotation(f,'textbox',...
|
||||
[0.398095238095238 0.273381294964029 0.491428571428572 0.140287769784173],...
|
||||
'String',{'PAM-8; 2 km; 1293 nm'},...
|
||||
'LineWidth',0.5,...
|
||||
'FitBoxToText','off',...
|
||||
'BackgroundColor',[1 1 1]);
|
||||
@@ -0,0 +1,146 @@
|
||||
%% ============================================================
|
||||
% SETTINGS
|
||||
% ============================================================
|
||||
database_type = 'mysql';
|
||||
db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
|
||||
|
||||
fiberL = 1; % km
|
||||
wlen = 1310; % nm
|
||||
bit = 300e9; % example (adjust if needed)
|
||||
max_pd = 7; % ROP limit (same as before)
|
||||
|
||||
PAM_list = [4 6 8]; % formats to compare
|
||||
|
||||
% Colors for PAM formats
|
||||
colors = {clr.Paired.red, clr.Paired.green, clr.Paired.blue};
|
||||
|
||||
% Best DSP selection:
|
||||
bestDSP = struct;
|
||||
bestDSP = struct;
|
||||
bestDSP.P4 = equalizer_structure.vnle_db_mlse; % PAM-4
|
||||
bestDSP.P6 = equalizer_structure.vnle; % PAM-6
|
||||
bestDSP.P8 = equalizer_structure.vnle; % PAM-8
|
||||
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% LOOP over PAM formats — extract data
|
||||
% ============================================================
|
||||
results = struct;
|
||||
|
||||
for pi = 1:numel(PAM_list)
|
||||
M = PAM_list(pi);
|
||||
eq = bestDSP.(sprintf('P%d', M));
|
||||
|
||||
% ---- DB FILTER ----
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','pam_level','EQUALS',M);
|
||||
fp.where('Runs','fiber_length','EQUALS',fiberL);
|
||||
fp.where('Runs','wavelength','EQUALS',wlen);
|
||||
fp.where('Runs','bitrate','EQUALS',bit);
|
||||
fp.where('Runs','power_pd_in','LESS_THAN',max_pd);
|
||||
|
||||
fields = [
|
||||
db.getTableFieldNames('power_state_info');
|
||||
db.getTableFieldNames('dashboard_ungrouped_alltime')
|
||||
];
|
||||
[T,~] = db.queryDB(fp, fields);
|
||||
|
||||
% ---- DSP OPTIONS ----
|
||||
pre_emph = decide_preemph(M, eq);
|
||||
precoded = decide_precoded(M, eq);
|
||||
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'power_mzm';
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.agg = 'min';
|
||||
cfg.outlier = 'none';
|
||||
cfg.show_raw = false;
|
||||
|
||||
cfg.filters = struct( ...
|
||||
'pam_level', M, ...
|
||||
'fiber_length', fiberL, ...
|
||||
'wavelength', wlen, ...
|
||||
'bitrate', bit, ...
|
||||
'is_mpi', 0, ...
|
||||
'equalizer_structure', eq, ...
|
||||
'pre_emph', pre_emph);
|
||||
|
||||
A = analyze_measurements_gpt(T, cfg);
|
||||
|
||||
results(pi).M = M;
|
||||
results(pi).x = A.group{1}.x;
|
||||
results(pi).color = colors{pi};
|
||||
|
||||
if precoded
|
||||
results(pi).ber = A.group{1}.y_precoded;
|
||||
else
|
||||
results(pi).ber = A.group{1}.y;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% PLOT — all PAM formats in one ROP plot
|
||||
% ============================================================
|
||||
fig = figure(91); hold on;
|
||||
|
||||
lw = 2.2; ms = 7;
|
||||
|
||||
for pi = 1:numel(results)
|
||||
plot(results(pi).x, results(pi).ber, ...
|
||||
'-o', ...
|
||||
'LineWidth', lw, ...
|
||||
'MarkerSize', ms, ...
|
||||
'MarkerFaceColor', results(pi).color, ...
|
||||
'Color', results(pi).color, ...
|
||||
'DisplayName', sprintf('PAM-%d', results(pi).M));
|
||||
end
|
||||
|
||||
set(gca,'YScale','log');
|
||||
grid minor;
|
||||
|
||||
xlabel('ROP / Power (MZM) [dBm]');
|
||||
ylabel('BER');
|
||||
|
||||
ylim([1e-4 2e-1]);
|
||||
|
||||
legend('Location','best');
|
||||
title(sprintf('BER vs ROP — Best DSP (4,6,8) at %.0f GBd, λ=%d nm, %.0f km', ...
|
||||
bit*1e-9, wlen, fiberL));
|
||||
|
||||
beautifyBERplot();
|
||||
|
||||
set(fig,'Position',1e3*[0.35 0.45 1.0 0.45]);
|
||||
|
||||
%% ============================================================
|
||||
% DECISION LOGIC (INLINE FUNCTIONS)
|
||||
% ============================================================
|
||||
|
||||
function pe = decide_preemph(M, eq)
|
||||
% PRE-EMPH RULES:
|
||||
switch M
|
||||
case 4
|
||||
if eq == equalizer_structure.vnle
|
||||
pe = 1; % PAM4: VNLE → pre-emph on
|
||||
else
|
||||
pe = 0; % PAM4: all others → off
|
||||
end
|
||||
case {6,8}
|
||||
pe = 1; % PAM6/8: all → pre-emph on
|
||||
otherwise
|
||||
pe = 0;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function flag = decide_precoded(M, eq)
|
||||
% PRE-CODE RULES:
|
||||
if eq == equalizer_structure.vnle_db_mlse
|
||||
flag = 1; % Always for DB-target
|
||||
elseif eq == equalizer_structure.ml_mlse && M == 4
|
||||
flag = 1; % PAM4: ML-based → precoded
|
||||
else
|
||||
flag = 0;
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,272 @@
|
||||
%% ============================================================
|
||||
% LOAD DATA FOR PAM = 4,6,8
|
||||
% ============================================================
|
||||
database_type = 'mysql';
|
||||
db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
|
||||
|
||||
pam_levels = [4, 6, 8]; % three tiles
|
||||
bitrate_set = 360e9;
|
||||
fiberL = 10;
|
||||
|
||||
fields = [
|
||||
db.getTableFieldNames('power_state_info');
|
||||
db.getTableFieldNames('dashboard_ungrouped_alltime')
|
||||
];
|
||||
|
||||
%% ============================================================
|
||||
% DEFINE DSP SCHEMES
|
||||
% ============================================================
|
||||
curves = struct;
|
||||
|
||||
curves(1).name = 'VNLE';
|
||||
curves(1).eq = equalizer_structure.vnle;
|
||||
curves(1).color = clr.Paired.red;
|
||||
|
||||
curves(2).name = 'PF + MLSE';
|
||||
curves(2).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(2).color = clr.Paired.green;
|
||||
|
||||
curves(3).name = 'DB-target + MLSE';
|
||||
curves(3).eq = equalizer_structure.vnle_db_mlse;
|
||||
curves(3).color = clr.Paired.blue;
|
||||
|
||||
curves(4).name = 'ML-based MLSE';
|
||||
curves(4).eq = equalizer_structure.ml_mlse;
|
||||
curves(4).color = clr.Paired.purple;
|
||||
|
||||
%% ============================================================
|
||||
% ANALYSIS — NO PLOTTING
|
||||
% results(p, k) → p: PAM index, k: DSP index
|
||||
% ============================================================
|
||||
results = struct;
|
||||
|
||||
for p = 1:length(pam_levels)
|
||||
M = pam_levels(p);
|
||||
|
||||
% --- Load DB rows for this PAM ---
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','pam_level','EQUALS', M);
|
||||
fp.where('Runs','fiber_length','EQUALS', fiberL);
|
||||
fp.where('Runs','bitrate','EQUALS', bitrate_set);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
|
||||
[dataTable, ~] = db.queryDB(fp, fields);
|
||||
|
||||
for k = 1:numel(curves)
|
||||
|
||||
%% =====================================================
|
||||
% DECIDE PRE-EMPHASIS AND PRECoded BER
|
||||
% ======================================================
|
||||
pre_emph = decide_preemph(M, curves(k).eq);
|
||||
use_precoded = decide_precoded(M, curves(k).eq);
|
||||
|
||||
%% ---- base config ----
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'wavelength';
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.agg = 'min';
|
||||
cfg.outlier = 'none';
|
||||
% cfg.group_by = {'wavelength'};
|
||||
cfg.show_raw = false;
|
||||
|
||||
cfg.filters = struct( ...
|
||||
'pam_level', M, ...
|
||||
'is_mpi', 0, ...
|
||||
'bitrate', bitrate_set, ...
|
||||
'fiber_length', fiberL, ...
|
||||
'equalizer_structure', curves(k).eq, ...
|
||||
'pre_emph', pre_emph);
|
||||
|
||||
%% ---- Run analysis ----
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
results(p,k).wavelength = A.group{1}.x;
|
||||
|
||||
%% ---- store BER variant ----
|
||||
if use_precoded
|
||||
results(p,k).ber = A.group{1}.y_precoded;
|
||||
else
|
||||
results(p,k).ber = A.group{1}.y;
|
||||
end
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% PLOT — 1×3 (PAM-4, PAM-6, PAM-8)
|
||||
% ============================================================
|
||||
fig = figure(9110); clf;
|
||||
tiledlayout(1,3,'TileSpacing','compact','Padding','compact');
|
||||
|
||||
lw = 1.8;
|
||||
ms = 6;
|
||||
|
||||
for p = 1:length(pam_levels)
|
||||
nexttile; hold on;
|
||||
|
||||
for k = 1:numel(curves)
|
||||
plot(results(p,k).wavelength, results(p,k).ber, ...
|
||||
'-o', ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerFaceColor', curves(k).color, ...
|
||||
'MarkerSize', ms, ...
|
||||
'LineWidth', lw, ...
|
||||
'DisplayName', curves(k).name);
|
||||
end
|
||||
|
||||
set(gca,'YScale','log');
|
||||
grid on;
|
||||
if p == 1
|
||||
ylabel('BER');
|
||||
else
|
||||
ylabel('');
|
||||
end
|
||||
xlabel('wavelength');
|
||||
|
||||
ylim([4e-4, 0.1]);
|
||||
|
||||
beautifyBERplot();
|
||||
|
||||
yline([2.2e-4 4.85e-3 2e-2], ...
|
||||
'LineWidth',1.1, 'Color',[0.2 0.2 0.2], ...
|
||||
'LineStyle',':','HandleVisibility','off');
|
||||
|
||||
|
||||
if p == 1
|
||||
|
||||
x1 = 1290;
|
||||
x2 = 1297;
|
||||
x3 = 1300;
|
||||
x4 = 1323;
|
||||
x5 = 1325;
|
||||
x6 = 1330;
|
||||
|
||||
elseif p == 2
|
||||
|
||||
x1 = 1290;
|
||||
x2 = 1295;
|
||||
x3 = 1300;
|
||||
x4 = 1323.5;
|
||||
x5 = 1325;
|
||||
x6 = 1330;
|
||||
|
||||
elseif p == 3
|
||||
|
||||
x1 = 1290;
|
||||
x2 = 1292;
|
||||
x3 = 1298;
|
||||
x4 = 1323;
|
||||
x5 = 1327.5;
|
||||
x6 = 1330;
|
||||
end
|
||||
|
||||
% --- Get current y-limits ---
|
||||
yl = ylim;
|
||||
|
||||
% --- LEFT AREA BELOW KP4 FEC ---
|
||||
patch([x1 x2 x2 x1], [yl(1) yl(1) yl(2) yl(2)], ...
|
||||
clr.Set1.red, ... % RGB = red
|
||||
'FaceAlpha', 0.1, ... % transparency 0.1
|
||||
'EdgeColor', 'none'); % no border
|
||||
|
||||
% --- RIGHT AREA BELOW KP4 FEC ---
|
||||
patch([x2 x3 x3 x2], [yl(1) yl(1) yl(2) yl(2)], ...
|
||||
clr.Set1.blue, ... % RGB = red
|
||||
'FaceAlpha', 0.10, ... % transparency 0.1
|
||||
'EdgeColor', 'none'); % no border
|
||||
|
||||
% --- LEFT AREA BELOW O-FEC ---
|
||||
patch([x4 x5 x5 x4], [yl(1) yl(1) yl(2) yl(2)], ...
|
||||
clr.Set1.blue, ... % RGB = red
|
||||
'FaceAlpha', 0.10, ... % transparency 0.1
|
||||
'EdgeColor', 'none'); % no border
|
||||
|
||||
% --- RIGHT AREA BELOW O-FEC ---
|
||||
patch([x5 x6 x6 x5], [yl(1) yl(1) yl(2) yl(2)], ...
|
||||
clr.Set1.red, ... % RGB = red
|
||||
'FaceAlpha', 0.10, ... % transparency 0.1
|
||||
'EdgeColor', 'none'); % no border
|
||||
|
||||
uistack(findobj(gca,'Type','patch'),'bottom'); % send the patch behind curves
|
||||
|
||||
|
||||
% ax = gca;
|
||||
% axpos = ax.Position; % [x y w h] normalized
|
||||
% xl = xlim;
|
||||
% yl = ylim;
|
||||
%
|
||||
% % Convert axis coords → normalized figure coords
|
||||
% toNorm = @(x,y) [ ...
|
||||
% axpos(1) + (x - xl(1)) / (xl(2)-xl(1)) * axpos(3), ...
|
||||
% axpos(2) + (y - yl(1)) / (yl(2)-yl(1)) * axpos(4) ...
|
||||
% ];
|
||||
%
|
||||
% % Choose vertical placement (10% above bottom of axis)
|
||||
% y_arrow = yl(1) * (yl(2)/yl(1))^0.10; % works with log-scale axes
|
||||
%
|
||||
% % === Arrow 1: x3 <-> x4 ======================================
|
||||
% p1 = toNorm(x3, y_arrow);
|
||||
% p2 = toNorm(x4, y_arrow);
|
||||
%
|
||||
% annotation('doublearrow', ...
|
||||
% [p1(1) p2(1)], [p1(2) p2(2)], ...
|
||||
% 'Color', [0 0 0], 'LineWidth', 1.4);
|
||||
%
|
||||
% % === Arrow 2: x2 <-> x5 ======================================
|
||||
% p3 = toNorm(x2, y_arrow);
|
||||
% p4 = toNorm(x5, y_arrow);
|
||||
%
|
||||
% annotation('doublearrow', ...
|
||||
% [p3(1) p4(1)], [p3(2) p4(2)], ...
|
||||
% 'Color', [0 0 0], 'LineWidth', 1.4);
|
||||
|
||||
end
|
||||
|
||||
pos = 1e3.*[2.7770 1.2017 1.4000 0.3200];
|
||||
set(fig, 'Position', pos);
|
||||
|
||||
%% === EXPORT ===
|
||||
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\wavelength_analysis.tikz';
|
||||
matlab2tikz(outfile, ...
|
||||
'width','\fwidth', ...
|
||||
'height','\fheight', ...
|
||||
'showInfo',false, ...
|
||||
'extraAxisOptions',{ ...
|
||||
'legend style={font=\footnotesize}', ...
|
||||
'legend columns=1' ...
|
||||
});
|
||||
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% DECISION LOGIC (INLINE FUNCTIONS)
|
||||
% ============================================================
|
||||
|
||||
function pe = decide_preemph(M, eq)
|
||||
% PRE-EMPH RULES:
|
||||
switch M
|
||||
case 4
|
||||
if eq == equalizer_structure.vnle
|
||||
pe = 1; % PAM4: VNLE → pre-emph on
|
||||
else
|
||||
pe = 0; % PAM4: all others → off
|
||||
end
|
||||
case {6,8}
|
||||
pe = 1; % PAM6/8: all → pre-emph on
|
||||
otherwise
|
||||
pe = 0;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function flag = decide_precoded(M, eq)
|
||||
% PRE-CODE RULES:
|
||||
if eq == equalizer_structure.vnle_db_mlse
|
||||
flag = 1; % Always for DB-target
|
||||
elseif eq == equalizer_structure.ml_mlse && M == 4
|
||||
flag = 1; % PAM4: ML-based → precoded
|
||||
else
|
||||
flag = 0;
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,132 @@
|
||||
database_type = 'mysql';
|
||||
dataBase = 'labor_highspeed';
|
||||
db = DBHandler("dataBase", dataBase, "type", database_type);
|
||||
|
||||
%% FILTER QUERY
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
fp.where('Runs', 'rop_attenuation','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('power_state_info');
|
||||
fields = [fields; db.getTableFieldNames('dashboard_ungrouped_alltime')];
|
||||
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
%% ---- CONFIG ----
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'grossrate';
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.y_scale = 'log';
|
||||
cfg.outlier = 'mad';
|
||||
cfg.show_raw = false;
|
||||
cfg.show_spread = 'none';
|
||||
cfg.agg = 'min';
|
||||
cfg.show_precoded = 1;
|
||||
cfg.fec_lines = [];
|
||||
|
||||
cfg.plot = struct;
|
||||
cfg.plot.use_cbrewer2 = false;
|
||||
cfg.plot.lineWidth = 2.0;
|
||||
cfg.plot.errWidth = 1.2;
|
||||
cfg.plot.scatterAlpha = 0.35;
|
||||
cfg.plot.legendLocation = 'best';
|
||||
cfg.plot.fecLineWidth = 2.4;
|
||||
cfg.plot.custom_colors_scatter = []; % disabled
|
||||
|
||||
%% ---- DSP DEFINITIONS ----
|
||||
DSP(1).name = 'VNLE';
|
||||
DSP(1).eq = equalizer_structure.vnle;
|
||||
DSP(1).color = clr.Paired.red;
|
||||
DSP(1).lightcolor = clr.Paired.lightred;
|
||||
|
||||
DSP(2).name = 'VNLE PF MLSE';
|
||||
DSP(2).eq = equalizer_structure.vnle_pf_mlse;
|
||||
DSP(2).color = clr.Paired.green;
|
||||
DSP(2).lightcolor = clr.Paired.lightgreen;
|
||||
|
||||
DSP(3).name = 'VNLE DB MLSE';
|
||||
DSP(3).eq = equalizer_structure.vnle_db_mlse;
|
||||
DSP(3).color = clr.Paired.blue;
|
||||
DSP(3).lightcolor = clr.Paired.lightblue;
|
||||
|
||||
DSP(4).name = 'ML MLSE';
|
||||
DSP(4).eq = equalizer_structure.ml_mlse;
|
||||
DSP(4).color = clr.Paired.purple;
|
||||
DSP(4).lightcolor = clr.Paired.lightpurple;
|
||||
|
||||
%% ---- GRID CONFIG ----
|
||||
rows = 3; % PAM 4,6,8
|
||||
cols = 4; % DSP schemes
|
||||
pam = [4 6 8];
|
||||
|
||||
cfg.figure_number = 46;
|
||||
fig = figure(cfg.figure_number); clf;
|
||||
|
||||
t = tiledlayout(rows, cols, ...
|
||||
'TileSpacing','compact', ...
|
||||
'Padding','compact');
|
||||
|
||||
cfg.group_by = {'equalizer_structure','pre_emph'};
|
||||
cfg.plot.use_cbrewer2 = false;
|
||||
|
||||
%% ==== MAIN PLOT LOOP =====
|
||||
for r = 1:rows
|
||||
Mlev = pam(r);
|
||||
|
||||
for c = 1:cols
|
||||
ax = nexttile(t, (r-1)*cols + c);
|
||||
cfg.ax = ax;
|
||||
|
||||
% ---- PRE-EMPH = 1 ----
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',Mlev, ...
|
||||
'equalizer_structure',DSP(c).eq, ...
|
||||
'pre_emph',1);
|
||||
cfg.plot.custom_colors = DSP(c).lightcolor;
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
[~, M1] = plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% ---- PRE-EMPH = 0 ----
|
||||
cfg.filters.pre_emph = 0;
|
||||
cfg.plot.custom_colors = DSP(c).color;
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
[~, M0] = plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% Axis limits
|
||||
if Mlev == 4
|
||||
ylim([1e-5 0.3]);
|
||||
elseif Mlev == 6
|
||||
ylim([6e-4 0.1]);
|
||||
elseif Mlev == 8
|
||||
ylim([9e-4 0.1]);
|
||||
end
|
||||
|
||||
% ---- FEC lines ----
|
||||
yline([2.2e-4 4.85e-3 2e-2], ...
|
||||
'LineWidth',1.1, 'Color',[0.2 0.2 0.2], ...
|
||||
'LineStyle',':','HandleVisibility','off');
|
||||
|
||||
beautifyBERplot;
|
||||
|
||||
% ---- Remove redundant labels ----
|
||||
if c > 1, ax.YLabel = []; end
|
||||
if r < rows, ax.XLabel = []; end
|
||||
|
||||
grid(ax,'on'); box(ax,'on');
|
||||
end
|
||||
end
|
||||
|
||||
%% ---- FIXED FIGURE SIZE ----
|
||||
pos = 1e3.*[0.1070 0.5497 1.4113 0.6847];
|
||||
set(fig, 'Position', pos);
|
||||
|
||||
% %% === EXPORT ===
|
||||
% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_pre_emphasis.tikz';
|
||||
% matlab2tikz(outfile, ...
|
||||
% 'width','\fwidth', ...
|
||||
% 'height','\fheight', ...
|
||||
% 'showInfo',false, ...
|
||||
% 'extraAxisOptions',{ ...
|
||||
% 'legend style={font=\footnotesize}', ...
|
||||
% 'legend columns=1' ...
|
||||
% });
|
||||
@@ -0,0 +1,257 @@
|
||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||
dsp_options.max_occurences = 1;
|
||||
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
||||
|
||||
rate = [300e9];
|
||||
cols = cbrewer2('BuPu',25);
|
||||
cols = [cols(end-10:2:end,:)];
|
||||
cols = cbrewer2('Set1',6);
|
||||
|
||||
fignum = 200;
|
||||
fig=figure(fignum);clf;
|
||||
|
||||
dbmode = 0;
|
||||
|
||||
|
||||
% 1 - PAM 4 with preemphasis
|
||||
fp = QueryFilter();
|
||||
M = 6;
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
fp.where('Runs', 'db_mode','EQUALS', dbmode);
|
||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||
|
||||
[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
|
||||
|
||||
dataTable = queryRunid(dataTable.run_id, database);
|
||||
fsym = dataTable.symbolrate;
|
||||
M = double(dataTable.pam_level);
|
||||
duob_mode = db_mode(strrep(dataTable.db_mode,'"',''));
|
||||
|
||||
% Load and Sync signal data from DB
|
||||
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
|
||||
|
||||
% Preprocess signal
|
||||
Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
|
||||
|
||||
Scpe_sig.eye(fsym,M,"fignum",M*10);
|
||||
|
||||
%% === EXPORT TO TIKZ ===
|
||||
% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\eye_pam_',num2str(M),'.tikz'];
|
||||
% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\vnle_optimization.tikz'];
|
||||
% matlab2tikz(outfile, ...
