new plots for Diss. Mostly AI gen. Few changes in actual codebase
This commit is contained in:
Silas Oettinghaus
@@ -1,5 +1,5 @@
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dsp_options.storage_path = 'Z:\2024\sioe_labor\';
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dsp_options.max_occurences = 1;
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dsp_options.storage_path = 'W:\labdata\sioe_labor\';
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dsp_options.max_occurences = 10;
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database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
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@@ -7,34 +7,32 @@ cols = cbrewer2('BuPu',25);
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cols = [cols(end-10:2:end,:)];
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cols = cbrewer2('Set1',6);
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fignum = 200;
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fig=figure(fignum);clf;
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dbmode = 0;
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% 1 - PAM 4 with preemphasis
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fp = QueryFilter();
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M = 8;
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rate = [360e9];
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M = 4;
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% rate = [300e9];
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fp.where('Runs', 'pam_level','EQUALS', M);
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fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
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% fp.where('Runs', 'symbolrate','EQUALS',165e9);
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fp.where('Runs', 'fiber_length','EQUALS', 2);
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fp.where('Runs', 'wavelength','EQUALS', 1310);
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fp.where('Runs', 'db_mode','EQUALS', dbmode);
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fp.where('Runs', 'rop_attenuation','EQUAL', 0);
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[dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
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dataTable = queryRunid(dataTable.run_id, database);
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dataTable = dataTable(1,:);
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% dataTable = queryRunid(dataTable.run_id, database);
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fsym = dataTable.symbolrate;
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M = double(dataTable.pam_level);
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duob_mode = db_mode(strrep(dataTable.db_mode,'"',''));
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% Load and Sync signal data from DB
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[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncRunSignals(dataTable, dsp_options);
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[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncRunSignals(dataTable,dsp_options);
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Scpe_sig_syncd = Scpe_cell{1};
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Scpe_sig_syncd.eye(fsym,M,"fignum",rate.*1e-9*M+1,"displayname",' Eye of Signal');
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% Scpe_sig_syncd.eye(fsym,M,"fignum",rate.*1e-9*M+1,"displayname",' Eye of Signal');
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%%%%%% SNR CHEAT - Avges the measured signal occurences found after correlation in "tsynch" %%%%%%
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average_signals = 1;
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if average_signals
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@@ -46,16 +44,19 @@ if average_signals
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scope_mean = scope_mean ./ n;
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Scpe_sig_avg.signal = scope_mean;
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Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1);
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Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
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% Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1);
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% Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
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Scpe_sig_avg = Scpe_sig_avg.*1.25;
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Scpe_sig_avg.eye(fsym,M,"fignum",rate.*1e-9*M,"displayname",' Eye of AVG Signal');
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% Scpe_sig_avg.eye(fsym,M,"fignum",rate.*1e-9*M,"displayname",' Eye of AVG Signal');
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Scpe_sig = Scpe_sig_avg.normalize("mode","rms");
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end
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% Preprocess signal
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Scpe_sig = preprocessSignal(Scpe_sig_avg, Symbols, fsym);
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% Scpe_sig = preprocessSignal(Scpe_sig_avg, Symbols, fsym);
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Scpe_sig.eye(fsym,M,"fignum",M*10);
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%% Eye of Preprocess signal
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% Scpe_sig.eye(fsym,M,"fignum",M*10);
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@@ -71,8 +72,67 @@ Scpe_sig.eye(fsym,M,"fignum",M*10);
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% 'legend style={font=\footnotesize}', ...
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% 'legend columns=1' ...
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% } );
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%% Simply FFE
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%%
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len_tr = 4096*2;
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% combine and minimize repeated params
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mu_ffe = [0.0001,0.0008,0.001];
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mu_dd = 0.05;
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mu_dc = 0.005;
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% single ffe_order used
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ffe_order = [50];
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% map into p for FFE init (compact)
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p.epochs_tr = 5;
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p.epochs_dd = 5;
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p.len_tr = 4096*2;
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p.ffe_mu_tr = mu_ffe(1);
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p.ffe_mu_dd = mu_dd;
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p.ffe_order = ffe_order;
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p.eq_sps = 2;
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p.optimize_mus = true;
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p.dd_mode = true;
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p.ffe_adaption = 1;
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p.mu_dc = mu_dc;
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% Initialize FFE with mapped parameters
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eq_ffe = FFE("epochs_tr", p.epochs_tr, ...
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"epochs_dd", p.epochs_dd, ...