|
||||
% 'width','\fwidth', ...
|
||||
% 'height','\fheight', ...
|
||||
% 'showInfo',false, ...
|
||||
% 'extraAxisOptions',{ ...
|
||||
% 'legend style={font=\footnotesize}', ...
|
||||
% 'legend columns=1' ...
|
||||
% } );
|
||||
|
||||
%%
|
||||
|
||||
if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
|
||||
trellexlusion = 1;
|
||||
else
|
||||
trellexlusion = 0;
|
||||
end
|
||||
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',3);
|
||||
len_tr = 4096*2;
|
||||
|
||||
ffe_order = [50, 5, 5];
|
||||
dfe_order = [0, 0, 0];
|
||||
pf_ncoeffs = 1;
|
||||
mu_ffe = [0.0001, 0.0008, 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
mu_dc = 0.005;
|
||||
dc_buffer_len = 1;
|
||||
|
||||
mu_tr = 0;
|
||||
mu_dd = 0.05;
|
||||
adaption= 1;
|
||||
use_dd_mode = 1;
|
||||
ffe_order = [50, 5, 5];
|
||||
eq_ = EQ("Ne",ffe_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);
|
||||
|
||||
dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
|
||||
"precode_mode", duob_mode, ...
|
||||
'showAnalysis', 1,...
|
||||
"postFFE", []);
|
||||
|
||||
%% === FINAL FIGURE SIZE ===
|
||||
|
||||
% Existing figure numbers
|
||||
figEye = 249;
|
||||
figConst = 341;
|
||||
|
||||
% Find axes in the source figures
|
||||
srcAxEye = findobj(figEye, 'Type', 'axes');
|
||||
srcAxConst = findobj(figConst, 'Type', 'axes');
|
||||
|
||||
% Create new combined figure
|
||||
figCombined = figure;
|
||||
t = tiledlayout(figCombined, 1, 2);
|
||||
t.TileSpacing = 'compact';
|
||||
t.Padding = 'compact';
|
||||
|
||||
% ------------------------------------------------------------
|
||||
% LEFT TILE: EYE DIAGRAM
|
||||
% ------------------------------------------------------------
|
||||
ax1 = nexttile(t, 1);
|
||||
hold(ax1, 'on')
|
||||
|
||||
% Copy children (images, lines, patches, hist objects, etc.)
|
||||
copyobj(srcAxEye.Children, ax1);
|
||||
|
||||
% Copy labels and title
|
||||
ax1.XLabel.String = srcAxEye.XLabel.String;
|
||||
ax1.YLabel.String = srcAxEye.YLabel.String;
|
||||
ax1.Title.String = srcAxEye.Title.String;
|
||||
|
||||
% Copy axis limits
|
||||
ax1.XLim = srcAxEye.XLim;
|
||||
ax1.YLim = srcAxEye.YLim;
|
||||
ax1.YDir = srcAxEye.YDir;
|
||||
|
||||
% Copy ticks + labels EXACTLY (including remapped/scaled ones)
|
||||
ax1.XTick = srcAxEye.XTick;
|
||||
ax1.XTickLabel = srcAxEye.XTickLabel;
|
||||
ax1.YTick = srcAxEye.YTick;
|
||||
ax1.YTickLabel = srcAxEye.YTickLabel;
|
||||
|
||||
% Copy colormap + clim (important for density eye)
|
||||
colormap(ax1, colormap(srcAxEye.Parent));
|
||||
ax1.CLim = srcAxEye.CLim;
|
||||
|
||||
% Copy any style props that matter
|
||||
ax1.TickDir = srcAxEye.TickDir;
|
||||
ax1.TickLength = srcAxEye.TickLength;
|
||||
ax1.FontSize = srcAxEye.FontSize;
|
||||
ax1.Box = srcAxEye.Box;
|
||||
|
||||
grid(ax1,'on');
|
||||
|
||||
|
||||
% ------------------------------------------------------------
|
||||
% RIGHT TILE: CONSTELLATION HISTOGRAM
|
||||
% ------------------------------------------------------------
|
||||
ax2 = nexttile(t, 2);
|
||||
hold(ax2, 'on')
|
||||
|
||||
copyobj(srcAxConst.Children, ax2);
|
||||
|
||||
% Copy labels and title
|
||||
ax2.XLabel.String = srcAxConst.XLabel.String;
|
||||
ax2.YLabel.String = srcAxConst.YLabel.String;
|
||||
ax2.Title.String = srcAxConst.Title.String;
|
||||
|
||||
% The histogram uses the same y-axis as the eye
|
||||
% Extract mapping from eye
|
||||
rawTicks = ax1.YTick;
|
||||
rawLabelsCell = ax1.YTickLabel;
|
||||
trueVoltages = str2double(rawLabelsCell);
|
||||
|
||||
% Apply true voltages to the histogram axis
|
||||
ax2.XTick = flip(trueVoltages);
|
||||
ax2.XTickLabel = flip(rawLabelsCell);
|
||||
|
||||
% Set histogram y-limits to match the actual voltages
|
||||
ax2.XLim = [min(trueVoltages) max(trueVoltages)];
|
||||
|
||||
% Ensure eye diagram prints the same (we *do not* touch ax1.YLim)
|
||||
ax1.XTickLabel = rawLabelsCell;
|
||||
|
||||
|
||||
% Copy colormap (your histogram uses same palette)
|
||||
colormap(ax2, colormap(srcAxConst.Parent));
|
||||
|
||||
% Style properties
|
||||
ax2.TickDir = srcAxConst.TickDir;
|
||||
ax2.TickLength = srcAxConst.TickLength;
|
||||
ax2.FontSize = srcAxConst.FontSize;
|
||||
ax2.Box = srcAxConst.Box;
|
||||
|
||||
grid(ax2,'on');
|
||||
|
||||
% ============================================================
|
||||
% remove right y-axis completely
|
||||
% ============================================================
|
||||
ax2.XAxis.Visible = 'off'; % hides ticks + labels + axis line
|
||||
|
||||
% BUT we still keep the YTick positions internally for alignment:
|
||||
% ax2.YTick = <values already set earlier> ;
|
||||
|
||||
|
||||
% ============================================================
|
||||
% minimize distance between the two plots
|
||||
% ============================================================
|
||||
t.TileSpacing = 'none'; % no space between tiles
|
||||
t.Padding = 'none'; % no outer padding
|
||||
|
||||
% Also reduce internal padding for each axis
|
||||
ax1.Position(3) = ax1.Position(3) + 0.02; % widen eye a bit
|
||||
ax2.Position(1) = ax2.Position(1) - 0.02; % pull histogram closer
|
||||
|
||||
|
||||
% Keep left axis grid visible
|
||||
ax2.YGrid = 'off';
|
||||
|
||||
%
|
||||
% =====================================================================
|
||||
% FINAL POLISHING: unified visual style
|
||||
% =======================================================================
|
||||
|
||||
% --- unified font size ---
|
||||
FS = 12;
|
||||
set([ax1 ax2], 'FontSize', FS);
|
||||
|
||||
% --- unified axis line width (outline stroke thickness) ---
|
||||
LW = 1.0;
|
||||
set([ax1 ax2], 'LineWidth', LW);
|
||||
|
||||
% --- unified tick length ---
|
||||
TL = [.015 .015];
|
||||
set([ax1 ax2], 'TickLength', TL);
|
||||
|
||||
% --- unified grid style ---
|
||||
set([ax1 ax2], 'XGrid', 'on', 'YGrid', 'on');
|
||||
set([ax1 ax2], 'GridLineStyle', '--');
|
||||
set([ax1 ax2], 'GridAlpha', 0.2);
|
||||
|
||||
% --- remove right y-axis ticks and labels ---
|
||||
ax2.YAxis.Visible = 'off';
|
||||
|
||||
% --- copy colormap + CLim from the eye to histogram (synchronize look) ---
|
||||
colormap(ax1, colormap(srcAxEye.Parent));
|
||||
colormap(ax2, colormap(srcAxEye.Parent));
|
||||
ax2.CLim = ax1.CLim;
|
||||
|
||||
% --- minimal spacing between tiles ---
|
||||
t.TileSpacing = 'none';
|
||||
t.Padding = 'none';
|
||||
|
||||
|
||||
% --- pull the panels together (touching boundary effect) ---
|
||||
pos1 = ax1.Position;
|
||||
pos2 = ax2.Position;
|
||||
|
||||
% Shift histogram left until the outlines touch
|
||||
pos2(1) = pos1(1) + pos1(3) - 0.002; % 0.002 = fine overlap control
|
||||
ax2.Position = pos2;
|
||||
|
||||
% Expand histogram slightly, remove white band
|
||||
pos2 = ax2.Position;
|
||||
pos2(3) = pos2(3) + 0.01;
|
||||
ax2.Position = pos2;
|
||||
|
||||
% Ensure the left plot stays correct after the move
|
||||
ax1.Position = pos1;
|
||||
|
||||
% --- enforce same visible outline ---
|
||||
% For ax2, create a fake left spine (since YAxis is hidden)
|
||||
ax2.Box = 'on'; % keep outline but no ticks on the right
|
||||
ax1.Box = 'on';
|
||||
|
||||
ax2.View = [90 -90];
|
||||
@@ -0,0 +1,221 @@
|
||||
%% ============================================================
|
||||
% GRID: NGMI, AIR, HD-NetRate, SD-NetRate (1 × 4)
|
||||
% ============================================================
|
||||
|
||||
db = DBHandler("dataBase","labor_highspeed","type","mysql");
|
||||
|
||||
%% --- Base DB Filters (shared across all curves)
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','fiber_length','EQUALS', 2);
|
||||
fp.where('Runs','wavelength','EQUALS', 1310);
|
||||
fp.where('Runs','rop_attenuation','EQUALS', 0);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('dashboard_ungrouped_alltime');
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
|
||||
%% === Curve Definitions =======================================
|
||||
curves = struct;
|
||||
|
||||
% PAM-8 — VNLE PF MLSE — no_emph = 1 — RED
|
||||
curves(1).pam = 8;
|
||||
curves(1).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(1).pre = 0;
|
||||
curves(1).color = clr.Paired.red;
|
||||
curves(1).mkr = 'o';
|
||||
|
||||
% PAM-6 — VNLE PF MLSE — no_emph = 1 — BLUE
|
||||
curves(2).pam = 6;
|
||||
curves(2).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(2).pre = 1;
|
||||
curves(2).color = clr.Paired.blue;
|
||||
curves(2).mkr = 'square';
|
||||
|
||||
% PAM-4 — VNLE DB MLSE — pre_emph = 0 — GREEN
|
||||
curves(3).pam = 4;
|
||||
curves(3).eq = equalizer_structure.vnle_db_mlse;
|
||||
curves(3).pre = 0;
|
||||
curves(3).color = clr.Paired.green;
|
||||
curves(3).mkr = 'diamond';
|
||||
|
||||
%% === Prepare Analysis Config ==================================
|
||||
base = struct;
|
||||
base.group_by = {'equalizer_structure','pre_emph'};
|
||||
base.x_axis = 'symbolrate';
|
||||
base.outlier = 'none';
|
||||
base.show_raw = false;
|
||||
base.filters = struct; % will be filled per curve
|
||||
|
||||
|
||||
%% === Precompute All Curves ====================================
|
||||
results = struct;
|
||||
|
||||
for k = 1:numel(curves)
|
||||
|
||||
% --- BER ---
|
||||
cfg = base;
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.agg = 'min';
|
||||
cfg.filters = struct('pam_level', curves(k).pam, ...
|
||||
'equalizer_structure', curves(k).eq, ...
|
||||
'pre_emph', curves(k).pre);
|
||||
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
cfg.x_axis = 'grossrate';
|
||||
B = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
results(k).baudr = A.group{1}.x;
|
||||
results(k).gross = B.group{1}.x;
|
||||
|
||||
if curves(k).pam == 4
|
||||
results(k).ber = A.group{1}.y_precoded;
|
||||
else
|
||||
results(k).ber = A.group{1}.y;
|
||||
end
|
||||
|
||||
% --- NGMI ---
|
||||
cfg.y_axis = 'NGMI';
|
||||
cfg.agg = 'max';
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
results(k).ngmi = A.group{1}.y;
|
||||
|
||||
|
||||
|
||||
% --- AIR ---
|
||||
cfg.y_axis = 'AIR';
|
||||
cfg.agg = 'max';
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
results(k).air = A.group{1}.y;
|
||||
results(k).air = results(k).ngmi .* results(k).gross;
|
||||
|
||||
% --- Net Rates ---
|
||||
tp = TransmissionPerformance;
|
||||
results(k).ndr = tp.calculateNetRate(results(k).gross, ...
|
||||
'NGMI', results(k).ngmi, ...
|
||||
'BER', results(k).ber);
|
||||
end
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% FIGURE: 1 × 4 GRID
|
||||
% ============================================================
|
||||
fig = figure(71); clf;
|
||||
t = tiledlayout(1,4, 'TileSpacing','compact', 'Padding','compact');
|
||||
|
||||
lw = 1.0;
|
||||
|
||||
% === NGMI vs Grossrate ===
|
||||
ax = nexttile(t,1);
|
||||
hold on;
|
||||
for k = 1:3
|
||||
plot(results(k).baudr, results(k).ngmi, ...
|
||||
'LineWidth', lw, ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerSize', 1, ...
|
||||
'MarkerFaceColor', curves(k).color,...
|
||||
'Marker',curves(k).mkr);
|
||||
end
|
||||
ylabel('NGMI');
|
||||
xlabel('Baud rate [GBd]');
|
||||
xlim([100 210]);
|
||||
xticks(100:15:225);
|
||||
ylim([0.9, 1]);
|
||||
grid minor; box on;
|
||||
beautifyBERplot("logscale",0,"setmarkers",0);
|
||||
|
||||
|
||||
% === AIR vs Grossrate ===
|
||||
ax = nexttile(t,2);
|
||||
hold on;
|
||||
for k = 1:3
|
||||
plot(results(k).baudr, results(k).air, ...
|
||||
'-', 'LineWidth', lw, ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerSize', 2, ...
|
||||
'MarkerFaceColor', curves(k).color,'Marker',curves(k).mkr);
|
||||
end
|
||||
ylabel('AIR [Gb/s]');
|
||||
xlabel('Baud rate [GBd]');
|
||||
ylim([280 430]);
|
||||
yticks(280:30:440)
|
||||
xlim([100 210]);
|
||||
xticks(100:15:225);
|
||||
grid minor; box on;
|
||||
beautifyBERplot("logscale",0,"setmarkers",0);
|
||||
yline(400,'LineStyle','--');
|
||||
|
||||
% === SD-FEC Net Rate ===
|
||||
ax = nexttile(t,3);
|
||||
hold on;
|
||||
for k = 1:3
|
||||
plot(results(k).baudr, results(k).ndr.SDHD.NetRate, ...
|
||||
'LineWidth', lw, ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerSize', 2, ...
|
||||
'MarkerFaceColor', curves(k).color,...
|
||||
'Marker',curves(k).mkr);
|
||||
end
|
||||
ylabel('NDR [Gb/s]');
|
||||
xlabel('Baud rate [GBd]');
|
||||
ylim([280 430]);
|
||||
yticks(280:30:440)
|
||||
xlim([100 210]);
|
||||
xticks(100:15:225);
|
||||
grid minor; box on;
|
||||
beautifyBERplot("logscale",0,"setmarkers",0);
|
||||
yline(400,'LineStyle','--');
|
||||
|
||||
% === HD-FEC Net Rate ===
|
||||
ax = nexttile(t,4);
|
||||
hold on;
|
||||
for k = 1:3
|
||||
% plot(results(k).baudr, results(k).ndr.STAIR.NetRate, ...
|
||||
% '-', 'LineWidth', lw, ...
|
||||
% 'Color', curves(k).color, ...
|
||||
% 'MarkerSize', 4,'Marker','+', ...
|
||||
% 'MarkerFaceColor', curves(k).color);
|
||||
|
||||
plot(results(k).baudr, results(k).ndr.O_FEC.NetRate, ...
|
||||
':', 'LineWidth', lw, ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerSize', 2,...
|
||||
'MarkerFaceColor', curves(k).color,...
|
||||
'Marker',curves(k).mkr);
|
||||
|
||||
plot(results(k).baudr, results(k).ndr.KP4_hamming.NetRate, ...
|
||||
'--', 'LineWidth', lw, ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerSize', 2,'Marker','diamond', ...
|
||||
'MarkerFaceColor', curves(k).color,...
|
||||
'Marker',curves(k).mkr);
|
||||
end
|
||||
|
||||
yline(400,'LineStyle','--');
|
||||
ylabel('');
|
||||
xlabel('Baud rate [GBd]');
|
||||
ylim([280 430]);
|
||||
yticks(280:30:440)
|
||||
xlim([100 210]);
|
||||
xticks(100:15:225);
|
||||
grid minor; box on;
|
||||
beautifyBERplot("logscale",0,"setmarkers",0);
|
||||
|
||||
% === FINAL FIGURE SIZE ===
|
||||
pos = 1e3.*[0.7950 1.1150 1.4113 0.1900];
|
||||
set(fig, 'Position', pos);
|
||||
|
||||
% % % %% === EXPORT ===
|
||||
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_ndr_v3.tikz';
|
||||
matlab2tikz(outfile, ...
|
||||
'width','\fwidth', ...
|
||||
'height','\fheight', ...
|
||||
'showInfo',false, ...
|
||||
'extraAxisOptions',{ ...
|
||||
'legend style={font=\footnotesize}', ...
|
||||
'legend columns=1' ...
|
||||
'every axis/.append style={font=\scriptsize}',...
|
||||
'minor grid style={line width=0.2pt, solid, color=black!10}',...
|
||||
'grid style={line width=0.4pt, solid, color=black!20}',...
|
||||
'grid style={dashed}',...
|
||||
});
|
||||
@@ -0,0 +1,180 @@
|
||||
%% ============================================================
|
||||
% GRID (1 × 4):
|
||||
% 1) NGMI overview (PAM4+PAM6+PAM8 superimposed)
|
||||
% 2) PAM-4 tile (AIR + SD-NDR + HD-NDR)
|
||||
% 3) PAM-6 tile
|
||||
% 4) PAM-8 tile
|
||||
% ============================================================
|
||||
|
||||
db = DBHandler("dataBase","labor_highspeed","type","mysql");
|
||||
|
||||
%% --- Base DB Filters (shared across all curves)
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','fiber_length','EQUALS', 2);
|
||||
fp.where('Runs','wavelength','EQUALS', 1310);
|
||||
fp.where('Runs','rop_attenuation','EQUALS', 0);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('dashboard_ungrouped_alltime');
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
%% === CURVE DEFINITIONS =================================================
|
||||
curves = struct;
|
||||
|
||||
curves(1).pam = 8;
|
||||
curves(1).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(1).pre = 0;
|
||||
curves(1).color = clr.Paired.red;
|
||||
|
||||
curves(2).pam = 6;
|
||||
curves(2).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(2).pre = 1;
|
||||
curves(2).color = clr.Paired.blue;
|
||||
|
||||
curves(3).pam = 4;
|
||||
curves(3).eq = equalizer_structure.vnle_db_mlse;
|
||||
curves(3).pre = 0;
|
||||
curves(3).color = clr.Paired.green;
|
||||
|
||||
% === ANALYSIS ENGINE (extract BER/NGMI/AIR/netrates) ===================
|
||||
base = struct;
|
||||
base.group_by = {'equalizer_structure','pre_emph'};
|
||||
base.x_axis = 'grossrate';
|
||||
base.outlier = 'none';
|
||||
base.show_raw = false;
|
||||
|
||||
results = struct;
|
||||
|
||||
for k = 1:numel(curves)
|
||||
|
||||
% ========== BER ==========
|
||||
cfg = base;
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.agg = 'min';
|
||||
|
||||
cfg.filters = struct('pam_level', curves(k).pam, ...
|
||||
'equalizer_structure', curves(k).eq, ...
|
||||
'pre_emph', curves(k).pre);
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
results(k).gross = A.group{1}.x;
|
||||
if curves(k).pam == 4
|
||||
results(k).ber = A.group{1}.y_precoded;
|
||||
else
|
||||
results(k).ber = A.group{1}.y;
|
||||
end
|
||||
|
||||
% ========== NGMI ==========
|
||||
cfg.y_axis = 'NGMI'; cfg.agg = 'max';
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
results(k).ngmi = A.group{1}.y;
|
||||
|
||||
% ========== AIR ==========
|
||||
cfg.y_axis = 'AIR'; cfg.agg = 'max';
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
results(k).air = A.group{1}.y;
|
||||
|
||||
% ========== NET RATES ==========
|
||||
tp = TransmissionPerformance;
|
||||
results(k).ndr = tp.calculateNetRate(results(k).gross, ...
|
||||
'NGMI', results(k).ngmi, ...
|
||||
'BER', results(k).ber);
|
||||
end
|
||||
|
||||
|
||||
% ============================================================
|
||||
% FIGURE
|
||||
% ============================================================
|
||||
fig = figure(3);
|
||||
t = tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
|
||||
|
||||
lw = 1.7;
|
||||
|
||||
% =======================================================================
|
||||
% (1) NGMI OVERVIEW TILE (all 3 curves)
|
||||
% =======================================================================
|
||||
ax = nexttile(t,1); hold on;
|
||||
|
||||
for k = 1:3
|
||||
plot(results(k).gross, results(k).ngmi, ...
|
||||
'-o', 'Color', curves(k).color, ...
|
||||
'LineWidth',lw,'MarkerSize',5, ...
|
||||
'MarkerFaceColor',curves(k).color);
|
||||
end
|
||||
|
||||
ylabel('NGMI');
|
||||
xlabel('Grossrate [Gb/s]');
|
||||
ylim([0.9 1]); % your chosen limits
|
||||
xlim([300 480]);
|
||||
xticks(300:30:480)
|
||||
grid minor; box on;
|
||||
beautifyBERplot;
|
||||
|
||||
% =======================================================================
|
||||
% (2–4) PAM-SPECIFIC TILES: AIR, SD-NDR, HD-NDR
|
||||
% =======================================================================
|
||||
|
||||
pam_order = [4 6 8]; % left → right
|
||||
|
||||
for ti = 1:3
|
||||
pam_target = pam_order(ti);
|
||||
ax = nexttile(t, 1+ti); hold on;
|
||||
|
||||
% find matching curve
|
||||
for k = 1:3
|
||||
if curves(k).pam ~= pam_target, continue; end
|
||||
|
||||
col = curves(k).color;
|
||||
|
||||
% AIR
|
||||
plot(results(k).gross, results(k).air, ...
|
||||
'-','Color',col,'LineWidth',lw,'Marker','*', ...
|
||||
'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','AIR');
|
||||
|
||||
% SD-based net rate
|
||||
plot(results(k).gross, results(k).ndr.SDHD.NetRate, ...
|
||||
'--','Color',col,'LineWidth',lw,'Marker','v', ...
|
||||
'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','SD+HD');
|
||||
|
||||
% HD-based net rate
|
||||
plot(results(k).gross, results(k).ndr.STAIR.NetRate, ...
|
||||
':','Color',col,'LineWidth',lw,'Marker','x', ...
|
||||
'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','HD-FEC (Staircase)');
|
||||
|
||||
% HD-based net rate
|
||||
plot(results(k).gross, results(k).ndr.O_FEC.NetRate, ...
|
||||
'LineStyle','-.','Color',col,'LineWidth',lw,'Marker','+', ...
|
||||
'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','O-FEC');
|
||||
|
||||
% HD-based net rate
|
||||
plot(results(k).gross, results(k).ndr.KP4_hamming.NetRate, ...
|
||||
'LineStyle','-','Color',col,'LineWidth',lw,'Marker','x', ...
|
||||
'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','KP4+Hamming');
|
||||
end
|
||||
|
||||
ylabel('NDR [Gb/s]');
|
||||
xlabel('Grossrate [Gb/s]');
|
||||
ylim([300 440]); % your chosen limits
|
||||
yticks(300:20:480)
|
||||
xlim([300 480]);
|
||||
xticks(300:30:480)
|
||||
grid minor; box on;
|
||||
beautifyBERplot;
|
||||
yline(400,'HandleVisibility','off');
|
||||
end
|
||||
|
||||
% === FIX FIGURE SIZE FOR TIKZ ==========================================
|
||||
if 0
|
||||
pos = 1e3.*[0.3643 0.9943 1.4113 0.2120];
|
||||
set(fig,'Position',pos);
|
||||
|
||||
% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_ndr.tikz';
|
||||
% matlab2tikz(outfile, ...
|
||||
% 'width','\fwidth', ...
|
||||
% 'height','\fheight', ...
|
||||
% 'showInfo',false, ...
|
||||
% 'extraAxisOptions',{ ...
|
||||
% 'legend style={font=\footnotesize}', ...
|
||||
% 'legend columns=1' ...
|
||||
% });
|
||||
end
|
||||
@@ -0,0 +1,153 @@
|
||||
%% ============================================================
|
||||
% LOAD DATA (PAM-4, sweep over ROP)
|
||||
% ============================================================
|
||||
database_type = 'mysql';
|
||||
db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
|
||||
|
||||
pam_level = 4;
|
||||
fiberL = 1; % 1 km
|
||||
wlen = 1310;
|
||||
baudrate = 360e9;
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','pam_level','EQUALS', pam_level);
|
||||
fp.where('Runs','fiber_length','EQUALS', fiberL);
|
||||
fp.where('Runs','wavelength','EQUALS', wlen);
|
||||
fp.where('Runs','bitrate','EQUALS', baudrate);
|
||||
fp.where('Runs','power_pd_in','LESS_THAN', 7);
|
||||
|
||||
fields = [
|
||||
db.getTableFieldNames('power_state_info');
|
||||
db.getTableFieldNames('dashboard_ungrouped_alltime')
|
||||
];
|
||||
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% DSP SCHEMES (Best combinations only)
|
||||
% ============================================================
|
||||
curves = struct;
|
||||
|
||||
curves(1).name = 'VNLE';
|
||||
curves(1).eq = equalizer_structure.vnle;
|
||||
curves(1).color = clr.Paired.red;
|
||||
|
||||
curves(2).name = 'PF + MLSE';
|
||||
curves(2).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(2).color = clr.Paired.green;
|
||||
|
||||
curves(3).name = 'DB-target + MLSE';
|
||||
curves(3).eq = equalizer_structure.vnle_db_mlse;
|
||||
curves(3).color = clr.Paired.blue;
|
||||
|
||||
curves(4).name = 'ML-based MLSE';
|
||||
curves(4).eq = equalizer_structure.ml_mlse;
|
||||
curves(4).color = clr.Paired.purple;
|
||||
|
||||
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% ANALYSIS ENGINE (No plotting)
|
||||
% ============================================================
|
||||
results = struct;
|
||||
|
||||
for k = 1:numel(curves)
|
||||
|
||||
pre_emph = decide_preemph(pam_level,curves(k).eq);
|
||||
precoded = decide_precoded(pam_level,curves(k).eq);
|
||||
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'power_mzm'; % ROP axis
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.agg = 'min';
|
||||
cfg.outlier = 'none';
|
||||
cfg.show_raw = false;
|
||||
|
||||
cfg.filters = struct( ...
|
||||
'pam_level', pam_level, ...
|
||||
'fiber_length', fiberL, ...
|
||||
'wavelength', wlen, ...
|
||||
'bitrate', baudrate, ...
|
||||
'is_mpi', 0, ...
|
||||
'equalizer_structure', curves(k).eq, ...
|
||||
'pre_emph', pre_emph);
|
||||
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
results(k).x = A.group{1}.x;
|
||||
if precoded
|
||||
results(k).ber = A.group{1}.y_precoded;
|
||||
else
|
||||
results(k).ber = A.group{1}.y;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% PLOT — BER vs ROP (Single Axis)
|
||||
% ============================================================
|
||||
fig = figure(); clf; hold on;
|
||||
|
||||
lw = 2.0;
|
||||
ms = 7;
|
||||
|
||||
for k = 1:numel(curves)
|
||||
plot(results(k).x, results(k).ber, ...
|
||||
'-o', ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerFaceColor', curves(k).color, ...
|
||||
'MarkerSize', ms, ...
|
||||
'LineWidth', lw, ...
|
||||
'DisplayName', curves(k).name);
|
||||
end
|
||||
|
||||
set(gca,'YScale','log');
|
||||
grid on;
|
||||
|
||||
xlabel('ROP / Power (MZM) [dBm]');
|
||||
ylabel('BER');
|
||||
|
||||
ylim([1e-4 2e-1]);
|
||||
|
||||
title(sprintf('BER vs ROP — PAM-%d, %.0f km, %.0f GBd, %.0f nm', ...
|
||||
pam_level, fiberL, baudrate*1e-9, wlen));
|
||||
|
||||
legend('Location','best');
|
||||
beautifyBERplot();
|
||||
|
||||
pos = 1e3.*[0.2 0.6 1.3 0.4];
|
||||
set(fig, 'Position', pos);
|
||||
|
||||
%% ============================================================
|
||||
% DECISION LOGIC (INLINE FUNCTIONS)
|
||||
% ============================================================
|
||||
|
||||
function pe = decide_preemph(M, eq)
|
||||
% PRE-EMPH RULES:
|
||||
switch M
|
||||
case 4
|
||||
if eq == equalizer_structure.vnle
|
||||
pe = 1; % PAM4: VNLE → pre-emph on
|
||||
else
|
||||
pe = 0; % PAM4: all others → off
|
||||
end
|
||||
case {6,8}
|
||||
pe = 1; % PAM6/8: all → pre-emph on
|
||||
otherwise
|
||||
pe = 0;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function flag = decide_precoded(M, eq)
|
||||
% PRE-CODE RULES:
|
||||
if eq == equalizer_structure.vnle_db_mlse
|
||||
flag = 1; % Always for DB-target
|
||||
elseif eq == equalizer_structure.ml_mlse && M == 4
|
||||
flag = 1; % PAM4: ML-based → precoded
|
||||
else
|
||||
flag = 0;
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,182 @@
|
||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||
dsp_options.max_occurences = 1;
|
||||
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
||||
|
||||
rates = [300e9];
|
||||
cols = cbrewer2('BuPu',25);
|
||||
cols = [cols(end-10:2:end,:)];
|
||||
cols = cbrewer2('Set1',6);
|
||||
|
||||
fignum = 200;
|
||||
fig=figure(fignum);clf;
|
||||
|
||||
for dbmode = 0:1%length(rates)
|
||||
|
||||
|
||||
if 0
|
||||
rcalpha = 0.05;
|
||||
fsym = rates/2;
|
||||
pulsef = 1;
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
Pamsource = PAMsource(...
|
||||
"fsym",fsym,"M",4,"order",18,"useprbs",0,...
|
||||
"fs_out",fdac,...
|
||||
"applyclipping",0,"clipfactor",1.2,...
|
||||
"applypulseform",pulsef,"pulseformer",Pform,...
|
||||
"randkey",20,...
|
||||
"db_precode",dbmode,"db_encode",0,...
|
||||
"mrds_code",0,"mrds_blocklength",512);
|
||||
|
||||
[Digi_sig,Symbols,Bits] = Pamsource.process();
|
||||
|
||||
Digi_sig = Digi_sig.normalize("mode","rms");
|
||||
|
||||
%%% 1) PLOT FULL RESPONSE SIGNAL
|
||||
Digi_sig.spectrum("displayname","Full Response","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0.2,0.2,0.2],"linestyle",'-','addDCoffset',0,'normalizeToDC',1);
|
||||
|
||||
|
||||
%%% 2) PLOT PREEMPH. TX SIGNAL
|
||||
if dbmode == 0
|
||||
maxamp = -37;
|
||||
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
|
||||
|
||||
precomp_path = "C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\precomp";
|
||||
precomp_fn = "lab_high_speed";
|
||||
Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
|
||||
Digi_sig_pre = Digi_sig_pre.resample("fs_out",fdac);
|
||||
|
||||
Digi_sig_pre= Digi_sig_pre.normalize("mode","rms");
|
||||
|
||||
Digi_sig_pre.spectrum("displayname","Strong Precomp","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0,0,0],"linestyle",'-.','addDCoffset',0,'normalizeToDC',1);
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
% 1 - PAM 4 with preemphasis
|
||||
fp = QueryFilter();
|
||||
M = 4;
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'bitrate','EQUALS', rates);%360,390
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 10);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1322.7); %1327.4
|
||||
fp.where('Runs', 'db_mode','EQUALS', dbmode);
|
||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||
|
||||
[dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
|
||||
|
||||
dataTable = queryRunid(dataTable.run_id, database);
|
||||
fsym = dataTable.symbolrate;
|
||||
M = double(dataTable.pam_level);
|
||||
|
||||
% Load and Sync signal data from DB
|
||||
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
|
||||
|
||||
% Preprocess signal
|
||||
Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
|
||||
Scpe_sig = Scpe_cell{1};
|
||||
|
||||
%%% 3) PLOT DB Tgt. SIGNAL
|
||||
if 1
|
||||
DB_Symbols = Duobinary().encode(Symbols);
|
||||
DB_Symbols.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'DB-Response','addDCoffset',0,'color',clr.Set1.blue,'normalizeToNyquist',0,'linestyle','--');
|
||||
end
|
||||
|
||||
%%% 4) Plot RX Signal
|
||||
Scpe_sig.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'Rx','addDCoffset',1,'color',[0,0,0],'normalizeToNyquist',0,'linestyle',':');
|
||||
Scpe_sig.eye(fsym,M,"fignum",47,"displayname",' Eye of AVG Signal');
|
||||
% xline(Symbols.fs/2.*1e-9,'Color',cols(r,:),'HandleVisibility','off');
|
||||
|
||||
average_signals = 1;
|
||||
if average_signals
|
||||
Scpe_sig_avg = Scpe_sig;
|
||||
scope_mean = zeros(size(Scpe_cell{1}.signal));
|
||||
for n=1:numel(Scpe_cell)
|
||||
scope_mean = scope_mean + Scpe_cell{n}.signal;
|
||||
end
|
||||
scope_mean = scope_mean ./ n;
|
||||
Scpe_sig_avg.signal = scope_mean;
|
||||
|
||||
Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1);
|
||||
Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
|
||||
Scpe_sig_avg.eye(fsym,M,"fignum",48,"displayname",' Eye of AVG Signal');
|
||||
end
|
||||
|
||||
|
||||
fig = figure(fignum+dbmode);
|
||||
if dbmode == 0
|
||||
ylim([-22,12]);
|
||||
else
|
||||
ylim([-22,2]);
|
||||
end
|
||||
xlim([0,105]);
|
||||
xticks(-100:20:100);
|
||||
yticks(-20:10:10);
|
||||
|
||||
beautifyBERplot("logscale",0,"setmarkers",0)
|
||||
pos = [100.3333 991.6667 358.0000 192.6667];
|
||||
set(fig, 'Position', pos);
|
||||
|
||||
%%%%%%%%%%%%
|
||||
drawnow;
|
||||
|
||||
% Do EQ and find alpha's
|
||||
len_tr = 4096*2;
|
||||
|
||||
ffe_order = [50, 5, 5];
|
||||
dfe_order = [0, 0, 0];
|
||||
pf_ncoeffs = 1;
|
||||
mu_ffe = [0.0001, 0.0008, 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
mu_dc = 0.005;
|
||||
|
||||
%%% FULL RESP TARGET
|
||||
eq_ = EQ("Ne",ffe_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",0);
|
||||
pf_1 = Postfilter("ncoeff",1,"useBurg",1);
|
||||
|
||||
[eq_signal_sd, eq_noise] = eq_.process(Scpe_sig, Symbols);
|
||||
|
||||
% eq_noise.signal = eq_noise.signal - mean(eq_noise.signal);
|
||||
% eq_noise = eq_noise.normalize("mode","rms");
|
||||
|
||||
[mlse_sig_sd,whitened_noise] = pf_1.process(eq_signal_sd, eq_noise);
|
||||
|
||||
fig = figure(fignum+dbmode+10); hold on
|
||||
|
||||
[h, w] = freqz(1, pf_1.coefficients, length(eq_noise), "whole", eq_noise.fs);
|
||||
h = h / max(abs(h)); % Normalize the filter response
|
||||
w_ = (w - eq_noise.fs / 2);
|
||||
|
||||
%%% DB TARGET
|
||||
db_ref_sequence = Duobinary().encode(Symbols);
|
||||
eq_ = EQ("Ne",ffe_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",0);
|
||||
[eq_signal, db_noise] = eq_.process(Scpe_sig,db_ref_sequence);
|
||||
|
||||
% db_noise.signal = db_noise.signal - mean(db_noise.signal);
|
||||
% db_noise = db_noise.normalize("mode","rms");
|
||||
|
||||
%%% 1-3) Plot EQ Noise EEN
|
||||
figure(fignum+dbmode+10)
|
||||
eq_noise.spectrum("displayname", 'Noise', "fignum", fignum+dbmode+10, "normalizeTo0dB", 0,"color",clr.Set1.green,"normalizeToDC",0,"addDCoffset",0);
|
||||
if dbmode == 1
|
||||
offset = 27.7;
|
||||
else
|
||||
offset = 29.8;
|
||||
end
|
||||
plot(w_ * 1e-9, 20 * log10(fftshift(abs(h)))-offset, 'DisplayName', ['Burg Coeffs: ', num2str(round(pf_1.coefficients, 2)), ' '], 'LineWidth', 1,'Color',clr.Set1.green,'LineStyle','--');
|
||||
db_noise.spectrum("displayname", 'DBt. Noise', "fignum", fignum+dbmode+10, "normalizeTo0dB", 0,"color",clr.Set1.blue,"normalizeToDC",0,"addDCoffset",0);
|
||||
|
||||
ylim([-54,-25]);
|
||||
xlim([0,105]);
|
||||
xticks(0:20:110);
|
||||
yticks(-50:10:10);
|
||||
|
||||
beautifyBERplot("logscale",0,"setmarkers",0)
|
||||
pos = [100.3333 991.6667 358.0000 192.6667];
|
||||
set(fig, 'Position', pos);
|
||||
|
||||
end
|
||||
|
||||
|
||||
% === FINAL FIGURE SIZE ===
|
||||
@@ -0,0 +1,124 @@
|
||||
%% ============================================================
|
||||
% LOAD DATA
|
||||
% ============================================================
|
||||
database_type = 'mysql';
|
||||
db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
|
||||
|
||||
M = 4; % PAM level for this analysis
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', M);
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 10);
|
||||
fp.where('Runs', 'bitrate', 'EQUALS', 360e9);
|
||||
fp.where('Runs', 'is_mpi', 'EQUALS', 0);
|
||||
|
||||
fields = [
|
||||
db.getTableFieldNames('power_state_info');
|
||||
db.getTableFieldNames('dashboard_ungrouped_alltime')
|
||||
];
|
||||
|
||||
[dataTable, ~] = db.queryDB(fp, fields);
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% COMMON CONFIGURATION FOR ALL SUBPLOTS
|
||||
% ============================================================
|
||||
%% ============================================================
|
||||
% DEFINE DSP ALGORITHMS FOR THE 4 SUBPLOTS
|
||||
% ============================================================
|
||||
curves = struct;
|
||||
|
||||
curves(1).name = 'VNLE';
|
||||
curves(1).eq = equalizer_structure.vnle;
|
||||
curves(1).pre = 0;
|
||||
curves(1).color = clr.Paired.red;
|
||||
|
||||
curves(2).name = 'PF + MLSE';
|
||||
curves(2).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(2).pre = 0;
|
||||
curves(2).color = clr.Paired.green;
|
||||
|
||||
curves(3).name = 'DB-target + MLSE';
|
||||
curves(3).eq = equalizer_structure.vnle_db_mlse;
|
||||
curves(3).pre = 0;
|
||||
curves(3).color = clr.Paired.blue;
|
||||
|
||||
curves(4).name = 'ML-based MLSE';
|
||||
curves(4).eq = equalizer_structure.ml_mlse;
|
||||
if M == 4
|
||||
curves(4).pre = 0;
|
||||
else
|
||||
curves(4).pre = 1;
|
||||
end
|
||||
curves(4).color = clr.Paired.purple;
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% ANALYSIS ENGINE — NO PLOTTING
|
||||
% ============================================================
|
||||
results = struct;
|
||||
|
||||
for k = 1:numel(curves)
|
||||
|
||||
%% ---- BASE CONFIG ----
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'wavelength';
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.agg = 'min';
|
||||
cfg.outlier = 'none';
|
||||
% cfg.group_by = {'wavelength'};
|
||||
cfg.show_raw = false;
|
||||
|
||||
cfg.filters = struct( ...
|
||||
'pam_level', M, ...
|
||||
'is_mpi', 0, ...
|
||||
'bitrate', 360e9, ...
|
||||
'fiber_length', 10, ...
|
||||
'equalizer_structure', curves(k).eq, ...
|
||||
'pre_emph', curves(k).pre);
|
||||
|
||||
%% ---- GET BER ----
|
||||
cfg.y_axis = 'BER';
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
results(k).wavelength = A.group{1}.x;
|
||||
|
||||
if curves(k).eq == equalizer_structure.vnle_db_mlse || ...
|
||||
curves(k).eq == equalizer_structure.ml_mlse
|
||||
% DB and ML-based need precoded BER
|
||||
results(k).ber = A.group{1}.y_precoded;
|
||||
else
|
||||
results(k).ber = A.group{1}.y;
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
%% ============================================================
|
||||
% 1×4 TILED BER-vs-WAVELENGTH FIGURE
|
||||
% ============================================================
|
||||
fig=figure(901);
|
||||
tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
|
||||
|
||||
lw = 1.8; % line width
|
||||
ms = 6; % marker size
|
||||
|
||||
for k = 1:numel(curves)
|
||||
nexttile; hold on;
|
||||
|
||||
plot(results(k).wavelength, results(k).ber, ...
|
||||
'-o', ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerFaceColor', curves(k).color, ...
|
||||
'MarkerSize', ms, ...
|
||||
'LineWidth', lw);
|
||||
|
||||
set(gca,'YScale','log');
|
||||
grid on;
|
||||
xlabel('wavelength');
|
||||
ylabel('BER');
|
||||
title(curves(k).name);
|
||||
ylim([1e-4, 0.1])
|
||||
beautifyBERplot();
|
||||
end
|
||||
pos = 1e3.*[0.1070 0.5497 1.4113 0.3253];
|
||||
set(fig, 'Position', pos);
|
||||
@@ -0,0 +1,160 @@
|
||||
%% ============================================================
|
||||
% PARAMETERS
|
||||
% ============================================================
|
||||
database_type = 'mysql';
|
||||
db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
|
||||
|
||||
pam_level = 4; % FIXED for this figure
|
||||
baudrates = [300e9 330e9 360e9 390e9];
|
||||
fiberL = 10;
|
||||
|
||||
fields = [
|
||||
db.getTableFieldNames('power_state_info');
|
||||
db.getTableFieldNames('dashboard_ungrouped_alltime')
|
||||
];
|
||||
|
||||
%% ============================================================
|
||||
% DEFINE DSP SCHEMES
|
||||
% ============================================================
|
||||
curves = struct;
|
||||
|
||||
curves(1).name = 'VNLE';
|
||||
curves(1).eq = equalizer_structure.vnle;
|
||||
curves(1).color = clr.Paired.red;
|
||||
|
||||
curves(2).name = 'PF + MLSE';
|
||||
curves(2).eq = equalizer_structure.vnle_pf_mlse;
|
||||
curves(2).color = clr.Paired.green;
|
||||
|
||||
curves(3).name = 'DB-target + MLSE';
|
||||
curves(3).eq = equalizer_structure.vnle_db_mlse;
|
||||
curves(3).color = clr.Paired.blue;
|
||||
|
||||
curves(4).name = 'ML-based MLSE';
|
||||
curves(4).eq = equalizer_structure.ml_mlse;
|
||||
curves(4).color = clr.Paired.purple;
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% ANALYSIS — results(b, k): b = baudrate index, k = DSP scheme index
|
||||
% ============================================================
|
||||
results = struct;
|
||||
|
||||
for b = 1:length(baudrates)
|
||||
|
||||
Rb = baudrates(b);
|
||||
|
||||
% --- query matching runs ---
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','pam_level','EQUALS', pam_level);
|
||||
fp.where('Runs','fiber_length','EQUALS', fiberL);
|
||||
fp.where('Runs','bitrate','EQUALS', Rb);
|
||||
fp.where('Runs','is_mpi','EQUALS', 0);
|
||||
|
||||
[dataTable, ~] = db.queryDB(fp, fields);
|
||||
|
||||
for k = 1:numel(curves)
|
||||
|
||||
%% ---- DECIDE PRE-EMPH & PRECoded RULES for PAM-4 ----
|
||||
pre_emph = decide_preemph(pam_level, curves(k).eq);
|
||||
use_precoded = decide_precoded(pam_level, curves(k).eq);
|
||||
|
||||
%% ---- SETUP ANALYSIS CONFIG ----
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'wavelength';
|
||||
cfg.y_axis = 'BER';
|
||||
cfg.agg = 'min';
|
||||
cfg.outlier = 'none';
|
||||
% cfg.group_by = {'wavelength'};
|
||||
cfg.show_raw = false;
|
||||
|
||||
cfg.filters = struct( ...