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"len_tr", p.len_tr, ...
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"mu_dd", p.ffe_mu_dd, ...
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"mu_tr", p.ffe_mu_tr, ...
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"order", p.ffe_order(1), ...
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"sps", p.eq_sps, ...
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"decide", false, ...
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"optmize_mus", p.optimize_mus, ...
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"dd_mode", p.dd_mode, ...
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"adaption_technique", p.ffe_adaption, ...
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"dc_tracking_mu", p.mu_dc);
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mu_ffe = [0.0001, 0.0008, 0.001];
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mu_dfe = 0.0004;
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eq_ffe = EQ("Ne", [50,0,0], ...
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"Nb", [0,0,0], ...
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"training_length", p.len_tr, ...
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"training_loops", p.epochs_tr, ...
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"dd_loops", p.epochs_dd, ...
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"K", 2, ...
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"DCmu", p.mu_dc, ...
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"DDmu", [mu_ffe mu_dfe], ...
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"DFEmu", 0.005, ...
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"FFEmu", 0, ...
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"plotfinal", 0, ...
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"ideal_dfe", false);
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[ffe_results, equalized_signal] = ffe(eq_ffe, M, Scpe_sig, Symbols, Tx_bits, ...
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"precode_mode", dbmode, ...
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"showAnalysis", 0, ...
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"postFFE", [], ...
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"eth_style_symbol_mapping", 0);
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equalized_signal.eye(fsym,M,"fignum",M*2);
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%% Duobinary
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if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
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trellexlusion = 1;
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@@ -102,178 +162,4 @@ dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
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'showAnalysis', 1,...
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"postFFE", []);
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%% === FINAL FIGURE SIZE ===
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% Existing figure numbers
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figEye = 249;
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figConst = 341;
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% Find axes in the source figures
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srcAxEye = findobj(figEye, 'Type', 'axes');
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srcAxConst = findobj(figConst, 'Type', 'axes');
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% Create new combined figure
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figCombined = figure;
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t = tiledlayout(figCombined, 1, 2);
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t.TileSpacing = 'compact';
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t.Padding = 'compact';
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% ------------------------------------------------------------
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% LEFT TILE: EYE DIAGRAM
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% ------------------------------------------------------------
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ax1 = nexttile(t, 1);
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hold(ax1, 'on')
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% Copy children (images, lines, patches, hist objects, etc.)
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copyobj(srcAxEye.Children, ax1);
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% Copy labels and title
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ax1.XLabel.String = srcAxEye.XLabel.String;
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ax1.YLabel.String = srcAxEye.YLabel.String;
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ax1.Title.String = srcAxEye.Title.String;
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% Copy axis limits
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ax1.XLim = srcAxEye.XLim;
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ax1.YLim = srcAxEye.YLim;
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ax1.YDir = srcAxEye.YDir;
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% Copy ticks + labels EXACTLY (including remapped/scaled ones)
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ax1.XTick = srcAxEye.XTick;
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ax1.XTickLabel = srcAxEye.XTickLabel;
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ax1.YTick = srcAxEye.YTick;
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ax1.YTickLabel = srcAxEye.YTickLabel;
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% Copy colormap + clim (important for density eye)
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colormap(ax1, colormap(srcAxEye.Parent));
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ax1.CLim = srcAxEye.CLim;
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% Copy any style props that matter
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ax1.TickDir = srcAxEye.TickDir;
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ax1.TickLength = srcAxEye.TickLength;
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ax1.FontSize = srcAxEye.FontSize;
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ax1.Box = srcAxEye.Box;
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grid(ax1,'on');
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% ------------------------------------------------------------
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% RIGHT TILE: CONSTELLATION HISTOGRAM
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% ------------------------------------------------------------
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ax2 = nexttile(t, 2);
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hold(ax2, 'on')
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copyobj(srcAxConst.Children, ax2);
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% Copy labels and title
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ax2.XLabel.String = srcAxConst.XLabel.String;
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ax2.YLabel.String = srcAxConst.YLabel.String;
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ax2.Title.String = srcAxConst.Title.String;
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% The histogram uses the same y-axis as the eye
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% Extract mapping from eye
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rawTicks = ax1.YTick;
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rawLabelsCell = ax1.YTickLabel;
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trueVoltages = str2double(rawLabelsCell);
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% Apply true voltages to the histogram axis
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ax2.XTick = flip(trueVoltages);
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ax2.XTickLabel = flip(rawLabelsCell);
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% Set histogram y-limits to match the actual voltages
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ax2.XLim = [min(trueVoltages) max(trueVoltages)];
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% Ensure eye diagram prints the same (we *do not* touch ax1.YLim)
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ax1.XTickLabel = rawLabelsCell;