|
||||
'pam_level', pam_level, ...
|
||||
'is_mpi', 0, ...
|
||||
'bitrate', Rb, ...
|
||||
'fiber_length', fiberL, ...
|
||||
'equalizer_structure', curves(k).eq, ...
|
||||
'pre_emph', pre_emph);
|
||||
|
||||
%% ---- RUN ANALYSIS ----
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
results(b,k).wavelength = A.group{1}.x;
|
||||
|
||||
if use_precoded
|
||||
results(b,k).ber = A.group{1}.y_precoded;
|
||||
else
|
||||
results(b,k).ber = A.group{1}.y;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% PLOT — 1×4 (one tile per baudrate)
|
||||
% ============================================================
|
||||
fig = figure(); clf;
|
||||
tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
|
||||
|
||||
lw = 1.8;
|
||||
ms = 6;
|
||||
|
||||
for b = 1:length(baudrates)
|
||||
nexttile; hold on;
|
||||
|
||||
for k = 1:numel(curves)
|
||||
plot(results(b,k).wavelength, results(b,k).ber, ...
|
||||
'-o', ...
|
||||
'Color', curves(k).color, ...
|
||||
'MarkerFaceColor', curves(k).color, ...
|
||||
'MarkerSize', ms, ...
|
||||
'LineWidth', lw, ...
|
||||
'DisplayName', curves(k).name);
|
||||
end
|
||||
|
||||
set(gca,'YScale','log');
|
||||
grid on;
|
||||
xlabel('Wavelength [nm]');
|
||||
ylabel('BER');
|
||||
ylim([1e-4 0.1]);
|
||||
title(sprintf('PAM-%d @ %.0f GBd',pam_level, baudrates(b)/1e9));
|
||||
legend('Location','best');
|
||||
beautifyBERplot();
|
||||
end
|
||||
|
||||
% Optional figure size
|
||||
pos = 1e3.*[0.1 0.55 1.4 0.32];
|
||||
set(fig, 'Position', pos);
|
||||
|
||||
|
||||
%% ============================================================
|
||||
% DECISION LOGIC (INLINE FUNCTIONS)
|
||||
% ============================================================
|
||||
|
||||
function pe = decide_preemph(M, eq)
|
||||
% PRE-EMPH RULES:
|
||||
switch M
|
||||
case 4
|
||||
if eq == equalizer_structure.vnle
|
||||
pe = 1; % PAM4: VNLE → pre-emph on
|
||||
else
|
||||
pe = 0; % PAM4: all others → off
|
||||
end
|
||||
case {6,8}
|
||||
pe = 1; % PAM6/8: all → pre-emph on
|
||||
otherwise
|
||||
pe = 0;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function flag = decide_precoded(M, eq)
|
||||
% PRE-CODE RULES:
|
||||
if eq == equalizer_structure.vnle_db_mlse
|
||||
flag = 1; % Always for DB-target
|
||||
elseif eq == equalizer_structure.ml_mlse && M == 4
|
||||
flag = 1; % PAM4: ML-based → precoded
|
||||
else
|
||||
flag = 0;
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,88 @@
|
||||
%% ============================================================
|
||||
% PLOT
|
||||
% ============================================================
|
||||
figure; hold on;
|
||||
ms = 32; % scatter size
|
||||
lw = 0.8; % line width
|
||||
|
||||
for k = 1:4 % PAM-2/4/6/8
|
||||
|
||||
M = pam_list(k);
|
||||
idxPam = (Mvals == M);
|
||||
|
||||
% Extract for this PAM
|
||||
x = baud(idxPam);
|
||||
y = netrate(idxPam);
|
||||
n = names(idxPam);
|
||||
|
||||
% Get color for this PAM format
|
||||
col = colors(k,:);
|
||||
|
||||
% ----- LEGEND FLAG (only add one entry per PAM) -----
|
||||
firstLegend = true;
|
||||
|
||||
% ---- PLOT ALL POINTS (marker based on publication) ----
|
||||
for i = 1:sum(idxPam)
|
||||
|
||||
% marker selection by publication
|
||||
pubIdx = find(pub_list == n(i), 1);
|
||||
marker = markerlist{mod(pubIdx-1, nMarkers) + 1};
|
||||
|
||||
if firstLegend
|
||||
h = scatter(x(i), y(i), ms, ...
|
||||
'Marker', marker, ...
|
||||
'MarkerEdgeColor', col, ...
|
||||
'MarkerFaceColor', col, ...
|
||||
'DisplayName', sprintf('PAM-%d', M));
|
||||
firstLegend = false;
|
||||
else
|
||||
h = scatter(x(i), y(i), ms, ...
|
||||
'Marker', marker, ...
|
||||
'MarkerEdgeColor', col, ...
|
||||
'MarkerFaceColor', col, ...
|
||||
'HandleVisibility','off');
|
||||
end
|
||||
|
||||
% ====== CUSTOM DATATIP CONTENT ======
|
||||
dt = h.DataTipTemplate;
|
||||
dt.DataTipRows(1).Label = 'Baud rate';
|
||||
dt.DataTipRows(2).Label = 'Net rate';
|
||||
|
||||
% Add publication name
|
||||
dt.DataTipRows(end+1) = dataTipTextRow('Publication', n(i));
|
||||
|
||||
|
||||
end
|
||||
|
||||
% ---- Fit (PAM-specific) ----
|
||||
valid = ~isnan(x) & ~isnan(y);
|
||||
if sum(valid) >= 3
|
||||
p = polyfit(x(valid), y(valid), 2);
|
||||
xfit = linspace(min(x(valid)), max(x(valid)), 200);
|
||||
yfit = polyval(p, xfit);
|
||||
|
||||
plot(xfit, yfit, ':', ...
|
||||
'LineWidth', lw, ...
|
||||
'Color', col, ...
|
||||
'HandleVisibility', 'off'); % do NOT add to legend
|
||||
end
|
||||
end
|
||||
|
||||
grid on;
|
||||
xlabel('Baud rate [GBd]');
|
||||
ylabel('Net rate [Gb/s]');
|
||||
|
||||
legend('Location','northwest');
|
||||
set(gca,'FontSize',11);
|
||||
|
||||
|
||||
%% === EXPORT ===
|
||||
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\highspeedresults.tikz';
|
||||
matlab2tikz(outfile, ...
|
||||
'width','\fwidth', ...
|
||||
'height','\fheight', ...
|
||||
'showInfo',false, ...
|
||||
'extraAxisOptions',{ ...
|
||||
'legend style={font=\footnotesize}', ...
|
||||
'legend columns=1' ...
|
||||
});
|
||||
Binary file not shown.
@@ -0,0 +1,334 @@
|
||||
function [M, cfg] = analyze_measurements_gpt(T, cfg)
|
||||
% ANALYZE_MEASUREMENTS_GPT
|
||||
% Filter, compute X/Y, group and aggregate measurements from table T.
|
||||
% No plotting here.
|
||||
%
|
||||
% Usage:
|
||||
% [M, cfg] = analyze_measurements_gpt(dataTable, cfg);
|
||||
%
|
||||
% Typical result (single group):
|
||||
% M.x -> aggregated x-values (e.g., grossrate)
|
||||
% M.y -> aggregated y-values (e.g., BER or NGMI)
|
||||
% M.y_precoded -> aggregated precoded BER (if available)
|
||||
%
|
||||
% For multiple groups:
|
||||
% M.group(g).x, M.group(g).y, M.group(g).label, ...
|
||||
|
||||
%% ---- Defaults (non-plot) ----
|
||||
if nargin < 2, cfg = struct; end
|
||||
defaults = struct( ...
|
||||
'x_axis' , 'symbolrate', ...
|
||||
'y_axis' , 'BER', ...
|
||||
'y_scale' , 'auto', ...
|
||||
'group_by' , {{'equalizer_structure','pre_emph'}}, ...
|
||||
'filters' , struct, ...
|
||||
'agg' , 'mean', ...
|
||||
'outlier' , 'auto', ...
|
||||
'mad_z' , 4, ...
|
||||
'pct_limits' , [2.5 97.5], ...
|
||||
'min_pts_x' , 3, ...
|
||||
'show_raw' , true, ...
|
||||
'show_precoded', [], ...
|
||||
'show_spread' , 'none', ...
|
||||
'fec_lines' , [], ...
|
||||
'plot' , struct() ... % plot settings handled in plot function
|
||||
);
|
||||
cfg = filldefaults(cfg, defaults);
|
||||
|
||||
%% ---- Derived/prep columns ----
|
||||
if ~ismember('pre_emph', T.Properties.VariableNames)
|
||||
if ~ismember('db_mode', T.Properties.VariableNames)
|
||||
error('Missing column "db_mode" for pre_emph derivation.');
|
||||
end
|
||||
T.pre_emph = T.db_mode == 0;
|
||||
end
|
||||
|
||||
if ~ismember(cfg.y_axis, T.Properties.VariableNames)
|
||||
error('y_axis "%s" not found in table.', cfg.y_axis);
|
||||
end
|
||||
|
||||
isBER = startsWith(cfg.y_axis, "BER", 'IgnoreCase', true);
|
||||
M.isBER = isBER;
|
||||
|
||||
if strcmpi(cfg.y_scale,'auto')
|
||||
cfg.y_scale = tern(isBER, 'log', 'linear');
|
||||
end
|
||||
if strcmpi(cfg.outlier,'auto')
|
||||
cfg.outlier = tern(isBER, 'mad', 'none');
|
||||
end
|
||||
if isempty(cfg.show_precoded)
|
||||
cfg.show_precoded = isBER && ismember('BER_precoded', T.Properties.VariableNames);
|
||||
end
|
||||
|
||||
%% ---- Filters & core X/Y extraction ----
|
||||
T = applyFilters(T, cfg.filters);
|
||||
|
||||
[x_raw, x_label] = computeX(T, cfg.x_axis);
|
||||
y_raw = T.(cfg.y_axis);
|
||||
|
||||
validXY = isfinite(x_raw) & isfinite(y_raw);
|
||||
T = T(validXY, :);
|
||||
x_raw = x_raw(validXY);
|
||||
y_raw = y_raw(validXY);
|
||||
|
||||
% Degiga if needed
|
||||
if mean(abs(y_raw)) > 1e8
|
||||
y_raw = y_raw .* 1e-9;
|
||||
end
|
||||
|
||||
if cfg.show_precoded && ismember('BER_precoded', T.Properties.VariableNames)
|
||||
y_raw_p = T.BER_precoded(validXY);
|
||||
else
|
||||
y_raw_p = [];
|
||||
end
|
||||
|
||||
%% ---- Grouping ----
|
||||
group_by = cfg.group_by;
|
||||
if ~all(ismember(group_by, T.Properties.VariableNames))
|
||||
error('Some group_by columns are missing in table.');
|
||||
end
|
||||
[G, grpTbl] = findgroups(T(:, group_by));
|
||||
nG = max(G);
|
||||
|
||||
%% ---- Aggregation per group ----
|
||||
M = struct;
|
||||
M.cfg = cfg;
|
||||
M.x_label = x_label;
|
||||
M.y_axis = cfg.y_axis;
|
||||
M.x_axis = cfg.x_axis;
|
||||
M.nGroups = nG;
|
||||
|
||||
% raw (filtered) data
|
||||
M.raw = struct;
|
||||
M.raw.x = x_raw;
|
||||
M.raw.y = y_raw;
|
||||
M.raw.y_precoded = y_raw_p;
|
||||
M.raw.T = T;
|
||||
|
||||
M.group = cell(nG,1);
|
||||
|
||||
useLog = strcmpi(cfg.y_scale,'log');
|
||||
for gi = 1:nG
|
||||
idx = (G == gi);
|
||||
Ti = T(idx,:);
|
||||
xi = x_raw(idx);
|
||||
yi = y_raw(idx);
|
||||
|
||||
[xu, ~, iu] = unique(xi);
|
||||
yu = nan(size(xu));
|
||||
ylo = nan(size(xu));
|
||||
yhi = nan(size(xu));
|
||||
|
||||
for k = 1:numel(xu)
|
||||
bin = (iu==k);
|
||||
yy = yi(bin);
|
||||
yy = yy(isfinite(yy));
|
||||
if isempty(yy), continue; end
|
||||
|
||||
km = outlierMask(yy, cfg, useLog);
|
||||
if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
|
||||
yy = yy(km);
|
||||
|
||||
switch lower(cfg.agg)
|
||||
case 'median'
|
||||
yu(k) = median(yy,'omitnan');
|
||||
case 'mean'
|
||||
yu(k) = mean(yy,'omitnan');
|
||||
case 'min'
|
||||
yu(k) = min(yy);
|
||||
case 'max'
|
||||
yu(k) = max(yy);
|
||||
otherwise
|
||||
error('Unknown agg mode "%s".', cfg.agg);
|
||||
end
|
||||
|
||||
if strcmpi(cfg.show_spread,'iqr')
|
||||
q = prctile(yy,[25 75]);
|
||||
ylo(k) = max(yu(k)-q(1), eps);
|
||||
yhi(k) = max(q(2)-yu(k), eps);
|
||||
elseif strcmpi(cfg.show_spread,'minmax')
|
||||
ylo(k) = min(yy);
|
||||
yhi(k) = max(yy);
|
||||
end
|
||||
end
|
||||
|
||||
% sort by x
|
||||
[xu, ord] = sort(xu);
|
||||
yu = yu(ord);
|
||||
ylo = ylo(ord);
|
||||
yhi = yhi(ord);
|
||||
|
||||
g = struct;
|
||||
g.label = buildLabel(grpTbl(gi,:), group_by);
|
||||
g.idx = find(G==gi);
|
||||
g.T = Ti;
|
||||
g.x_raw = xi;
|
||||
g.y_raw = yi;
|
||||
g.x = xu;
|
||||
g.y = yu;
|
||||
g.y_lo = ylo;
|
||||
g.y_hi = yhi;
|
||||
g.y_precoded = [];
|
||||
g.y_precoded_lo = [];
|
||||
g.y_precoded_hi = [];
|
||||
|
||||
% Precoded aggregation (if requested & available)
|
||||
if cfg.show_precoded && ~isempty(y_raw_p) && strcmpi(cfg.y_axis,'BER')
|
||||
ypi = y_raw_p(idx);
|
||||
ypu = nan(size(xu));
|
||||
|
||||
for k = 1:numel(xu)
|
||||
bin = (iu==k);
|
||||
yy = ypi(bin);
|
||||
yy = yy(isfinite(yy));
|
||||
if isempty(yy), continue; end
|
||||
|
||||
km = outlierMask(yy, cfg, true);
|
||||
if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
|
||||
yy = yy(km);
|
||||
|
||||
switch lower(cfg.agg)
|
||||
case 'median'
|
||||
ypu(k) = median(yy,'omitnan');
|
||||
case 'mean'
|
||||
ypu(k) = mean(yy,'omitnan');
|
||||
case 'min'
|
||||
ypu(k) = min(yy);
|
||||
case 'max'
|
||||
ypu(k) = max(yy);
|
||||
end
|
||||
end
|
||||
|
||||
g.y_precoded = ypu(ord);
|
||||
end
|
||||
|
||||
M.group{gi} = g;
|
||||
end
|
||||
|
||||
% Convenience flatten for single-group case
|
||||
if nG == 1
|
||||
g = M.group{1};
|
||||
M.x = g.x;
|
||||
M.y = g.y;
|
||||
M.y_precoded = g.y_precoded;
|
||||
end
|
||||
|
||||
end % ===== main =====
|
||||
|
||||
|
||||
%% ===================== Helpers =====================
|
||||
|
||||
function cfg = filldefaults(cfg, defs)
|
||||
fn = fieldnames(defs);
|
||||
for i = 1:numel(fn)
|
||||
f = fn{i};
|
||||
if ~isfield(cfg, f) || isempty(cfg.(f))
|
||||
cfg.(f) = defs.(f);
|
||||
elseif isstruct(defs.(f)) && isstruct(cfg.(f))
|
||||
cfg.(f) = filldefaults(cfg.(f), defs.(f)); % recursive
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function out = tern(cond, a, b)
|
||||
if cond
|
||||
out = a;
|
||||
else
|
||||
out = b;
|
||||
end
|
||||
end
|
||||
|
||||
function T2 = applyFilters(T, filters)
|
||||
if isempty(filters), T2 = T; return; end
|
||||
keep = true(height(T),1);
|
||||
fns = fieldnames(filters);
|
||||
for i = 1:numel(fns)
|
||||
name = fns{i};
|
||||
if ~ismember(name, T.Properties.VariableNames)
|
||||
warning('Filter column "%s" not found. Ignored.', name);
|
||||
continue
|
||||
end
|
||||
val = filters.(name);
|
||||
col = T.(name);
|
||||
if isa(val,'function_handle')
|
||||
m = val(col);
|
||||
if ~islogical(m) || ~isequal(size(m), size(col))
|
||||
error('Filter for %s must return logical mask of same size.', name);
|
||||
end
|
||||
keep = keep & m;
|
||||
else
|
||||
keep = keep & ismember(col, val);
|
||||
end
|
||||
end
|
||||
T2 = T(keep,:);
|
||||
end
|
||||
|
||||
function [x, label] = computeX(T, whichX)
|
||||
switch lower(whichX)
|
||||
case {'symbolrate','baudrate'}
|
||||
x = T.symbolrate * 1e-9;
|
||||
label = 'Symbol rate [GBd]';
|
||||
case 'bitrate'
|
||||
if ~ismember('pam_level', T.Properties.VariableNames)
|
||||
error('bitrate requires "pam_level" column.');
|
||||
end
|
||||
bits = floor(log2(double(T.pam_level))*10)/10;
|
||||
x = (T.symbolrate .* bits) * 1e-9;
|
||||
label = 'Grossrate [Gb/s]';
|
||||
case 'grossrate'
|
||||
x = T.grossrate * 1e-9;
|
||||
label = 'Grossrate [Gb/s]';
|
||||
otherwise
|
||||
if ~ismember(whichX, T.Properties.VariableNames)
|
||||
error('x_axis "%s" not found in table.', whichX);
|
||||
end
|
||||
x = T.(whichX);
|
||||
label = whichX;
|
||||
end
|
||||
x = double(x(:));
|
||||
end
|
||||
|
||||
function keep = outlierMask(y, cfg, useLog)
|
||||
if isempty(y), keep = false(size(y)); return; end
|
||||
y = y(:);
|
||||
switch lower(cfg.outlier)
|
||||
case 'none'
|
||||
keep = true(size(y)); return
|
||||
case 'mad'
|
||||
z = tern(useLog, log10(y), y);
|
||||
med = median(z,'omitnan');
|
||||
madv = median(abs(z-med),'omitnan');
|
||||
if ~(isfinite(madv) && madv>0)
|
||||
keep = true(size(y)); return
|
||||
end
|
||||
sigma = 1.4826*madv;
|
||||
zz = tern(useLog, log10(y), y);
|
||||
keep = abs(zz - med) <= cfg.mad_z*sigma;
|
||||
case 'pctl'
|
||||
pr = prctile(y, cfg.pct_limits);
|
||||
keep = (y >= pr(1)) & (y <= pr(2));
|
||||
otherwise
|
||||
error('Unknown outlier mode "%s".', cfg.outlier);
|
||||
end
|
||||
end
|
||||
|
||||
function s = buildLabel(grpRow, group_by)
|
||||
parts = strings(1, numel(group_by));
|
||||
for i = 1:numel(group_by)
|
||||
key = group_by{i};
|
||||
val = grpRow.(key);
|
||||
if iscell(val), val = val{1}; end
|
||||
if islogical(val), val = tern(val,'w/','w/o'); end
|
||||
if key == "equalizer_structure"
|
||||
key = '';
|
||||
val = upper(val);
|
||||
val = strrep(val,'_',' ');
|
||||
end
|
||||
if key == "pre_emph"
|
||||
val = [val, ' pre-emph.'];
|
||||
key = '';
|
||||
end
|
||||
parts(i) = sprintf('%s %s', key, string(val));
|
||||
end
|
||||
s = strjoin(parts, ', ');
|
||||
end
|
||||
@@ -0,0 +1,240 @@
|
||||
|
||||
|
||||
database_type = 'mysql';
|
||||
dataBase = 'labor_highspeed';
|
||||
db = DBHandler("dataBase", [dataBase], "type", database_type);
|
||||
|
||||
|
||||
% M = 4;
|
||||
fp = QueryFilter();
|
||||
% fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength','LESS_THAN', 1312);
|
||||
% fp.where('Runs', 'db_mode','EQUALS', 1); % 0 == high preemphasis // 1 == low preemphasis
|
||||
fp.where('Runs', 'rop_attenuation','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('power_state_info');
|
||||
fields = [fields; db.getTableFieldNames('dashboard_ungrouped_alltime')]; %dashboard_ungrouped_after_nov_2025 dashboard_ungrouped_aug_nov_2025
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
%%
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'grossrate'; % 'symbol rate' | 'bitrate' | 'wavelength' grossrate
|
||||
cfg.y_axis = 'BER'; % 'BER' | 'GMI' | 'AIR' | ...
|
||||
|
||||
cfg.y_scale = 'auto'; % auto -> log for BER*, linear otherwise
|
||||
cfg.outlier = 'mad'; % simple, robust; 'none' or 'pctl' also available
|
||||
cfg.show_raw = false;
|
||||
cfg.show_spread = 'none'; % 'none' or 'iqr' or minmax
|
||||
cfg.agg = 'min'; % or 'median'
|
||||
cfg.show_precoded = 0;
|
||||
|
||||
% cfg.fec_lines = [2.2e-4 4.85e-3 2e-2]; % optional
|
||||
cfg.fec_lines = [];
|
||||
cfg.plot.custom_colors = [
|
||||
clr.Paired.red;
|
||||
clr.Paired.blue;
|
||||
clr.Paired.green;
|
||||
clr.Paired.orange;
|
||||
clr.Paired.purple
|
||||
];
|
||||
|
||||
cfg.plot.custom_colors_scatter = [
|
||||
clr.Paired.lightred;
|
||||
clr.Paired.lightblue;
|
||||
clr.Paired.lightgreen;
|
||||
clr.Paired.lightorange;
|
||||
clr.Paired.lightpurple
|
||||
];
|
||||
|
||||
|
||||
% New styling knobs
|
||||
cfg.plot.use_cbrewer2 = true;
|
||||
cfg.plot.colormap = 'Paired';
|
||||
cfg.plot.paired_dark_first = false; % dark for lines, light for scatter
|
||||
cfg.plot.lineWidth = 2.0;
|
||||
cfg.plot.errWidth = 1.2;
|
||||
cfg.plot.scatterAlpha = 0.35;
|
||||
cfg.plot.legendLocation = 'best';
|
||||
cfg.plot.fecLineWidth = 2.4; % thicker FEC limits
|
||||
cfg.plot.lineStyle_pre_emph_on = '-';
|
||||
cfg.plot.lineStyle_pre_emph_off = '-';
|
||||
|
||||
%% PLOT NGMI
|
||||
|
||||
% Common cfg
|
||||
cfg.show_precoded = 1;
|
||||
cfg.group_by = {'equalizer_structure','pre_emph'};
|
||||
cfg.x_axis = 'grossrate';
|
||||
cfg.y_axis = 'NGMI'; % 'BER' | 'GMI' | 'AIR' | ...
|
||||
cfg.y_scale = 'lin';
|
||||
cfg.plot.custom_colors_scatter = [];
|
||||
cfg.plot.use_cbrewer2 = false;
|
||||
cfg.fec_lines = [];
|
||||
cfg.agg = 'max';
|
||||
|
||||
cfg.figure_number = 45;
|
||||
% fig = figure(cfg.figure_number);
|
||||
|
||||
lambda = 1310;
|
||||
% PAM 4
|
||||
cfg.plot.custom_colors = clr.Paired.red;
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',4, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_db_mlse, ...
|
||||
'pre_emph',0,'wavelength',lambda);
|
||||
cfg.show_precoded = 1;
|
||||
a = plot_measurements_gpt(dataTable, cfg);
|
||||
ngmi_pam4 = a.lines(1).YData;
|
||||
grossrates = a.lines(1).XData;
|
||||
tp = TransmissionPerformance;
|
||||
netrates_vnle = tp.calculateNetRate(grossrates, ...
|
||||
'NGMI', ngmi_pam4, ...
|
||||
'BER', BER_VNLE);
|
||||
|
||||
cfg.plot.custom_colors = clr.Paired.red;
|
||||
cfg.plot.custom_linetypes = {'--'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',4, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_db_mlse, ...
|
||||
'pre_emph',0,'wavelength',1293);
|
||||
cfg.show_precoded = 1;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% PAM 6
|
||||
cfg.plot.custom_colors = clr.Paired.blue;
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',6, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',1,'wavelength',lambda);
|
||||
cfg.show_precoded = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
cfg.plot.custom_colors = clr.Paired.blue;
|
||||
cfg.plot.custom_linetypes = {'--'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',6, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',1,'wavelength',1293);
|
||||
cfg.show_precoded = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% PAM 8
|
||||
cfg.plot.custom_colors = clr.Paired.green;
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',8, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',1,'wavelength',lambda);
|
||||
cfg.show_precoded = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
cfg.plot.custom_colors = clr.Paired.green;
|
||||
cfg.plot.custom_linetypes = {'--'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',8, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',1,'wavelength',1293);
|
||||
cfg.show_precoded = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
beautifyBERplot
|
||||
ylim([0.87,1.01]);
|
||||
% xlim([290,480]);
|
||||
|
||||
%% PLOT AIR
|
||||
|
||||
% Common cfg
|
||||
cfg.show_precoded = 1;
|
||||
cfg.group_by = {'equalizer_structure','pre_emph'};
|
||||
cfg.x_axis = 'grossrate';
|
||||
cfg.y_axis = 'AIR'; % 'BER' | 'GMI' | 'AIR' | ...
|
||||
cfg.y_scale = 'lin';
|
||||
cfg.plot.custom_colors_scatter = [];
|
||||
cfg.plot.use_cbrewer2 = false;
|
||||
cfg.fec_lines = [];
|
||||
cfg.agg = 'max';
|
||||
|
||||
cfg.figure_number = 47;
|
||||
|
||||
lambda = 1310;
|
||||
|
||||
% cfg = struct;
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',4, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_db_mlse, ...
|
||||
'pre_emph',0,'wavelength',lambda);
|
||||
cfg.x_axis = 'grossrate';
|
||||
cfg.y_axis = 'NGMI'; % or 'NGMI', etc.
|
||||
cfg.show_precoded = 1;
|
||||
|
||||
[M, cfg] = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
grossrates = M.x; % aggregated X
|
||||
ber = M.y; % aggregated Y (BER or NGMI)
|
||||
ber_prec = M.y_precoded; % precoded BER (if available)
|
||||
|
||||
[h, M] = plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
|
||||
% PAM 4
|
||||
cfg.plot.custom_colors = clr.Paired.red;
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',4, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_db_mlse, ...
|
||||
'pre_emph',0,'wavelength',lambda);
|
||||
cfg.show_precoded = 1;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
|
||||
|
||||
cfg.plot.custom_colors = clr.Paired.red;
|
||||
cfg.plot.custom_linetypes = {'--'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',4, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_db_mlse, ...
|
||||
'pre_emph',0,'wavelength',1293);
|
||||
cfg.show_precoded = 1;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% PAM 6
|
||||
cfg.plot.custom_colors = clr.Paired.blue;
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',6, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',1,'wavelength',lambda);
|
||||
cfg.show_precoded = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
cfg.plot.custom_colors = clr.Paired.blue;
|
||||
cfg.plot.custom_linetypes = {'--'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',6, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',1,'wavelength',1293);
|
||||
cfg.show_precoded = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% PAM 8
|
||||
cfg.plot.custom_colors = clr.Paired.green;
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',8, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',1,'wavelength',lambda);
|
||||
cfg.show_precoded = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
cfg.plot.custom_colors = clr.Paired.green;
|
||||
cfg.plot.custom_linetypes = {'--'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',8, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',1,'wavelength',1293);
|
||||
cfg.show_precoded = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
ax = gca;
|
||||
|
||||
beautifyBERplot
|
||||
|
||||
ylim([275,435]);
|
||||
xlim([290,480]);
|
||||
|
||||
|
||||
%%
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,112 @@
|
||||
database_type = 'mysql';
|
||||
dataBase = 'labor_highspeed';
|
||||
db = DBHandler("dataBase", [dataBase], "type", database_type);
|
||||
|
||||
|
||||
M = 4;
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
% fp.where('Runs', 'db_mode','EQUALS', 1); % 0 == high preemphasis // 1 == low preemphasis
|
||||
fp.where('Runs', 'rop_attenuation','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('power_state_info');
|
||||
fields = [fields; db.getTableFieldNames('dashboard_ungrouped_aug_nov_2025')]; %dashboard_ungrouped_after_nov_2025 dashboard_ungrouped_aug_nov_2025
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
%%
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'grossrate'; % 'symbol rate' | 'bitrate' | 'wavelength' grossrate
|
||||
cfg.y_axis = 'BER'; % 'BER' | 'GMI' | 'AIR' | ...
|
||||
|
||||
cfg.y_scale = 'auto'; % auto -> log for BER*, linear otherwise
|
||||
cfg.outlier = 'mad'; % simple, robust; 'none' or 'pctl' also available
|
||||
cfg.show_raw = false;
|
||||
cfg.show_spread = 'none'; % 'none' or 'iqr' or minmax
|
||||
cfg.agg = 'min'; % or 'median'
|
||||
cfg.show_precoded = 0;
|
||||
% cfg.fec_lines = [2.2e-4 4.85e-3 2e-2]; % optional
|
||||
cfg.fec_lines = [];
|
||||
cfg.plot.custom_colors = [
|
||||
clr.Paired.red;
|
||||
clr.Paired.blue;
|
||||
clr.Paired.green;
|
||||
clr.Paired.orange;
|
||||
clr.Paired.purple
|
||||
];
|
||||
|
||||
cfg.plot.custom_colors_scatter = [
|
||||
clr.Paired.lightred;
|
||||
clr.Paired.lightblue;
|
||||
clr.Paired.lightgreen;
|
||||
clr.Paired.lightorange;
|
||||
clr.Paired.lightpurple
|
||||
];
|
||||
|
||||
|
||||
% New styling knobs
|
||||
cfg.plot.use_cbrewer2 = true;
|
||||
cfg.plot.colormap = 'Paired';
|
||||
cfg.plot.paired_dark_first = false; % dark for lines, light for scatter
|
||||
cfg.plot.lineWidth = 2.0;
|
||||
cfg.plot.errWidth = 1.2;
|
||||
cfg.plot.scatterAlpha = 0.35;
|
||||
cfg.plot.legendLocation = 'best';
|
||||
cfg.plot.fecLineWidth = 2.4; % thicker FEC limits
|
||||
cfg.plot.lineStyle_pre_emph_on = '-';
|
||||
cfg.plot.lineStyle_pre_emph_off = '-';
|
||||
|
||||
%%
|
||||
cfg.figure_number = 42;
|
||||
|
||||
|
||||
% ---- VNLE, no pre-emph (solid red) ----
|
||||
cfg.plot.custom_colors = [clr.Paired.red];
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',M, ...
|
||||
'equalizer_structure',equalizer_structure.vnle, ...
|
||||
'pre_emph',0);
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% ---- VNLE, with pre-emph (dashed red) ----
|
||||
cfg.plot.custom_colors = [clr.Paired.red];
|
||||
cfg.plot.custom_linetypes = {'--'};
|
||||
cfg.filters.pre_emph = 1;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% ---- VNLE PF MLSE, no pre-emph (solid green) ----
|
||||
cfg.plot.custom_colors = [clr.Paired.green];
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.filters.equalizer_structure = equalizer_structure.vnle_pf_mlse;
|
||||
cfg.filters.pre_emph = 0;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% ---- VNLE PF MLSE, with pre-emph (dashed green) ----
|
||||
cfg.plot.custom_colors = [clr.Paired.green];
|
||||
cfg.plot.custom_linetypes = {'--'};
|
||||
cfg.filters.pre_emph = 1;
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
|
||||
% === FEC LINES (no legend) ===
|
||||
yline([2.2e-4 4.85e-3 2e-2], ...
|
||||
'LineWidth',1.5,'Color',[0.4 0.4 0.4], ...
|
||||
'LineStyle',':','HandleVisibility','off');
|
||||
|
||||
|
||||
% === BEAUTIFY ===
|
||||
% beautifyBERplot; % your function
|
||||
|
||||
|
||||
%% === EXPORT TO TIKZ ===
|
||||
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_pre_emphasis.tikz';
|
||||
|
||||
matlab2tikz(outfile, ...
|
||||
'width','\fwidth', ...
|
||||
'height','\fheight', ...
|
||||
'showInfo',false, ...
|
||||
'extraAxisOptions',{ ...
|
||||
'legend style={font=\footnotesize}', ...
|
||||
'legend columns=1' ...
|
||||
} );
|
||||
@@ -0,0 +1,92 @@
|
||||
% ============================================================
|
||||
% MINIMAL EXAMPLE: Query → Analyze → Plot → Extract X/Y data
|
||||
% ============================================================
|
||||
|
||||
%% === Load from database ===
|
||||
db = DBHandler("dataBase","labor_highspeed","type","mysql");
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','fiber_length','EQUALS',2);
|
||||
% fp.where('Runs','pam_level','EQUALS',6); % PAM-4
|
||||
% fp.where('Runs','db_mode','EQUALS',0); % w/o pre-emph
|
||||
|
||||
fields = db.getTableFieldNames('dashboard_ungrouped_alltime');
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
%% === Define config ===
|
||||
|
||||
for m = [4,6,8]
|
||||
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'symbolrate';
|
||||
cfg.y_axis = 'Alpha';
|
||||
cfg.group_by = {'equalizer_structure','pre_emph'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',m, ...
|
||||
'equalizer_structure',equalizer_structure.vnle_pf_mlse, ...
|
||||
'pre_emph',0);
|
||||
cfg.agg = 'max';
|
||||
cfg.outlier = 'mad';
|
||||
cfg.show_raw = false;
|
||||
cfg.show_precoded = 0;
|
||||
|
||||
% Plot cosmetics (minimal)
|
||||
cfg.plot = struct;
|
||||
cfg.plot.custom_colors = linspecer(8);
|
||||
cfg.plot.custom_linetypes = {'-'};
|
||||
cfg.plot.lineWidth = 2;
|
||||
|
||||
|
||||
% ============================================================
|
||||
% === ANALYSIS ONLY (no plotting) =============================
|
||||
% ============================================================
|
||||
A = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% Now you have:
|
||||
% A.raw.x = raw x-values
|
||||
% A.raw.y = raw BER values
|
||||
% A.group{1}.x = unique sorted x-values
|
||||
% A.group{1}.y = aggregated BER for each x
|
||||
|
||||
x_values = A.group{1}.x;
|
||||
y_values = A.group{1}.y;
|
||||
|
||||
|
||||
% ============================================================
|
||||
% === PLOT ====================================================
|
||||
% ============================================================
|
||||
|
||||
figure(10);hold on
|
||||
cfg.ax = gca; % optional: plot into existing axes
|
||||
plot(x_values,y_values,...
|
||||
'LineWidth', 2, ...
|
||||
'Color', clr.Set1.red, ...
|
||||
'MarkerSize', 5, ...
|
||||
'MarkerFaceColor', clr.Set1.red,...
|
||||
'Marker','o');
|
||||
% [h, ~] = plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
title('Minimal VNLE BER Example')
|
||||
xlabel('Grossrate [Gb/s]')
|
||||
ylabel('BER')
|
||||
xticks(100:30:220)
|
||||
xlim([100,220]);
|
||||
ylim([0,1]);
|
||||
|
||||
end
|
||||
% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\alphas.tikz';
|
||||
% matlab2tikz(outfile, ...
|
||||
% 'width','\fwidth', ...
|
||||
% 'height','\fheight', ...
|
||||
% 'showInfo',false, ...
|
||||
% 'extraAxisOptions',{ ...
|
||||
% 'legend style={font=\footnotesize}', ...
|
||||
% 'legend columns=1' ...
|
||||
% 'every axis/.append style={font=\scriptsize}',...
|
||||
% 'minor grid style={line width=0.2pt, solid, color=black!10}',...
|
||||
% 'grid style={line width=0.4pt, solid, color=black!20}',...
|
||||
% 'grid style={dashed}',...
|
||||
% });
|
||||
@@ -0,0 +1,111 @@
|
||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||
dsp_options.max_occurences = 1;
|
||||
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
|
||||
|
||||
rate = 390e9;
|
||||
|
||||
%% 1 - PAM 4 with preemphasis
|
||||
fp = QueryFilter();
|
||||
M = 4;
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
fp.where('Runs', 'db_mode','EQUALS', 0);
|
||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||
|
||||
[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
|
||||
|
||||
dataTable = queryRunid(dataTable.run_id, database);
|
||||
fsym = dataTable.symbolrate;
|
||||
M = double(dataTable.pam_level);
|
||||
|
||||
% Load and Sync signal data from DB
|
||||
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
|
||||
|
||||
% Preprocess signal
|
||||
Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
|
||||
|
||||
if rate == 390e9
|
||||
Scpe_sig.spectrum("fignum",200,"normalizeTo0dB",1,"displayname",'Rx','addDCoffset',-5.8);
|
||||
elseif rate == 300e9
|
||||
Scpe_sig.spectrum("fignum",200,"normalizeTo0dB",1,"displayname",'Rx','addDCoffset',-4.7);
|
||||
end
|
||||
ylim([-30,3]);
|
||||
xlim([-5,100]);
|
||||
Scpe_sig.spectrum("fignum",201,"normalizeTo0dB",0,"displayname",'Rx');
|
||||
|
||||
|
||||
%% 1 - PAM 4 without preemphasis
|
||||
fp = QueryFilter();
|
||||
M = 4;
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
fp.where('Runs', 'db_mode','EQUALS', 1);
|
||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||
|
||||
[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
|
||||
|
||||
dataTable = queryRunid(dataTable.run_id, database);
|
||||
fsym = dataTable.symbolrate;
|
||||
M = double(dataTable.pam_level);
|
||||
|
||||
% Load and Sync signal data from DB
|
||||
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
|
||||
|
||||
% Preprocess signal
|
||||
Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
|
||||
|
||||
Scpe_sig.spectrum("fignum",200,"normalizeTo0dB",1,"displayname",'Rx','addDCoffset',0);
|
||||
ylim([-30,3]);
|
||||
xlim([-5,100]);
|
||||
Scpe_sig.spectrum("fignum",201,"normalizeTo0dB",0,"displayname",'Rx');
|
||||
|
||||
|
||||
|
||||
if 1
|
||||
%% show freuqncy response of filter
|
||||
|
||||
measure = 1;
|
||||
|
||||
freqresp = ChannelFreqResp("Nacq",1024,"Navg",64,"Ncp",70,"f_ref",256e9);
|
||||
%
|
||||
Digi_sig = freqresp.buildOFDM();
|
||||
|
||||
% Digi_sig.spectrum("fignum",1112,"displayname",['maxamp:',num2str(maxamp)]);
|
||||
|
||||
Digi_sig = Filter('filtdegree',3,"f_cutoff",70e9,"fs",256e9,"filterType",filtertypes.butterworth,"active",true).process(Digi_sig);
|
||||
|
||||
Digi_sig = Filter('filtdegree',3,"f_cutoff",70e9,"fs",256e9,"filterType",filtertypes.bessel_inp,"active",true).process(Digi_sig);
|
||||
|
||||
freqresp.estimate(Digi_sig,"fileName",'','save',false);
|
||||
|
||||
freqresp.plot()
|
||||
|
||||
a = gca;
|
||||
a.YTick = [-30,-20,-10,0];
|
||||
|
||||
%% system frex
|
||||
|
||||
|
||||
precomp_filename ='lab_high_speed';
|
||||
precomp_path = "C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\precomp";
|
||||
freqresp = ChannelFreqResp("Nacq",1024,"Navg",64,"Ncp",63,'f_ref',92e9);
|
||||
freqresp.load('loadPath', precomp_path, 'fileName', precomp_filename);
|
||||
|
||||
fprintf('Plotting: %s\n', precomp_filename);
|
||||
freqresp.plot();
|
||||
|
||||
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\spectrum_2.tikz';
|
||||
matlab2tikz(outfile, ...
|
||||
'width','\fwidth', ...
|
||||
'height','\fheight', ...
|
||||
'showInfo',false, ...
|
||||
'extraAxisOptions',{ ...
|
||||
'legend style={font=\footnotesize}', ...
|
||||
'legend columns=1' ...
|
||||
});
|
||||
|
||||
end
|
||||
@@ -0,0 +1,222 @@
|
||||
function [h, M, cfg] = plot_measurements_gpt(T, cfg)
|
||||
% PLOT_MEASUREMENTS_GPT
|
||||
% Plot measurements, using analyze_measurements_gpt for data handling.
|
||||
%
|
||||
% Usage:
|
||||
% h = plot_measurements_gpt(dataTable, cfg);
|
||||
% [h, M] = plot_measurements_gpt(dataTable, cfg);
|
||||
%
|
||||
% For values only, without plotting, call:
|
||||
% [M, cfg] = analyze_measurements_gpt(dataTable, cfg);
|
||||
|
||||
if nargin < 2, cfg = struct; end
|
||||
|
||||
% --- First: run analysis (filtering, grouping, aggregation) ---
|
||||
[M, cfg] = analyze_measurements_gpt(T, cfg);
|
||||
|
||||
nG = M.nGroups;
|
||||
|
||||
%% ---- Plot defaults ----
|
||||
plotdefs = struct( ...
|
||||
'use_cbrewer2' , true, ...
|
||||
'colormap' , 'Paired', ...
|
||||
'paired_dark_first' , true, ...
|
||||
'lineWidth' , 1.8, ...
|
||||
'errWidth' , 1.0, ...
|
||||
'scatterSize' , 14, ...
|
||||
'scatterAlpha' , 0.35, ...
|
||||
'marker' , 'o', ...
|
||||
'marker_precoded' , 's', ...
|
||||
'legendLocation' , 'best', ...
|
||||
'fecLineWidth' , 2.2, ...
|
||||
'fecColor' , [0.25 0.25 0.25], ...
|
||||
'capSize' , 6, ...
|
||||
'lineStyle_default' , '-', ...
|
||||
'custom_colors' , [], ...
|
||||
'custom_colors_scatter' , [] ...
|
||||
);
|
||||
if ~isfield(cfg,'plot') || isempty(cfg.plot)
|
||||
cfg.plot = struct;
|
||||
end
|
||||
cfg.plot = filldefaults(cfg.plot, plotdefs);
|
||||
|
||||
%% ---- Colors ----
|
||||
[cols_line, cols_scatter] = buildGroupColors(nG, cfg.plot);
|
||||
|
||||
%% ---- Axes / Figure handling ----
|
||||
if isfield(cfg,'ax') && ~isempty(cfg.ax) && isgraphics(cfg.ax,'axes')
|
||||
ax = cfg.ax;
|
||||
set(gcf,'CurrentAxes',ax);
|
||||
else
|
||||
if isfield(cfg,'figure_number') && ~isempty(cfg.figure_number)
|
||||
figure(cfg.figure_number);
|
||||
else
|
||||
figure;
|
||||
end
|
||||
ax = gca;
|
||||
end
|
||||
hold(ax,'on');
|
||||
grid(ax,'on');
|
||||
|
||||
h.lines = gobjects(nG,1);
|
||||
h.err = gobjects(nG,1);
|
||||
h.scat = gobjects(nG,1);
|
||||
h.lines_p = gobjects(nG,1);
|
||||
|
||||
%% ---- Plot each group ----
|
||||
for gi = 1:nG
|
||||
g = M.group{gi};
|
||||
xu = g.x;
|
||||
yu = g.y;
|
||||
ylo = g.y_lo;
|
||||
yhi = g.y_hi;
|
||||
|
||||
% --- Linestyle selection ---
|
||||
ls = cfg.plot.lineStyle_default;
|
||||
if isfield(cfg.plot,'custom_linetypes') && ~isempty(cfg.plot.custom_linetypes)
|
||||
L = cfg.plot.custom_linetypes;
|
||||
ls = L{ mod(gi-1, numel(L)) + 1 };
|
||||
end
|
||||
|
||||
colL = cols_line(gi,:);
|
||||
lbl = g.label;
|
||||
|
||||
% Main line
|
||||
h.lines(gi) = plot(ax, xu, yu, ...