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% Copy colormap (your histogram uses same palette)
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colormap(ax2, colormap(srcAxConst.Parent));
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% Style properties
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ax2.TickDir = srcAxConst.TickDir;
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ax2.TickLength = srcAxConst.TickLength;
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ax2.FontSize = srcAxConst.FontSize;
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ax2.Box = srcAxConst.Box;
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grid(ax2,'on');
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% ============================================================
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% remove right y-axis completely
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% ============================================================
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ax2.XAxis.Visible = 'off'; % hides ticks + labels + axis line
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% BUT we still keep the YTick positions internally for alignment:
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% ax2.YTick = <values already set earlier> ;
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% ============================================================
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% minimize distance between the two plots
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% ============================================================
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t.TileSpacing = 'none'; % no space between tiles
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t.Padding = 'none'; % no outer padding
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% Also reduce internal padding for each axis
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ax1.Position(3) = ax1.Position(3) + 0.02; % widen eye a bit
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ax2.Position(1) = ax2.Position(1) - 0.02; % pull histogram closer
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% Keep left axis grid visible
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ax2.YGrid = 'off';
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%
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% =====================================================================
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% FINAL POLISHING: unified visual style
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% =======================================================================
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% --- unified font size ---
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FS = 12;
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set([ax1 ax2], 'FontSize', FS);
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% --- unified axis line width (outline stroke thickness) ---
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LW = 1.0;
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set([ax1 ax2], 'LineWidth', LW);
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% --- unified tick length ---
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TL = [.015 .015];
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set([ax1 ax2], 'TickLength', TL);
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% --- unified grid style ---
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set([ax1 ax2], 'XGrid', 'on', 'YGrid', 'on');
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set([ax1 ax2], 'GridLineStyle', '--');
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set([ax1 ax2], 'GridAlpha', 0.2);
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% --- remove right y-axis ticks and labels ---
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ax2.YAxis.Visible = 'off';
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% --- copy colormap + CLim from the eye to histogram (synchronize look) ---
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colormap(ax1, colormap(srcAxEye.Parent));
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colormap(ax2, colormap(srcAxEye.Parent));
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ax2.CLim = ax1.CLim;
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% --- minimal spacing between tiles ---
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t.TileSpacing = 'none';
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t.Padding = 'none';
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% --- pull the panels together (touching boundary effect) ---
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pos1 = ax1.Position;
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pos2 = ax2.Position;
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% Shift histogram left until the outlines touch
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pos2(1) = pos1(1) + pos1(3) - 0.002; % 0.002 = fine overlap control
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ax2.Position = pos2;
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% Expand histogram slightly, remove white band
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pos2 = ax2.Position;
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pos2(3) = pos2(3) + 0.01;
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ax2.Position = pos2;
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% Ensure the left plot stays correct after the move
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ax1.Position = pos1;
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% --- enforce same visible outline ---
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% For ax2, create a fake left spine (since YAxis is hidden)
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ax2.Box = 'on'; % keep outline but no ticks on the right
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ax1.Box = 'on';
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ax2.View = [90 -90];
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@@ -1,4 +1,4 @@
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dsp_options.storage_path = 'Z:\2024\sioe_labor\';
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dsp_options.storage_path = 'W:\labdata\sioe_labor\';
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dsp_options.max_occurences = 1;
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% database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
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dsp_options.database_type = "mysql";
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@@ -18,85 +18,102 @@ cols = cbrewer2('BuPu',25);
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cols = [cols(end-10:2:end,:)];
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cols = cbrewer2('Set1',6);
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fignum = 200;
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fig=figure(fignum);clf;
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% fignum = 200;
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% fig=figure(fignum);clf;
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for dbmode = 0%length(rates)
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if 1
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rcalpha = 0.05;
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fsym = rates/2;
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pulsef = 1;
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Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
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rcalpha = 0.05;
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fsym = rates/2;
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pulsef = 1;
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Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
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Pamsource = PAMsource(...
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"fsym",fsym,"M",4,"order",18,"useprbs",0,...
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"fs_out",fdac,...
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"applyclipping",0,"clipfactor",1.2,...
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"applypulseform",pulsef,"pulseformer",Pform,...