|
||||
'LineWidth', cfg.plot.lineWidth, ...
|
||||
'Marker', cfg.plot.marker, 'MarkerSize', 3, ...
|
||||
'Color', colL, 'LineStyle', ls, ...
|
||||
'DisplayName', char(lbl));
|
||||
|
||||
% Spread
|
||||
if any(isfinite(ylo)) && any(isfinite(yhi))
|
||||
h.err(gi) = errorbar(ax, xu, yu, ylo, yhi, 'LineStyle','none', ...
|
||||
'Color', colL, 'CapSize', cfg.plot.capSize, 'HandleVisibility','off');
|
||||
h.err(gi).LineWidth = cfg.plot.errWidth;
|
||||
end
|
||||
|
||||
% Raw scatter
|
||||
if cfg.show_raw
|
||||
% reuse stored raw data (no extra filtering)
|
||||
xi = g.x_raw;
|
||||
yi = g.y_raw;
|
||||
colS = cols_scatter(gi,:);
|
||||
scatter(ax, xi, yi, cfg.plot.scatterSize, colS, 'filled', ...
|
||||
'MarkerFaceAlpha', cfg.plot.scatterAlpha, ...
|
||||
'MarkerEdgeAlpha', cfg.plot.scatterAlpha, ...
|
||||
'HandleVisibility','off');
|
||||
end
|
||||
|
||||
% Precoded overlay
|
||||
if cfg.show_precoded && ~isempty(g.y_precoded) && strcmpi(M.y_axis,'BER')
|
||||
ypu = g.y_precoded;
|
||||
h.lines_p(gi) = plot(ax, xu, ypu, ...
|
||||
'LineWidth', max(1.2, cfg.plot.lineWidth-0.2), ...
|
||||
'Marker', cfg.plot.marker_precoded, 'MarkerSize', 3, ...
|
||||
'Color', colL, 'LineStyle', ':', ...
|
||||
'DisplayName', [char(lbl) ' (precoded)']);
|
||||
end
|
||||
end
|
||||
|
||||
%% ---- Axes / Labels / FEC ----
|
||||
ylabel(ax, M.y_axis, 'Interpreter','none');
|
||||
xlabel(ax, M.x_label, 'Interpreter','none');
|
||||
set(ax, 'YScale', cfg.y_scale, 'FontSize', 11);
|
||||
|
||||
% X ticks/limits using all group x-values
|
||||
allX = cellfun(@(g) g.x(:), M.group, 'UniformOutput', false);
|
||||
allX = unique(vertcat(allX{:}));
|
||||
if ~isempty(allX)
|
||||
xticks(ax, allX);
|
||||
xticklabels(cellstr(num2str(round(allX,1), '%.4f')))
|
||||
xlim(ax, [min(allX), max(allX)]);
|
||||
end
|
||||
|
||||
if startsWith(M.y_axis,"BER",'IgnoreCase',true)
|
||||
for v = cfg.fec_lines
|
||||
yline(ax, v, '--', 'Color', cfg.plot.fecColor, ...
|
||||
'LineWidth', cfg.plot.fecLineWidth, 'HandleVisibility','off');
|
||||
end
|
||||
ylim(ax, [1e-5, 0.5]);
|
||||
yticks(ax, [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]);
|
||||
end
|
||||
|
||||
% legend(ax, 'Location', cfg.plot.legendLocation); % if you want legends
|
||||
|
||||
box(ax,'on');
|
||||
|
||||
end % ===== main =====
|
||||
|
||||
|
||||
%% ===================== Helpers =====================
|
||||
|
||||
function cfg = filldefaults(cfg, defs)
|
||||
fn = fieldnames(defs);
|
||||
for i = 1:numel(fn)
|
||||
f = fn{i};
|
||||
if ~isfield(cfg, f) || isempty(cfg.(f))
|
||||
cfg.(f) = defs.(f);
|
||||
elseif isstruct(defs.(f)) && isstruct(cfg.(f))
|
||||
cfg.(f) = filldefaults(cfg.(f), defs.(f));
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function [cols_line, cols_scatter] = buildGroupColors(nG, plotcfg)
|
||||
|
||||
% 1) User-provided custom colors
|
||||
if isfield(plotcfg,'custom_colors') && ~isempty(plotcfg.custom_colors)
|
||||
C = plotcfg.custom_colors;
|
||||
if size(C,1) < nG
|
||||
error('custom_colors must have at least nG=%d rows.', nG);
|
||||
end
|
||||
cols_line = C(1:nG, :);
|
||||
|
||||
if isfield(plotcfg,'custom_colors_scatter') && ~isempty(plotcfg.custom_colors_scatter)
|
||||
Cs = plotcfg.custom_colors_scatter;
|
||||
if size(Cs,1) < nG
|
||||
error('custom_colors_scatter must have at least nG=%d rows.', nG);
|
||||
end
|
||||
cols_scatter = Cs(1:nG, :);
|
||||
else
|
||||
cols_scatter = zeros(nG,3);
|
||||
for i = 1:nG
|
||||
cols_scatter(i,:) = lightenColor(cols_line(i,:), 0.40);
|
||||
end
|
||||
end
|
||||
return;
|
||||
end
|
||||
|
||||
% 2) Standard behavior
|
||||
useBrewer = plotcfg.use_cbrewer2 && exist('cbrewer2','file')==2;
|
||||
if useBrewer
|
||||
N = max(2*nG, 12);
|
||||
C = cbrewer2(plotcfg.colormap, N);
|
||||
cols_line = zeros(nG,3);
|
||||
cols_scatter = zeros(nG,3);
|
||||
for i = 1:nG
|
||||
if plotcfg.paired_dark_first
|
||||
dark = C(2*i-1, :);
|
||||
light = C(2*i, :);
|
||||
else
|
||||
light = C(2*i-1, :);
|
||||
dark = C(2*i, :);
|
||||
end
|
||||
cols_line(i,:) = dark;
|
||||
cols_scatter(i,:) = light;
|
||||
end
|
||||
else
|
||||
C = lines(max(nG,7));
|
||||
cols_line = C(1:nG,:);
|
||||
cols_scatter = zeros(nG,3);
|
||||
for i = 1:nG
|
||||
cols_scatter(i,:) = lightenColor(cols_line(i,:), 0.50);
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
function c2 = lightenColor(c, fracTowardWhite)
|
||||
c = c(:).';
|
||||
c2 = (1-fracTowardWhite)*c + fracTowardWhite*1;
|
||||
end
|
||||
@@ -0,0 +1,436 @@
|
||||
function h = plot_measurements_gpt_old(T, cfg)
|
||||
% Versatile plotting from your DB table (with cbrewer2 'Paired' palette).
|
||||
%
|
||||
% Usage:
|
||||
% h = plot_measurements_flex(dataTable, cfg)
|
||||
|
||||
%% ---- Defaults
|
||||
if nargin < 2, cfg = struct; end
|
||||
defaults = struct( ...
|
||||
'x_axis' , 'symbolrate', ...
|
||||
'y_axis' , 'BER', ...
|
||||
'y_scale' , 'auto', ...
|
||||
'group_by' , {{'equalizer_structure','pre_emph'}}, ...
|
||||
'filters' , struct, ...
|
||||
'agg' , 'mean', ...
|
||||
'outlier' , 'auto', ...
|
||||
'mad_z' , 4, ...
|
||||
'pct_limits' , [2.5 97.5], ...
|
||||
'min_pts_x' , 3, ...
|
||||
'show_raw' , true, ...
|
||||
'show_precoded', [], ...
|
||||
'show_spread' , 'none', ...
|
||||
'fec_lines' , [], ...
|
||||
'plot', struct() ...
|
||||
);
|
||||
cfg = filldefaults(cfg, defaults);
|
||||
|
||||
% ---- Plot defaults (new)
|
||||
plotdefs = struct( ...
|
||||
'use_cbrewer2' , true, ...
|
||||
'colormap' , 'Paired', ... % ColorBrewer 'Paired'
|
||||
'paired_dark_first' , true, ... % dark for lines, light for scatter
|
||||
'lineWidth' , 1.8, ...
|
||||
'errWidth' , 1.0, ...
|
||||
'scatterSize' , 14, ...
|
||||
'scatterAlpha' , 0.35, ...
|
||||
'marker' , 'o', ...
|
||||
'marker_precoded' , 's', ...
|
||||
'lineStyle_pre_emph_on' , '--', ...
|
||||
'lineStyle_pre_emph_off', '-', ...
|
||||
'legendLocation' , 'best', ...
|
||||
'fecLineWidth' , 2.2, ... % thicker FEC limits
|
||||
'fecColor' , [0.25 0.25 0.25], ...
|
||||
'capSize' , 6, ...
|
||||
'lineStyle_default' , '-', ...
|
||||
'use_pre_emph_styling' , true ...
|
||||
);
|
||||
cfg.plot = filldefaults(cfg.plot, plotdefs);
|
||||
|
||||
%% ---- Derived/prep columns
|
||||
if ~ismember('pre_emph', T.Properties.VariableNames)
|
||||
if ~ismember('db_mode', T.Properties.VariableNames)
|
||||
error('Missing column "db_mode" for pre_emph derivation.');
|
||||
end
|
||||
T.pre_emph = T.db_mode == 0;
|
||||
end
|
||||
if ~ismember(cfg.y_axis, T.Properties.VariableNames)
|
||||
error('y_axis "%s" not found in table.', cfg.y_axis);
|
||||
end
|
||||
|
||||
isBER = startsWith(cfg.y_axis, "BER", 'IgnoreCase', true);
|
||||
if strcmpi(cfg.y_scale,'auto'), cfg.y_scale = tern(isBER, 'log', 'linear'); end
|
||||
if strcmpi(cfg.outlier,'auto'), cfg.outlier = tern(isBER, 'mad', 'none'); end
|
||||
if isempty(cfg.show_precoded)
|
||||
cfg.show_precoded = isBER && ismember('BER_precoded', T.Properties.VariableNames);
|
||||
end
|
||||
|
||||
%% ---- Filters
|
||||
T = applyFilters(T, cfg.filters);
|
||||
[x_raw, x_label] = computeX(T, cfg.x_axis);
|
||||
y_raw = T.(cfg.y_axis);
|
||||
|
||||
validXY = isfinite(x_raw) & isfinite(y_raw);
|
||||
T = T(validXY, :);
|
||||
x_raw = x_raw(validXY);
|
||||
y_raw = y_raw(validXY);
|
||||
|
||||
if mean(abs(y_raw)) > 1e8
|
||||
%giga values
|
||||
y_raw = y_raw.*1e-9;
|
||||
end
|
||||
|
||||
if cfg.show_precoded && ismember('BER_precoded', T.Properties.VariableNames)
|
||||
y_raw_p = T.BER_precoded(validXY);
|
||||
else
|
||||
y_raw_p = [];
|
||||
end
|
||||
|
||||
%% ---- Grouping
|
||||
group_by = cfg.group_by;
|
||||
if ~all(ismember(group_by, T.Properties.VariableNames))
|
||||
error('Some group_by columns are missing in table.');
|
||||
end
|
||||
[G, grpTbl] = findgroups(T(:, group_by));
|
||||
nG = max(G);
|
||||
|
||||
% ==== Colors (cbrewer2 'Paired' with dark/ light pairs) ====
|
||||
[cols_line, cols_scatter] = buildGroupColors(nG, cfg.plot);
|
||||
|
||||
%% ---- Axes / Figure handling (new unified logic)
|
||||
|
||||
% Priority:
|
||||
% 1) cfg.ax → use existing axes (subplots/tiles)
|
||||
% 2) cfg.figure_number → select/create figure
|
||||
% 3) fallback: create new figure
|
||||
|
||||
if isfield(cfg,'ax') && ~isempty(cfg.ax) && isgraphics(cfg.ax,'axes')
|
||||
ax = cfg.ax; % use caller-provided axes
|
||||
set(gcf,'CurrentAxes',ax);
|
||||
else
|
||||
if isfield(cfg,'figure_number') && ~isempty(cfg.figure_number)
|
||||
figure(cfg.figure_number);
|
||||
else
|
||||
figure;
|
||||
end
|
||||
ax = gca; % active axes
|
||||
end
|
||||
|
||||
hold(ax,'on');
|
||||
grid(ax,'on');
|
||||
|
||||
|
||||
h.lines = gobjects(nG,1);
|
||||
h.err = gobjects(nG,1);
|
||||
h.scat = gobjects(nG,1);
|
||||
h.lines_p = gobjects(nG,1);
|
||||
|
||||
for gi = 1:nG
|
||||
idx = (G==gi);
|
||||
Ti = T(idx,:);
|
||||
xi = x_raw(idx);
|
||||
yi = y_raw(idx);
|
||||
|
||||
% Aggregate per unique x
|
||||
[xu, ia, iu] = unique(xi);
|
||||
yu = nan(size(xu));
|
||||
ylo = nan(size(xu));
|
||||
yhi = nan(size(xu));
|
||||
|
||||
for k = 1:numel(xu)
|
||||
bin = (iu==k);
|
||||
yy = yi(bin);
|
||||
yy = yy(isfinite(yy));
|
||||
if isempty(yy), continue; end
|
||||
km = outlierMask(yy, cfg, strcmpi(cfg.y_scale,'log'));
|
||||
if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
|
||||
yy = yy(km);
|
||||
|
||||
if strcmpi(cfg.agg,'median'), yu(k)=median(yy,'omitnan'); elseif strcmpi(cfg.agg,'mean'), yu(k)=mean(yy,'omitnan'); elseif strcmpi(cfg.agg,'min'), yu(k)=min(yy); elseif strcmpi(cfg.agg,'max'), yu(k)=max(yy); end
|
||||
if strcmpi(cfg.show_spread,'iqr')
|
||||
q = prctile(yy,[25 75]);
|
||||
ylo(k) = max(yu(k)-q(1), eps);
|
||||
yhi(k) = max(q(2)-yu(k), eps);
|
||||
elseif strcmpi(cfg.show_spread,'minmax')
|
||||
ylo(k) = min(yy);
|
||||
yhi(k) = max(yy);
|
||||
end
|
||||
end
|
||||
|
||||
% sort
|
||||
[xu, ord] = sort(xu);
|
||||
yu = yu(ord);
|
||||
ylo = ylo(ord);
|
||||
yhi = yhi(ord);
|
||||
|
||||
% Styles
|
||||
% Decide if we style by pre_emph
|
||||
% --- LINE TYPE SELECTION (no pre-emphasis logic) ---
|
||||
ls = cfg.plot.lineStyle_default;
|
||||
|
||||
% User-defined override (cycled)
|
||||
if isfield(cfg.plot,'custom_linetypes') && ~isempty(cfg.plot.custom_linetypes)
|
||||
L = cfg.plot.custom_linetypes;
|
||||
ls = L{ mod(gi-1, numel(L)) + 1 };
|
||||
end
|
||||
|
||||
lbl = buildLabel(grpTbl(gi,:), group_by);
|
||||
|
||||
% Main line (dark)
|
||||
colL = cols_line(gi,:);
|
||||
h.lines(gi) = plot(xu, yu, ...
|
||||
'LineWidth', cfg.plot.lineWidth, ...
|
||||
'Marker', cfg.plot.marker, 'MarkerSize', 3, ...
|
||||
'Color', colL, 'LineStyle', ls, ...
|
||||
'DisplayName', char(lbl));
|
||||
|
||||
% Spread (IQR) in line color
|
||||
if any(isfinite(ylo)) && any(isfinite(yhi))
|
||||
h.err(gi) = errorbar(xu, yu, ylo, yhi, 'LineStyle','none', ...
|
||||
'Color', colL, 'CapSize', cfg.plot.capSize, 'HandleVisibility','off');
|
||||
h.err(gi).LineWidth = cfg.plot.errWidth;
|
||||
end
|
||||
|
||||
% Raw kept scatter (light)
|
||||
if cfg.show_raw
|
||||
keep_all = false(size(yi));
|
||||
for k = 1:numel(xu)
|
||||
bin = (iu==k);
|
||||
yy = yi(bin);
|
||||
km = outlierMask(yy, cfg, strcmpi(cfg.y_scale,'log'));
|
||||
if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
|
||||
keep_all(bin) = km;
|
||||
end
|
||||
colS = cols_scatter(gi,:);
|
||||
scatter(xi(keep_all), yi(keep_all), cfg.plot.scatterSize, colS, 'filled', ...
|
||||
'MarkerFaceAlpha', cfg.plot.scatterAlpha, 'MarkerEdgeAlpha', cfg.plot.scatterAlpha, ...
|
||||
'HandleVisibility','off');
|
||||
end
|
||||
|
||||
% Precoded overlay (dotted, squares), in line color
|
||||
if cfg.show_precoded && ~isempty(y_raw_p) && strcmpi(cfg.y_axis,'BER')
|
||||
ypi = y_raw_p(idx);
|
||||
ypu = nan(size(xu));
|
||||
for k = 1:numel(xu)
|
||||
bin = (iu==k);
|
||||
yy = ypi(bin);
|
||||
yy = yy(isfinite(yy));
|
||||
if isempty(yy), continue; end
|
||||
km = outlierMask(yy, cfg, true);
|
||||
if nnz(km) < cfg.min_pts_x, km = true(size(yy)); end
|
||||
yy = yy(km);
|
||||
if strcmpi(cfg.agg,'median'), ypu(k)=median(yy,'omitnan'); elseif strcmpi(cfg.agg,'mean'), ypu(k)=mean(yy,'omitnan'); elseif strcmpi(cfg.agg,'min'), ypu(k)=min(yy); elseif strcmpi(cfg.agg,'max'), ypu(k)=max(yy); end
|
||||
end
|
||||
h.lines_p(gi) = plot(xu, ypu, ...
|
||||
'LineWidth', max(1.2, cfg.plot.lineWidth-0.2), ...
|
||||
'Marker', cfg.plot.marker_precoded, 'MarkerSize', 3, ...
|
||||
'Color', colL, 'LineStyle', ':', ...
|
||||
'DisplayName', [char(lbl) ' (precoded)']);
|
||||
end
|
||||
|
||||
|
||||
end
|
||||
|
||||
%% ---- Axes / Labels / FEC
|
||||
ylabel(cfg.y_axis, 'Interpreter','none');
|
||||
xlabel(x_label, 'Interpreter','none');
|
||||
set(gca, 'YScale', cfg.y_scale, 'FontSize', 11);
|
||||
% legend('Location', cfg.plot.legendLocation); box on;
|
||||
|
||||
xticks(floor(xu));
|
||||
xlim([min(xu), max(xu)])
|
||||
|
||||
if startsWith(cfg.y_axis,"BER",'IgnoreCase',true)
|
||||
for v = cfg.fec_lines
|
||||
yline(v, '--', 'Color', cfg.plot.fecColor, ...