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"randkey",20,...
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"db_precode",dbmode,"db_encode",0,...
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"mrds_code",0,"mrds_blocklength",512);
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Pamsource = PAMsource(...
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"fsym",fsym,"M",4,"order",18,"useprbs",0,...
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"fs_out",256e9,...
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"applyclipping",0,"clipfactor",1.2,...
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"applypulseform",pulsef,"pulseformer",Pform,...
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"randkey",20,...
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"duobinary_mode",dbmode,...
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"mrds_code",0,"mrds_blocklength",512);
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[Digi_sig,Symbols,Bits] = Pamsource.process();
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[Digi_sig,Symbols,Bits] = Pamsource.process();
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Digi_sig = Digi_sig.normalize("mode","rms");
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Digi_sig = Digi_sig.normalize("mode","rms");
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%%% 1) PLOT FULL RESPONSE SIGNAL
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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);
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%%% 1) PLOT FULL RESPONSE SIGNAL
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Digi_sig.spectrum("displayname","Full Response","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",clr.Paired.dgreen,"linestyle",'-','addDCoffset',0,'normalizeToDC',1);
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%%% 2) PLOT PREEMPH. TX SIGNAL
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if dbmode == 0
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maxamp = -37;
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precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
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precomp_path = "W:\labdata\sioe_labor\precomp";
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precomp_fn = "lab_high_speed";
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Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
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%%% 2) PLOT PREEMPH. TX SIGNAL
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if dbmode == 0
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maxamp = -37;
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precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
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Digi_sig_pre = Digi_sig_pre.resample("fs_out",fdac);
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precomp_path = "W:\labdata\sioe_labor\precomp";
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precomp_fn = "lab_high_speed";
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Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
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Digi_sig_pre= Digi_sig_pre.normalize("mode","rms");
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Digi_sig_pre = Digi_sig_pre.resample("fs_out",256e9);
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Digi_sig_pre.spectrum("displayname","Strong Precomp","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0,0,0],"linestyle",'-.','addDCoffset',0,'normalizeToDC',1);
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end
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Digi_sig_pre= Digi_sig_pre.normalize("mode","rms");
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Digi_sig_pre.spectrum("displayname","Tx w/ pre-emph.","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",clr.Paired.dred,"linestyle",'-.','addDCoffset',0,'normalizeToDC',1);
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elseif dbmode == 2
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maxamp = -38;
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precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
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precomp_path = "W:\labdata\sioe_labor\precomp";
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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",256e9);
|
||||
|
||||
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', 'pam_level','EQUALS', M);
|
||||
fp.where('Runs', 'bitrate','EQUALS', rates);%360,390
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 2);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310); %1327.4
|
||||
fp.where('Runs', 'db_mode','EQUALS', dbmode);
|
||||
fp.where('Runs', 'rop_attenuation','EQUAL', 0);
|
||||
|
||||
fp.where('Runs', 'fiber_length','EQUALS', 10);
|
||||
fp.where('Runs', 'wavelength','EQUALS', 1310); %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, ~] = loadAndSyncRunSignals(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
|
||||
if dbmode ~= 2
|
||||
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','--');
|
||||
else
|
||||
DB_Symbols = Symbols;
|
||||
end
|
||||
DB_Symbols.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'DB response','addDCoffset',0,'color',clr.Paired.dblue,'normalizeToNyquist',0,'linestyle','-');
|
||||
|
||||
|
||||
%%% 4) Plot RX Signal
|
||||
Scpe_sig = Scpe_sig - mean(Scpe_sig.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');
|
||||
% 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;
|
||||
@@ -113,8 +130,8 @@ for dbmode = 0%length(rates)
|
||||
Symbols.spectrum("fignum",20,"normalizeTo0dB",1,"displayname",'Full Response','addDCoffset',0,'color',clr.Set1.red,'normalizeToNyquist',0,'linestyle','--');
|
||||
DB_Symbols.spectrum("fignum",20,"normalizeTo0dB",1,"displayname",'DB-Response','addDCoffset',0,'color',clr.Set1.blue,'normalizeToNyquist',0,'linestyle','--');
|
||||
Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1,"addDCoffset",5);
|
||||
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');
|
||||
% 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
|
||||
|
||||
|
||||
@@ -128,48 +145,48 @@ for dbmode = 0%length(rates)
|
||||
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);
|
||||
beautifyBERplot("logscale",0,"setmarkers",0,"setcolors",0,"changemarkers",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_ = 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);
|
||||
@@ -187,8 +204,8 @@ for dbmode = 0%length(rates)
|
||||
yticks(-50:10:10);
|
||||
|
||||
beautifyBERplot("logscale",0,"setmarkers",0)
|
||||
pos = [100.3333 991.6667 358.0000 192.6667];
|
||||
set(fig, 'Position', pos);
|
||||
% pos = [100.3333 991.6667 358.0000 192.6667];
|
||||
% set(fig, 'Position', pos);
|
||||
|
||||
end
|
||||
|
||||
|
||||
@@ -1,160 +0,0 @@
|
||||
%% ============================================================
|
||||
% 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
|
||||
@@ -107,14 +107,14 @@ if 1
|
||||
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' ...