|
||||
'LineWidth', cfg.plot.fecLineWidth, 'HandleVisibility','off');
|
||||
end
|
||||
ylim([1e-5, 0.5]);
|
||||
yticks([1e-5, 1e-4, 1e-3, 1e-2, 1e-1]);
|
||||
end
|
||||
|
||||
|
||||
|
||||
end % ===== main =====
|
||||
|
||||
|
||||
%% ===================== Helpers =====================
|
||||
|
||||
function cfg = filldefaults(cfg, defs)
|
||||
fn = fieldnames(defs);
|
||||
for i = 1:numel(fn)
|
||||
f = fn{i};
|
||||
if ~isfield(cfg, f) || isempty(cfg.(f))
|
||||
cfg.(f) = defs.(f);
|
||||
elseif isstruct(defs.(f)) && isstruct(cfg.(f))
|
||||
cfg.(f) = filldefaults(cfg.(f), defs.(f)); % recursive for structs
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function out = tern(cond, a, b)
|
||||
if cond
|
||||
out = a;
|
||||
else
|
||||
out = b;
|
||||
end
|
||||
end
|
||||
|
||||
function T2 = applyFilters(T, filters)
|
||||
if isempty(filters), T2 = T; return; end
|
||||
keep = true(height(T),1);
|
||||
fns = fieldnames(filters);
|
||||
for i = 1:numel(fns)
|
||||
name = fns{i};
|
||||
if ~ismember(name, T.Properties.VariableNames)
|
||||
warning('Filter column "%s" not found. Ignored.', name); %#ok<*WNTAG>
|
||||
continue
|
||||
end
|
||||
val = filters.(name);
|
||||
col = T.(name);
|
||||
if isa(val,'function_handle')
|
||||
m = val(col);
|
||||
if ~islogical(m) || ~isequal(size(m), size(col))
|
||||
error('Filter for %s must return logical mask of same size.', name);
|
||||
end
|
||||
keep = keep & m;
|
||||
else
|
||||
keep = keep & ismember(col, val);
|
||||
end
|
||||
end
|
||||
T2 = T(keep,:);
|
||||
end
|
||||
|
||||
function [x, label] = computeX(T, whichX)
|
||||
switch lower(whichX)
|
||||
case {'symbolrate','baudrate'}
|
||||
x = T.symbolrate * 1e-9;
|
||||
label = 'Symbol rate [GBd]';
|
||||
case 'bitrate'
|
||||
if ~ismember('pam_level', T.Properties.VariableNames)
|
||||
error('bitrate requires "pam_level" column.');
|
||||
end
|
||||
bits = floor(log2(double(T.pam_level))*10)/10;
|
||||
x = (T.symbolrate .* bits) * 1e-9;
|
||||
label = 'Grossrate [Gb/s]';
|
||||
case 'grossrate'
|
||||
x = (T.grossrate) * 1e-9;
|
||||
label = 'Grossrate [Gb/s]';
|
||||
otherwise
|
||||
if ~ismember(whichX, T.Properties.VariableNames)
|
||||
error('x_axis "%s" not found in table.', whichX);
|
||||
end
|
||||
x = T.(whichX);
|
||||
label = whichX;
|
||||
end
|
||||
x = double(x(:));
|
||||
end
|
||||
|
||||
function keep = outlierMask(y, cfg, useLog)
|
||||
if isempty(y), keep = false(size(y)); return; end
|
||||
y = y(:);
|
||||
switch lower(cfg.outlier)
|
||||
case 'none'
|
||||
keep = true(size(y)); return
|
||||
case 'mad'
|
||||
z = tern(useLog, log10(y), y);
|
||||
med = median(z,'omitnan');
|
||||
madv = median(abs(z-med),'omitnan');
|
||||
if ~(isfinite(madv) && madv>0)
|
||||
keep = true(size(y)); return
|
||||
end
|
||||
sigma = 1.4826*madv;
|
||||
zz = tern(useLog, log10(y), y);
|
||||
keep = abs(zz - med) <= cfg.mad_z*sigma;
|
||||
case 'pctl'
|
||||
pr = prctile(y, cfg.pct_limits);
|
||||
keep = (y >= pr(1)) & (y <= pr(2));
|
||||
otherwise
|
||||
error('Unknown outlier mode "%s".', cfg.outlier);
|
||||
end
|
||||
end
|
||||
|
||||
function s = buildLabel(grpRow, group_by)
|
||||
parts = strings(1, numel(group_by));
|
||||
for i = 1:numel(group_by)
|
||||
key = group_by{i};
|
||||
val = grpRow.(key);
|
||||
if iscell(val), val = val{1}; end
|
||||
if islogical(val), val = tern(val,'w/','w/o'); end
|
||||
if key == "equalizer_structure"
|
||||
key = '';
|
||||
val = upper(val);
|
||||
val = strrep(val,'_',' ');
|
||||
end
|
||||
|
||||
if key == "pre_emph"
|
||||
% key = strrep(key,'_','-');
|
||||
val = [val, ' pre-emph.'];
|
||||
key = '';
|
||||
end
|
||||
|
||||
parts(i) = sprintf('%s %s', key, string(val));
|
||||
end
|
||||
s = strjoin(parts, ', ');
|
||||
end
|
||||
|
||||
function [cols_line, cols_scatter] = buildGroupColors(nG, plotcfg)
|
||||
|
||||
% --- 1) User-provided custom colors -------------------------------
|
||||
if isfield(plotcfg,'custom_colors') && ~isempty(plotcfg.custom_colors)
|
||||
C = plotcfg.custom_colors;
|
||||
if size(C,1) < nG
|
||||
error('custom_colors must have at least nG=%d rows.', nG);
|
||||
end
|
||||
cols_line = C(1:nG, :);
|
||||
|
||||
% Scatter colors: either user-provided or lightened
|
||||
if isfield(plotcfg,'custom_colors_scatter') && ~isempty(plotcfg.custom_colors_scatter)
|
||||
Cs = plotcfg.custom_colors_scatter;
|
||||
if size(Cs,1) < nG
|
||||
error('custom_colors_scatter must have at least nG=%d rows.', nG);
|
||||
end
|
||||
cols_scatter = Cs(1:nG, :);
|
||||
else
|
||||
% auto-lighten scatter colors
|
||||
cols_scatter = zeros(nG,3);
|
||||
for i = 1:nG
|
||||
cols_scatter(i,:) = lightenColor(cols_line(i,:), 0.40);
|
||||
end
|
||||
end
|
||||
return;
|
||||
end
|
||||
|
||||
% --- 2) Standard behavior (using cbrewer2 or fallback) ------------
|
||||
useBrewer = plotcfg.use_cbrewer2 && exist('cbrewer2','file')==2;
|
||||
if useBrewer
|
||||
N = max(2*nG, 12);
|
||||
C = cbrewer2(plotcfg.colormap, N);
|
||||
cols_line = zeros(nG,3);
|
||||
cols_scatter = zeros(nG,3);
|
||||
for i = 1:nG
|
||||
if plotcfg.paired_dark_first
|
||||
dark = C(2*i-1, :);
|
||||
light = C(2*i, :);
|
||||
else
|
||||
light = C(2*i-1, :);
|
||||
dark = C(2*i, :);
|
||||
end
|
||||
cols_line(i,:) = dark;
|
||||
cols_scatter(i,:) = light;
|
||||
end
|
||||
else
|
||||
C = lines(max(nG,7));
|
||||
cols_line = C(1:nG,:);
|
||||
cols_scatter = zeros(nG,3);
|
||||
for i = 1:nG
|
||||
cols_scatter(i,:) = lightenColor(cols_line(i,:), 0.50);
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
function c2 = lightenColor(c, fracTowardWhite)
|
||||
c = c(:).';
|
||||
c2 = (1-fracTowardWhite)*c + fracTowardWhite*1;
|
||||
end
|
||||
@@ -0,0 +1,38 @@
|
||||
X-Werte;PAM-2;PAM-4;PAM-6;PAM-8;PAM-12
|
||||
224;204;;;;
|
||||
205,5;193,4;;;;
|
||||
205,1;189,7;;;;
|
||||
192;168;;;;
|
||||
240;;427,4;;;
|
||||
225;;420,5;;;
|
||||
210;;336;;;
|
||||
184;;332;;;
|
||||
192;;320;;;
|
||||
176;;306,0869565;;;
|
||||
190;;304;;;
|
||||
168;;294;;;
|
||||
156,2;;287,1;;;
|
||||
160,8;;286,9;;;
|
||||
170;;272;;;
|
||||
132;;250,9505703;;;
|
||||
112;;209,3457944;;;
|
||||
172;;337;;;
|
||||
216;;;474,6;;
|
||||
147,2;;;329,9;;
|
||||
132;;;319,7891753;;
|
||||
143,1;;;318;;
|
||||
160;;;377;;
|
||||
225;;;;562,5;
|
||||
200;;;;510;
|
||||
160;;;;438;
|
||||
180;;;;432;
|
||||
180;;;;432;
|
||||
144;;;;384;
|
||||
143,7;;;;363,4;
|
||||
144;;;;360;
|
||||
136;;;;353,859497;
|
||||
136;;;;342,7995295;
|
||||
128;;;;329,0488432;
|
||||
129,7;;;;311,2;
|
||||
160;;;;413;
|
||||
160;;;;;481,2
|
||||
|
@@ -0,0 +1,76 @@
|
||||
database_type = 'mysql';
|
||||
dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
||||
db = DBHandler("dataBase", [dataBase], "type", database_type);
|
||||
|
||||
M = 8;
|
||||
fp = QueryFilter();
|
||||
% fp.where('Runs', 'run_id','EQUALS', 987);
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
% fp.where('Runs', 'symbolrate','EQUALS', 165e9); %150, 165, 180, 195, 210, 225, 240
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
% fp.where('Runs', 'is_mpi','EQUALS', 0);
|
||||
% fp.where('Runs', 'power_pd_in','GREATER_THAN', 7);
|
||||
% fp.where('Runs', 'interference_path_length','EQUALS', 1000);
|
||||
% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
|
||||
% fp.where('Runs', 'sir','EQUALS',18);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
% fp.where('Runs', 'db_mode','EQUALS', 1); % 0 == high preemphasis // 1 == low preemphasis
|
||||
% fp.where('Runs', 'rop_attenuation','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('power_state_info');
|
||||
fields = [fields; db.getTableFieldNames('dashboard_ungrouped_alltime')]; %dashboard_ungrouped_after_nov_2025 dashboard_ungrouped_aug_nov_2025
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
|
||||
%%
|
||||
cfg = struct;
|
||||
cfg.x_axis = 'grossrate'; % 'symbol rate' | 'bitrate' | 'wavelength' grossrate
|
||||
cfg.y_axis = 'BER'; % 'BER' | 'GMI' | 'AIR' | ...
|
||||
cfg.group_by = {'equalizer_structure','pre_emph'};
|
||||
cfg.filters = struct('is_mpi',0,'pam_level',M,'equalizer_structure',[equalizer_structure.ml_mlse]);%,equalizer_structure.vnle_pf_mlse,equalizer_structure.vnle]);
|
||||
|
||||
cfg.y_scale = 'auto'; % auto -> log for BER*, linear otherwise
|
||||
cfg.outlier = 'mad'; % simple, robust; 'none' or 'pctl' also available
|
||||
cfg.show_raw = false;
|
||||
cfg.show_spread = 'none'; % 'none' or 'iqr' or minmax
|
||||
cfg.agg = 'min'; % or 'median'
|
||||
cfg.show_precoded = 0;
|
||||
cfg.fec_lines = [2.2e-4 4.85e-3 2e-2]; % optional
|
||||
|
||||
cfg.figure_number = 42;
|
||||
cfg.plot.custom_colors = [
|
||||
clr.Paired.red;
|
||||
clr.Paired.blue;
|
||||
clr.Paired.green;
|
||||
clr.Paired.orange;
|
||||
clr.Paired.purple
|
||||
];
|
||||
|
||||
cfg.plot.custom_colors_scatter = [
|
||||
clr.Paired.lightred;
|
||||
clr.Paired.lightblue;
|
||||
clr.Paired.lightgreen;
|
||||
clr.Paired.lightorange;
|
||||
clr.Paired.lightpurple
|
||||
];
|
||||
|
||||
|
||||
% New styling knobs
|
||||
cfg.plot.use_cbrewer2 = true;
|
||||
cfg.plot.colormap = 'Paired';
|
||||
cfg.plot.paired_dark_first = false; % dark for lines, light for scatter
|
||||
cfg.plot.lineWidth = 2.0;
|
||||
cfg.plot.errWidth = 1.2;
|
||||
cfg.plot.scatterAlpha = 0.35;
|
||||
cfg.plot.legendLocation = 'best';
|
||||
cfg.plot.fecLineWidth = 2.4; % thicker FEC limits
|
||||
|
||||
plot_measurements_gpt(dataTable, cfg);
|
||||
|
||||
% beautifyBERplot()
|
||||
|
||||
%% FIG PRE EMPHASIS
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,87 @@
|
||||
|
||||
database_type = 'mysql';
|
||||
dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
||||
db = DBHandler("dataBase", [dataBase], "type", database_type);
|
||||
|
||||
M = 8;
|
||||
fp = QueryFilter();
|
||||
% fp.where('Runs', 'run_id','EQUALS', 987);
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
% fp.where('Runs', 'symbolrate','EQUALS', 165e9);
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'is_mpi','EQUALS', 0);
|
||||
% fp.where('Runs', 'interference_path_length','EQUALS', 1000);
|
||||
% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
|
||||
% fp.where('Runs', 'sir','EQUALS',18);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
% fp.where('Runs', 'db_mode','EQUALS', 1); % 0 == high preemphasis // 1 == low preemphasis
|
||||
fp.where('Runs', 'rop_attenuation','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('power_state_info');
|
||||
fields = [fields; db.getTableFieldNames('dashboard_ungrouped_new')];
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
eqstructures = unique(dataTable.equalizer_structure);
|
||||
|
||||
% Create the figure
|
||||
figure(18);
|
||||
hold on
|
||||
|
||||
for pre_emph = [0,1]
|
||||
|
||||
dbmode_filtered = dataTable(dataTable.db_mode == ~pre_emph,:);
|
||||
|
||||
for eqs = [equalizer_structure.vnle]
|
||||
|
||||
eq_choice = equalizer_structure(eqs);
|
||||
if sum(eqstructures == eq_choice)~=1
|
||||
disp(eq_choice)
|
||||
continue
|
||||
end
|
||||
|
||||
eq_filtered = dbmode_filtered(dbmode_filtered.equalizer_structure == eq_choice,:);
|
||||
if eqs ==equalizer_structure.vnle_pf_mlse
|
||||
eq_filtered = eq_filtered(eq_filtered.DIR == "1",:);
|
||||
end
|
||||
symbolrate_sorted = sortrows(eq_filtered,{'symbolrate'}, 'ascend');
|
||||
|
||||
|
||||
% Example data (replace these with your real vectors)
|
||||
symbolrate = symbolrate_sorted.symbolrate.*1e-9; % in baud
|
||||
bitrate = symbolrate * 2;
|
||||
gmi = symbolrate_sorted.GMI; % BER
|
||||
snr = symbolrate_sorted.SNR; % BER
|
||||
cols = cbrewer2('Paired',12);
|
||||
|
||||
dname = [char(eq_choice)];
|
||||
dname = strrep(dname,'_','+');
|
||||
if pre_emph
|
||||
dname = [dname,'; w/ pre-emph.'];
|
||||
else
|
||||
dname = [dname,'; w/o pre-emph.'];
|
||||
end
|
||||
|
||||
plot(symbolrate, snr, '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]);
|
||||
grid on;
|
||||
|
||||
% Axis labels and title
|
||||
xlabel('Bit Rate Gbps', 'FontSize', 12);
|
||||
ylabel('GMI', 'FontSize', 12);
|
||||
title('GMI vs. Bit Rate', 'FontSize', 14, 'FontWeight', 'bold');
|
||||
|
||||
% Improve tick formatting
|
||||
set(gca, 'XScale', 'linear', ...
|
||||
'YScale', 'linear', ...
|
||||
'TickLabelInterpreter', 'none', ...
|
||||
'FontSize', 11);
|
||||
legend
|
||||
|
||||
xticks(symbolrate);
|
||||
|
||||
% Optional: tighten axis limits
|
||||
xlim([min(symbolrate), max(symbolrate)]);
|
||||
% ylim([log2(M)-1, log2(M)]);
|
||||
|
||||
end
|
||||
|
||||
end
|
||||
@@ -0,0 +1,96 @@
|
||||
|
||||
database_type = 'mysql';
|
||||
dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
||||
db = DBHandler("dataBase", [dataBase], "type", database_type);
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('power_state_info', 'pam_level','EQUALS', 4);
|
||||
fp.where('power_state_info', 'db_mode','EQUALS', 1);
|
||||
% fp.where('power_state_info', 'fiber_length','EQUALS', 1);
|
||||
fp.where('power_state_info', 'is_mpi','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('power_state_info');
|
||||
% [dataTable,~] = db.queryDB(fp, fields);
|
||||
|
||||
fiber_len = unique(dataTable.fiber_length);
|
||||
cnt = 0;
|
||||
|
||||
y_variable = 'power_mzm';
|
||||
x_variable = "wavelength";
|
||||
f = figure(3);
|
||||
clf
|
||||
hold on
|
||||
for fl = 1:numel(fiber_len)
|
||||
|
||||
|
||||
fl_filtered = dataTable(dataTable.fiber_length == fiber_len(fl),:);
|
||||
[~, ia] = unique(fl_filtered.run_id, 'first');
|
||||
fl_filtered = fl_filtered(ia, :);
|
||||
|
||||
fl_filtered_ = groupsummary( ...
|
||||
fl_filtered, ... % input table
|
||||
x_variable, ... % grouping variable
|
||||
"mean", ... % which summary statistic
|
||||
y_variable); % which column to average
|
||||
|
||||
wavelength_sorted = sortrows(fl_filtered, {'wavelength'}, 'ascend');
|
||||
|
||||
% pull out your vectors
|
||||
lambda = wavelength_sorted.wavelength;
|
||||
power_laser = wavelength_sorted.power_laser;
|
||||
power_mzm = wavelength_sorted.power_mzm;
|
||||
power_rop = wavelength_sorted.power_rop;
|
||||
power_pd = wavelength_sorted.power_pd_in;
|
||||
voa = wavelength_sorted.voa_atten;
|
||||
len = wavelength_sorted.fiber_length;
|
||||
run_ids = wavelength_sorted.run_id; % <-- this is what we want in the datatip
|
||||
cols = linspecer(8);
|
||||
|
||||
|
||||
% plot the two curves and capture their Line handles
|
||||
% h1 = plot(lambda, power_laser,'LineWidth', 0.5, 'MarkerSize', 4,'Marker','o','LineStyle','none','Color',cols(fl,:),'MarkerFaceColor',cols(fl,:),'DisplayName','Laser Output');
|
||||
% % —————— Add run_id as a datatip row ——————
|
||||
% % For each line, tell the datatip template where to find the run_id:
|
||||
% h1.DataTipTemplate.DataTipRows(end+1) = ...
|
||||
% dataTipTextRow('run\_id', run_ids);
|
||||
% h1.DataTipTemplate.DataTipRows(end+1) = ...
|
||||
% dataTipTextRow('len', run_ids);
|
||||
% h1.DataTipTemplate.DataTipRows(end+1) = ...
|
||||
% dataTipTextRow('voaatten', voa);
|
||||
|
||||
%
|
||||
dname = sprintf('%s; %d km',y_variable, fiber_len(fl));
|
||||
h2 = plot(fl_filtered_.(x_variable), fl_filtered_.(['mean_',y_variable]), 'LineWidth', 1, 'MarkerSize', 4,'Marker','o','LineStyle','-','Color',cols(fl,:),'MarkerFaceColor',cols(fl,:),'DisplayName',dname);
|
||||
|
||||
h2.DataTipTemplate.DataTipRows(end+1) = ...
|
||||
dataTipTextRow('run\_id', run_ids);
|
||||
h2.DataTipTemplate.DataTipRows(end+1) = ...
|
||||
dataTipTextRow('len', len);
|
||||
h2.DataTipTemplate.DataTipRows(end+1) = ...
|
||||
dataTipTextRow('voaatten', voa);
|
||||
|
||||
grid on;
|
||||
xticks(sort(unique(lambda)));
|
||||
xticklabels(sort(unique(lambda)));
|
||||
|
||||
% Labels, scales, legend, etc.
|
||||
xlabel('Wavelength in nm','FontSize',12);
|
||||
ylabel('Power in dB','FontSize',12);
|
||||
title('Power ','FontSize',14,'FontWeight','bold');
|
||||
set(gca, 'XScale','linear','YScale','linear','FontSize',11);
|
||||
legend
|
||||
|
||||
xlim([min(lambda)-2, max(lambda)+2]);
|
||||
ylim([floor(min(fl_filtered_.(['mean_',y_variable])))-1 12]);
|
||||
ylim([-12 12]);
|
||||
|
||||
cnt = cnt+1;
|
||||
|
||||
yline(8,'HandleVisibility','off');
|
||||
|
||||
end
|
||||
|
||||
yline([4.85e-3, 2e-2],'--','LineWidth',1,'HandleVisibility','off');
|
||||
posH = get(f, 'Position'); % [left, bottom, width, height]
|
||||
newPos = [posH(1), posH(2), 750, 300];
|
||||
set(f, 'Position', newPos);
|
||||
@@ -0,0 +1,379 @@
|
||||
% === SETTINGS ===
|
||||
dsp_options.append_to_db = 1;
|
||||
dsp_options.max_occurences = 1;
|
||||
|
||||
experiment = "highspeed_2024";
|
||||
dsp_options.mode = "load_run_id"; % 'simulate' & 'load_files'
|
||||
dsp_options.load_file_path = struct();
|
||||
|
||||
if dsp_options.mode == "load_run_id"
|
||||
|
||||
if experiment == "highspeed_2024"
|
||||
|
||||
dsp_options.database_type = 'mysql';
|
||||
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||
db = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
|
||||
|
||||
elseif experiment == "mpi_ecoc_2025"
|
||||
|
||||
dsp_options.database_type = 'mysql';
|
||||
dsp_options.dataBase = 'labor';
|
||||
dsp_options.storage_path = 'Z:\2025\ECOC Silas\ecoc_2025\';
|
||||
db = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
|
||||
|
||||
end
|
||||
|
||||
elseif dsp_options.mode == "load_files"
|
||||
|
||||
dsp_options.load_file_path.tx_bits_path = "Z:\2025\ECOC Silas\ecoc_2025\mpi_opti_1000m_pam2 4 6 8\20250417_091513_PAM_4_R_112_bits.mat"';
|
||||
dsp_options.load_file_path.tx_symbols_path = "Z:\2025\ECOC Silas\ecoc_2025\mpi_opti_1000m_pam2 4 6 8\20250417_091513_PAM_4_R_112_symbols.mat"';
|
||||
dsp_options.load_file_path.rx_raw_path = "Z:\2025\ECOC Silas\ecoc_2025\mpi_opti_1000m_pam2 4 6 8\20250417_091525_PAM_4_R_112_rec01_rx_signal_raw.mat"';
|
||||
|
||||
elseif dsp_options.mode == "simulate"
|
||||
|
||||
error('Not yet implemented')
|
||||
|
||||
end
|
||||
|
||||
% === Get Run ID's ===
|
||||
|
||||
fp = QueryFilter();
|
||||
% fp.where('Runs', 'run_id','EQUALS', 2776);
|
||||
M = 6;
|
||||
fp.where('Runs', 'pam_level','EQUALS', M);
|
||||
% fp.where('Runs', 'bitrate','EQUALS', 390e9);%360,390
|
||||
% fp.where('Runs', 'symbolrate','EQUALS', 195e9);
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 10);
|
||||
fp.where('Runs', 'is_mpi','EQUALS', 0);
|
||||
% fp.where('Runs', 'interference_path_length','EQUALS', 1000);
|
||||
% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
|
||||
% fp.where('Runs', 'sir','EQUALS',18);
|
||||
% fp.where('Runs', 'wavelength','EQUALS', 1310);
|
||||
fp.where('Runs', 'db_mode','EQUALS', 0);
|
||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||
% fp.where('Runs', 'power_pd_in','LESS_THAN', 7);
|
||||
|
||||
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||
|
||||
% === Set LOOPS & Initialize DataStorage ===
|
||||
dsp_options.parameters = struct();
|
||||
% dsp_options.parameters.pf_ncoeffs = [1,2];%s[0,logspace(-4,0,10)];
|
||||
|
||||
wh = DataStorage(dsp_options.parameters);
|
||||
wh.addStorage("ffe_package");
|
||||
wh.addStorage("mlse_package");
|
||||
wh.addStorage("vnle_package");
|
||||
wh.addStorage("dbtgt_package");
|
||||
wh.addStorage("dbenc_package");
|
||||
wh.addStorage("mlmlse_package");
|
||||
|
||||
%% === RUN IT ===
|
||||
|
||||
[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "parallel", 'wh', wh, 'waitbar', true);
|
||||
|
||||
|
||||
|
||||
%% =========================================================================
|
||||
% LOAD METADATA
|
||||
% =========================================================================
|
||||
[dataTable, ~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||
results = results(:).'; % ensure row vector
|
||||
N = numel(results);
|
||||
|
||||
% =========================================================================
|
||||
% PREALLOCATE METRIC ARRAYS
|
||||
% =========================================================================
|
||||
BER_VNLE = nan(1,N);
|
||||
BER_MLSE = nan(1,N);
|
||||
BER_DB = nan(1,N);
|
||||
BER_DB_PREC = nan(1,N);
|
||||
BER_MLMLSE = nan(1,N);
|
||||
BER_MLMLSE_PREC = nan(1,N);
|
||||
|
||||
% =========================================================================
|
||||
% EXTRACT METRICS (ONE LOOP, ROBUST)
|
||||
% =========================================================================
|
||||
for i = 1:N
|
||||
r = results{i};
|
||||
|
||||
% ---- VNLE (no-DB mode) ----
|
||||
if isfield(r, 'vnle_package') && ~isempty(r.vnle_package)
|
||||
pkg = r.vnle_package;
|
||||
BER_VNLE(i) = min(cellfun(@(c) c.metrics.BER, pkg));
|
||||
end
|
||||
|
||||
% ---- Classical MLSE (DB mode) ----
|
||||
if isfield(r, 'mlse_package') && ~isempty(r.mlse_package)
|
||||
pkg = r.mlse_package;
|
||||
BER_MLSE(i) = min(cellfun(@(c) c.metrics.BER, pkg));
|
||||
BER_MLSE_PREC(i) = min(cellfun(@(c) c.metrics.BER_precoded, pkg));
|
||||
end
|
||||
|
||||
% ---- DB Target (DB mode) ----
|
||||
if isfield(r, 'dbtgt_package') && ~isempty(r.dbtgt_package)
|
||||
pkg = r.dbtgt_package;
|
||||
BER_DB(i) = min(cellfun(@(c) c.metrics.BER, pkg));
|
||||
BER_DB_PREC(i) = min(cellfun(@(c) c.metrics.BER_precoded, pkg));
|
||||
end
|
||||
|
||||
% ---- ML-based MLSE (both modes) ----
|
||||
if isfield(r, 'mlmlse_package') && ~isempty(r.mlmlse_package)
|
||||
pkg = r.mlmlse_package;
|
||||
|
||||
% raw BER
|
||||
BER_MLMLSE(i) = min(cellfun(@(c) c.metrics.BER, pkg));
|
||||
|
||||
% precoded BER
|
||||
if isfield(pkg{1}.metrics, 'BER_precoded')
|
||||
BER_MLMLSE_PREC(i) = min(cellfun(@(c) c.metrics.BER_precoded, pkg));
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
%% =========================================================================
|
||||
% METADATA (ALWAYS INDEX-ALIGNED WITH RESULTS)
|
||||
% =========================================================================
|
||||
bitrate = dataTable.bitrate(:).';
|
||||
baudrate = dataTable.symbolrate(:).';
|
||||
|
||||
rop_atten = dataTable.rop_attenuation(2:2:end).';
|
||||
rop_pre = dataTable.power_rop(1:2:end).';
|
||||
rop = dataTable.power_rop(2:2:end).';
|
||||
|
||||
% =========================================================================
|
||||
% PLOT STYLE
|
||||
% =========================================================================
|
||||
STYLE_BASE = 2;
|
||||
MARKER_SIZE = STYLE_BASE;
|
||||
LINE_WIDTH = max(2, STYLE_BASE/3);
|
||||
|
||||
cols = cbrewer2('Paired', 8);
|
||||
|
||||
cm.VNLE = cols(1,:);
|
||||
cm.MLSE = cols(2,:);
|
||||
cm.DB_PREC = cols(3,:);
|
||||
cm.DB = cols(4,:);
|
||||
cm.ML_MLSE = cols(6,:);
|
||||
|
||||
mk = @(col,shape) {'Marker',shape,'MarkerFaceColor',col,'MarkerEdgeColor',col,'MarkerSize',MARKER_SIZE};
|
||||
|
||||
% =========================================================================
|
||||
% FIGURE 1: BER vs BAUDRATE
|
||||
% =========================================================================
|
||||
figure(112+M); clf; hold on;
|
||||
xGHz = baudrate * 1e-9;
|
||||
|
||||
plot(xGHz, BER_VNLE, 'DisplayName','VNLE', 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
plot(xGHz, BER_MLSE, 'DisplayName','MLSE', 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
% plot(xGHz, BER_MLSE_PREC, 'DisplayName','MLSE', 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
plot(xGHz, BER_DB_PREC, 'DisplayName','Diff. Precode + DB', 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.DB_PREC);
|
||||
plot(xGHz, BER_MLMLSE_PREC, 'DisplayName','ML-based MLSE', 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.ML_MLSE);
|
||||
|
||||
yline(2e-2,'LineWidth',1,'HandleVisibility','off');
|
||||
yline(4.85e-3,'LineWidth',1,'HandleVisibility','off');
|
||||
yline(2.2e-4,'LineWidth',1,'HandleVisibility','off');
|
||||
|
||||
xlabel('Baudrate in GBd');
|
||||
ylabel('BER');
|
||||
set(gca, 'YScale', 'log'); grid on; legend('Location','best');
|
||||
% beautifyBERplot;
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
%% ---------------- FIGURE 15 : GMI ----------------
|
||||
figure(113+M); clf; hold on;
|
||||
plot(xGHz, GMI_VNLE, ...
|
||||
'DisplayName','VNLE', ...
|
||||
mk.VNLE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
plot(xGHz, GMI_MLSE, ...
|
||||
'DisplayName','MLSE', ...
|
||||
mk.MLSE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
plot(xGHz, GMI_DB, ...
|
||||
'DisplayName','DB tgt.', ...
|
||||
mk.DB{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.DB);
|
||||
|
||||
ylim([log2(M)-1, log2(M)]);
|
||||
xlabel('Baudrate in GBd');
|
||||
ylabel('GMI');
|
||||
set(gca, 'XTick', xticks_vals(1:2:end), 'XTickLabel', xtick_labels(1:2:end));
|
||||
grid on;
|
||||
legend('Location','best');
|
||||
|
||||
|
||||
|
||||
% ---------------- FIGURE 15 : AIR ----------------
|
||||
m = floor(log2(M)*10)/10;
|
||||
figure(114+M); clf; hold on;
|
||||
plot(xGHz, GMI_VNLE.*xGHz, ...
|
||||
'DisplayName','AIR VNLE', ...
|
||||
mk.VNLE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
% duobinary has only one GMI curve (DB output)
|
||||
plot(xGHz, GMI_MLSE.*xGHz, ...
|
||||
'DisplayName','AIR MLSE', ...
|
||||
mk.MLSE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
% MLSE symbol-wise (if present)
|
||||
plot(xGHz, GMI_DB.*xGHz, ...
|
||||
'DisplayName','AIR DB tgt.', ...
|
||||
mk.DB{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.DB);
|
||||
|
||||
% ylim([log2(M)-1, log2(M)]);
|
||||
xlabel('Baudrate in GBd');
|
||||
ylabel('AIR in Gbps');
|
||||
set(gca, 'XTick', xticks_vals(1:2:end), 'XTickLabel', xtick_labels(1:2:end));
|
||||
grid on;
|
||||
legend('Location','best');
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
% ---------------- FIGURE 15 : Information Rates ----------------
|
||||
tp = TransmissionPerformance;
|
||||
|
||||
|
||||
m = floor(log2(M)*10)/10;
|
||||
figure(213+M); clf; hold on;
|
||||
|
||||
netrates_vnle = tp.calculateNetRate(baudrate.* m, ...
|
||||
'NGMI', GMI_VNLE./m, ...
|
||||
'BER', BER_VNLE);
|
||||
%
|
||||
% plot(xGHz, GMI_VNLE.*xGHz, ...
|
||||
% 'DisplayName','GMI*R VNLE', ...
|
||||
% mk.VNLE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
%
|
||||
% plot(xGHz, netrates_vnle.SDHD.NetRate.*1e-9, ...
|
||||
% 'DisplayName','SD+HD VNLE', ...
|
||||
% mk.VNLE{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
% plot(xGHz, netrates_vnle.HD.NetRate.*1e-9, ...
|
||||
% 'DisplayName','Staircase VNLE', ...
|
||||
% mk.VNLE{:}, 'LineStyle','-.','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
%
|
||||
%
|
||||
% %
|
||||
% % MLSE symbol-wise (if present)
|
||||
% plot(xGHz, GMI_MLSE.*xGHz, ...
|
||||
% 'DisplayName','GMI*R MLSE', ...
|
||||
% mk.MLSE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
%
|
||||
% netrates_mlse = tp.calculateNetRate(baudrate.* m, ...
|
||||
% 'NGMI', GMI_MLSE./m, ...
|
||||
% 'BER', BER_MLSE);
|
||||
% plot(xGHz, netrates_mlse.SDHD.NetRate.*1e-9, ...
|
||||
% 'DisplayName','SD+HD MLSE', ...
|
||||
% mk.MLSE{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
% plot(xGHz, netrates_mlse.HD.NetRate.*1e-9, ...
|
||||
% 'DisplayName','Staircase MLSE', ...
|
||||
% mk.MLSE{:}, 'LineStyle','-.','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
|
||||
|
||||
% duobinary has only one GMI curve (DB output)
|
||||
figure(1111); clf; hold on;
|
||||
plot(xGHz, GMI_DB.*xGHz, ...
|
||||
'DisplayName','GMI*R DB tgt.', ...
|
||||
mk.DB{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.DB);
|
||||
|
||||
netrates_db = tp.calculateNetRate(baudrate.* m, ...
|
||||
'NGMI', GMI_DB./m, ...
|
||||
'BER', BER_DB_PREC);
|
||||
|
||||
plot(xGHz, netrates_db.SDHD.NetRate.*1e-9, ...
|
||||
'DisplayName','SD+HD DB', ...
|
||||
mk.DB{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.DB);
|
||||
plot(xGHz, netrates_db.STAIR.NetRate.*1e-9, ...
|
||||
'DisplayName','Staircase DB', ...
|
||||
mk.DB_precode{:}, 'LineStyle','-.','LineWidth',LINE_WIDTH,'Color',cm.DB_precode);
|
||||
plot(xGHz, netrates_db.O_FEC.NetRate.*1e-9, ...
|
||||
'DisplayName','O-FEC DB', ...
|
||||
mk.DB_precode{:}, 'LineStyle','--','LineWidth',LINE_WIDTH,'Color',cm.DB_precode);
|
||||
plot(xGHz, netrates_db.KP4_hamming.NetRate.*1e-9, ...
|
||||
'DisplayName','KP4 Hamming DB', ...
|
||||
mk.DB_precode{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.DB_precode);
|
||||
|
||||
% ylim([log2(M)-1, log2(M)]);
|
||||
xlabel('Baudrate in GBd');
|
||||
ylabel('AIR in Gbps');
|
||||
set(gca, 'XTick', xticks_vals(1:2:end), 'XTickLabel', xtick_labels(1:2:end));
|
||||
grid on;
|
||||
legend('Location','best');
|
||||
% xlim([1, 256])
|
||||
|
||||
|
||||
|
||||
figure(2222); clf; hold on;
|
||||
plot(xGHz, GMI_MLSE.*xGHz, ...
|
||||
'DisplayName','GMI*R DB tgt.', ...
|
||||
mk.MLSE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
|
||||
netrates_mlse = tp.calculateNetRate(baudrate.* m, ...
|
||||
'NGMI', GMI_MLSE./m, ...
|
||||
'BER', BER_MLSE);
|
||||
|
||||
plot(xGHz, netrates_mlse.SDHD.NetRate.*1e-9, ...
|
||||
'DisplayName','SD+HD DB', ...
|
||||
mk.MLSE{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
plot(xGHz, netrates_mlse.STAIR.NetRate.*1e-9, ...
|
||||
'DisplayName','Staircase DB', ...
|
||||
mk.VNLE{:}, 'LineStyle','-.','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
plot(xGHz, netrates_mlse.O_FEC.NetRate.*1e-9, ...
|
||||
'DisplayName','O-FEC DB', ...
|
||||
mk.VNLE{:}, 'LineStyle','--','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
plot(xGHz, netrates_mlse.KP4_hamming.NetRate.*1e-9, ...
|
||||
'DisplayName','KP4 Hamming DB', ...
|
||||
mk.VNLE{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
|
||||
% ylim([log2(M)-1, log2(M)]);
|
||||
xlabel('Baudrate in GBd');
|
||||
ylabel('AIR in Gbps');
|
||||
set(gca, 'XTick', xticks_vals(1:2:end), 'XTickLabel', xtick_labels(1:2:end));
|
||||
grid on;
|
||||
legend('Location','best');
|
||||
% xlim([1, 256])
|
||||
|
||||
|
||||
|
||||
figure(3333); clf; hold on;
|
||||
plot(xGHz, GMI_VNLE.*xGHz, ...
|
||||
'DisplayName','GMI*R DB tgt.', ...
|
||||
mk.MLSE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
|
||||
netrates_vnle = tp.calculateNetRate(baudrate.* m, ...
|
||||
'NGMI', GMI_VNLE./m, ...
|
||||
'BER', BER_VNLE);
|
||||
|
||||
plot(xGHz, netrates_vnle.SDHD.NetRate.*1e-9, ...
|
||||
'DisplayName','SD+HD DB', ...
|
||||
mk.MLSE{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
|
||||
plot(xGHz, netrates_vnle.STAIR.NetRate.*1e-9, ...
|
||||
'DisplayName','Staircase DB', ...
|
||||
mk.VNLE{:}, 'LineStyle','-.','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
plot(xGHz, netrates_vnle.O_FEC.NetRate.*1e-9, ...
|
||||
'DisplayName','O-FEC DB', ...
|
||||
mk.VNLE{:}, 'LineStyle','--','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
plot(xGHz, netrates_vnle.KP4_hamming.NetRate.*1e-9, ...
|
||||
'DisplayName','KP4 Hamming DB', ...
|
||||
mk.VNLE{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.VNLE);
|
||||
|
||||
% ylim([log2(M)-1, log2(M)]);
|
||||
xlabel('Baudrate in GBd');
|
||||
ylabel('AIR in Gbps');
|
||||
set(gca, 'XTick', xticks_vals(1:2:end), 'XTickLabel', xtick_labels(1:2:end));
|
||||
grid on;
|
||||
legend('Location','best');
|
||||
% xlim([1, 256])
|
||||
@@ -0,0 +1,236 @@
|
||||
|
||||
precomp_mode = 0;
|
||||
precomp_path = "C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\HighSpeedExperiment_2024\Auswertung_JLT";
|
||||
precomp_fn = "precomp_simulated.mat";
|
||||
|
||||
% TX
|
||||
M = 4;
|
||||
fsym = 72e9;
|
||||
f_nyquist = fsym/2;
|
||||
apply_pulsef = 1;
|
||||
fdac = 256e9;
|
||||
fadc = 256e9;
|
||||
% fdac = 2*fsym;
|
||||
% fadc = 2*fsym;
|
||||
random_key = 1;
|
||||
|
||||
duob_mode = db_mode.no_db;
|
||||
|
||||
tx_bwl = 0.8.*f_nyquist;
|
||||
rx_bwl = 0.8.*f_nyquist;
|
||||
|
||||
rcalpha = 0.05;
|
||||
kover = 16;
|
||||
vbias_rel = 0.5;
|
||||
u_pi = 2.9;
|
||||
vbias = -vbias_rel*u_pi;
|
||||
laser_wavelength = 1293;
|
||||
laser_linewidth = 0;
|
||||
|
||||
|
||||
% Channel
|
||||
link_length = 60000;
|
||||
|
||||
% RX
|
||||
rop = -8;
|
||||
|
||||
% EQ
|
||||
eq_mode = equalizer_structure.vnle_pf_mlse;
|
||||
ffe_order=[50,0,0];
|
||||
vnle_order=[50,5,5];
|
||||
dfe_order = [0 0 0];
|
||||
|
||||
len_tr = 4096*2;
|
||||
mu_ffe = [0.0004 0.0004 0.0004];
|
||||
mu_dfe = 0.0004;
|
||||
mu_dc = 0.00;
|
||||
|
||||
dfe_ = sum(dfe_order)>0;
|
||||
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
|
||||
"fsym",fsym,"M",M,"order",18,"useprbs",0,...
|
||||
"fs_out",fdac,...
|
||||
"applyclipping",0,"clipfactor",1.5,...
|
||||
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||
"randkey",random_key,...
|
||||
"duobinary_mode",duob_mode).process();
|
||||
|
||||
if precomp_mode == 1 % measure channel
|
||||
precomp_est = ChannelFreqResp("Nacq",1024,"Navg",64,"Ncp",63,'f_ref',fdac);
|
||||
Digi_sig = precomp_est.buildOFDM();
|
||||
elseif precomp_mode == 2 % apply precomp
|
||||
precomp_est = ChannelFreqResp("Nacq",1024,"Navg",64,"Ncp",63,'f_ref',Digi_sig.fs);
|
||||
Digi_sig = precomp_est.precomp(Digi_sig,'maxampdb',-50,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
end
|
||||
|
||||
% Symbols.spectrum("displayname",'Tx Symbols','fignum',10,'normalizeTo0dB',1);
|
||||
|
||||
Digi_sig.eye(fsym,M,"fignum",1234567);
|
||||
%%%%% AWG
|
||||
%El_sig = M8199B("kover",kover).process(Digi_sig);
|
||||
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1).process(Digi_sig);
|
||||
% El_sig.spectrum("displayname",'Digi Spectrum','fignum',100,'normalizeTo0dB',0);
|
||||
% El_sig = El_sig.setPower(0,"dBm");
|
||||
El_sig.spectrum("displayname",'Tx Signal','fignum',10,'normalizeTo0dB',0);
|
||||
%%%%% Low-pass el. components %%%%%%
|
||||
|
||||
El_sig = Filter('filtdegree',4,"f_cutoff",tx_bwl,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true).process(El_sig);
|
||||
% El_sig.spectrum("displayname",'Digi Spectrum','fignum',100,'normalizeTo0dB',1);
|
||||
|
||||
%%%%% Electrical Driver Amplifier %%%%%%
|
||||
El_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",3).process(El_sig);
|
||||
El_sig = El_sig.normalize("mode","oneone");
|
||||
|
||||
%%%%% MODULATE E/O CONVERSION %%%%%%
|
||||
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1).process(El_sig);
|
||||
|
||||
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
|
||||
|
||||
%%%%%% ROP %%%%%%
|
||||
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
|
||||
|
||||
%%%%%% PD Square Law %%%%%%
|
||||
Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
|
||||
|
||||
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
|
||||
Rx_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover,"filterType",filtertypes.bessel_inp,"active",true).process(Rx_sig);
|
||||
|
||||
% %%%%%% Low-pass Scope %%%%%%
|
||||
Lp_scpe = Filter('filtdegree',4,"f_cutoff",35e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
|
||||
|
||||
% Rx_sig.spectrum("displayname",'Analog Rx Spectrum','fignum',100,'normalizeTo0dB',1);
|
||||
|
||||
%%%%%% Scope %%%%%%
|
||||
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
|
||||
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
|
||||
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
|
||||
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);
|
||||
|
||||
Scpe_sig.spectrum("displayname",'Rx Signal','fignum',10,'normalizeTo0dB',1);
|
||||
Scpe_sig.eye(fsym,M,"fignum",1973763)
|
||||
|
||||
%%%%% Precompensation Routine %%%%%%
|
||||
if precomp_mode == 1
|
||||
Scpe_sig_resampled = Scpe_sig.resample("fs_in",fadc,"fs_out",2*fsym);
|
||||
precomp_est.estimate(Scpe_sig_resampled,"save",false,"savePath",precomp_path,"fileName",precomp_fn);
|
||||
precomp_est.plot();
|
||||
precomp_est.save();
|
||||
end
|
||||
|
||||
% Preprocess signal
|
||||
Scpe_sig = preprocessSignal(Scpe_sig, Symbols, fsym);
|
||||
Scpe_sig.signal = Scpe_sig.signal(1:2*Symbols.length);
|
||||
use_ffe = 0;
|
||||
use_dfe = 0;
|
||||
use_vnle_mlse = 1;
|
||||
use_dbtgt = 1;
|
||||
use_dbenc = 1;
|
||||
|
||||
|
||||
if duob_mode ~= db_mode.db_encoded
|
||||
|
||||
if use_ffe
|
||||
|
||||
ffe_order = [50, 0, 0];
|
||||
eq_dfe = EQ("Ne",ffe_order,"Nb",[0,0,0],"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",0);
|
||||
|
||||
ffe_results = ffe(eq_dfe,M,Scpe_sig,Symbols,Tx_bits,...
|
||||
"precode_mode",duob_mode,...
|
||||
'showAnalysis',1,...
|
||||
"postFFE",[],...
|
||||
"eth_style_symbol_mapping",0);
|
||||
|
||||
disp('FFE:')
|
||||
ffe_results.metrics.print;
|
||||
|
||||
|
||||
end
|
||||
|
||||
if use_dfe
|
||||
|
||||
ffe_order = [50, 5, 5];
|
||||
eq_dfe = EQ("Ne",ffe_order,"Nb",[2,0,0],"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",0);
|
||||
|
||||
dfe_results = ffe(eq_dfe,M,Scpe_sig,Symbols,Tx_bits,...
|
||||
"precode_mode",duob_mode,...
|
||||
'showAnalysis',0,...
|
||||
"postFFE",[],...
|
||||
"eth_style_symbol_mapping",0);
|
||||
|
||||
disp('DFE:')
|
||||
dfe_results.metrics.print;
|
||||
|
||||
|
||||
end
|
||||
|
||||
if use_vnle_mlse
|
||||
|
||||
if 0
|
||||
pf_ncoeffs = 1;
|
||||
eq_ = EQ("Ne",ffe_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);
|
||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||
% mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
||||
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
||||
|
||||
[ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
|
||||
"precode_mode", duob_mode,...
|
||||
'showAnalysis', 1, ...
|
||||
"postFFE", [],...
|
||||
"eth_style_symbol_mapping", 0);
|
||||
|
||||
disp('VNLE:')
|
||||
ffe_results.metrics.print;
|
||||
disp('MLSE:')
|
||||
mlse_results.metrics.print;
|
||||
end
|
||||
|
||||
pf_ncoeffs = 2;
|
||||
eq_ = EQ("Ne",ffe_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);
|
||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||
% mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
||||
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
||||
|
||||
[ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
|
||||
"precode_mode", duob_mode,...
|
||||
'showAnalysis', 1, ...
|
||||
"postFFE", [],...
|
||||
"eth_style_symbol_mapping", 0);
|
||||
|
||||
disp('VNLE:')
|
||||
ffe_results.metrics.print;
|
||||
disp('MLSE:')
|
||||
mlse_results.metrics.print;
|
||||
|
||||
|
||||
end
|
||||
|
||||
if use_dbtgt
|
||||
eq_ = EQ("Ne",ffe_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);
|
||||
|
||||
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
|
||||
|
||||
dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
|
||||
"precode_mode", duob_mode, ...
|
||||
'showAnalysis',1 ,...
|
||||
"postFFE", []);
|
||||
|
||||
disp('DB:')
|
||||
dbt_results.metrics.print;
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
if duob_mode == db_mode.db_encoded
|
||||
|
||||
eq_db_enc = EQ("Ne", ffe_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);
|
||||
|
||||
mlse_db_enc = MLSE("DIR", [1,1], "duobinary_output", 0, "M", M, "trellis_states", PAMmapper(M,0).levels);
|
||||
|
||||
db_results = duobinary_signaling(eq_db_enc, mlse_db_enc, M, Scpe_sig, Symbols, Tx_bits, "precode_mode",duob_mode, "showAnalysis",1,"postFFE",[]);
|
||||
|
||||
db_results.metrics.print;
|
||||
end
|
||||
Reference in New Issue
Block a user