|
||||
});
|
||||
% 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
|
||||
|
||||
@@ -8,6 +8,7 @@ fp.where('power_state_info', 'pam_level','EQUALS', 4);
|
||||
fp.where('power_state_info', 'db_mode','EQUALS', 0);
|
||||
% fp.where('power_state_info', 'fiber_length','EQUALS', 1);
|
||||
fp.where('power_state_info', 'is_mpi','EQUALS', 0);
|
||||
fp.where('rop_attenuation', 'is_mpi','EQUALS', 0);
|
||||
|
||||
fields = db.getTableFieldNames('power_state_info');
|
||||
[dataTable,~] = db.queryDB(fp, fields);
|
||||
@@ -60,7 +61,7 @@ for fl = 1:numel(fiber_len)
|
||||
|
||||
%
|
||||
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 = scatter(fl_filtered_.(x_variable), fl_filtered_.(['mean_',y_variable]), 36, 'Marker','o', 'MarkerEdgeColor',cols(fl,:), 'MarkerFaceColor',cols(fl,:), 'DisplayName',dname);
|
||||
|
||||
h2.DataTipTemplate.DataTipRows(end+1) = ...
|
||||
dataTipTextRow('run\_id', run_ids);
|
||||
@@ -93,4 +94,29 @@ 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);
|
||||
set(f, 'Position', newPos);
|
||||
|
||||
%% Average laser power over all measurements for each wavelength
|
||||
valid_rows = ~ismissing(dataTable.wavelength) & ...
|
||||
~ismissing(dataTable.power_laser);
|
||||
laser_measurements = dataTable(valid_rows, :);
|
||||
|
||||
laser_power_by_wavelength = groupsummary( ...
|
||||
laser_measurements, ...
|
||||
'wavelength', ...
|
||||
'mean', ...
|
||||
'power_mzm');
|
||||
laser_power_by_wavelength = sortrows( ...
|
||||
laser_power_by_wavelength, 'wavelength', 'ascend');
|
||||
|
||||
f_all = figure(4);
|
||||
clf(f_all);
|
||||
scatter(laser_power_by_wavelength.wavelength, ...
|
||||
laser_power_by_wavelength.mean_power_mzm, ...
|
||||
36, 'o', 'filled');
|
||||
grid on;
|
||||
xlabel('Wavelength in nm', 'FontSize', 12);
|
||||
ylabel('Average laser power in dB', 'FontSize', 12);
|
||||
title('Average laser power over all measurements', ...
|
||||
'FontSize', 14, 'FontWeight', 'bold');
|
||||
set(gca, 'FontSize', 11);
|
||||
@@ -1,5 +1,5 @@
|
||||
|
||||
filename = "Z:\2024\sioe\High Speed Messungen Oktober\bias_5km\PAMX_5km_20241025_204334_wh.mat";
|
||||
filename = "W:\labdata\sioe_labor\bias_5km\PAMX_5km_20241025_204334_wh.mat";
|
||||
|
||||
a = load(filename);
|
||||
wh = a.obj;
|
||||
@@ -30,7 +30,7 @@ for l = 1:numel(lambda_vals)
|
||||
for m = 1:numel(M_vals)
|
||||
|
||||
ber_ffe = wh.getStoValue('ber_ffe',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
ber = wh.getStoValue('ber_ffe',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
ber = wh.getStoValue('ber_collect',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
exfo = wh.getStoValue('exfo',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
|
||||
lb = wh.getStoValue('exfo',v_bias_vals,awg_vpp_vals(1),precomp_amp_max_vals(1),rop_atten_vals(1),M_vals(m),lambda_vals(l));
|
||||
|
||||
Reference in New Issue
Block a user