start with 400G analysis work
This commit is contained in:
Silas Oettinghaus
@@ -9,7 +9,7 @@ clear; clc;
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studyName = "block_update_sweep";
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selectedBlockUpdate = 1; % set [] to pool all block_update values
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selectedPamLevels = 6; % set [] to use all PAM levels in the query result
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selectedPamLevels = 4; % set [] to use all PAM levels in the query result
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algorithmSelection = table( ...
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["plain_ffe"; ...
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@@ -17,7 +17,7 @@ algorithmSelection = table( ...
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"a2_residual"; ...
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"a1_moving_average"; ...
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"dc_tracking"], ...
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[true; true; true; true; true], ...
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[true; false; false; false; true], ...
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'VariableNames', ["algorithm", "enabled"]);
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selectedAlgorithms = algorithmSelection.algorithm(algorithmSelection.enabled);
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@@ -286,6 +286,81 @@ for regimeIdx = 1:numel(regimeNames)
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/mpi/ber_vs_sir_" + regimeName + ".tikz","cleanfigure",1);
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end
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%% 4) Plot BER spread over SIR by delay/coherence regime and algorithm
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spreadTable = berSpreadSummary(cleanData, groupVars);
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fprintf("Calculated BER spread for %d regime/PAM/algorithm/SIR groups.\n", ...
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height(spreadTable));
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figure(); clf; hold on;
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for regimeIdx = 1:numel(regimeNames)
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regimeName = regimeNames(regimeIdx);
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lineStyle = pathRegimeLineStyle(regimeName);
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for algIdx = 1:numel(selectedAlgorithms)
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algorithmName = selectedAlgorithms(algIdx);
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algColor = algorithmColor(algorithmName);
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marker = algorithmMarker(algorithmName, algorithmMarkers);
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displayName = algorithmDisplayName(algorithmName);
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curveMask = spreadTable.path_regime == regimeName & ...
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spreadTable.algorithm == algorithmName;
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if ~any(curveMask)
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continue
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end
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curveTable = sortrows(spreadTable(curveMask, :), ...
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["pam_level", "sir_exact"]);
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for pamIdx = 1:numel(selectedPamLevels)
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pamLevel = selectedPamLevels(pamIdx);
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pamMask = curveTable.pam_level == pamLevel;
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if ~any(pamMask)
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continue
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end
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x = curveTable.sir_exact(pamMask).';
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y = curveTable.std_log10_BER(pamMask).';
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valid = isfinite(x) & isfinite(y);
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if ~any(valid)
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continue
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end
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if isscalar(selectedPamLevels)
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legendText = sprintf("%s, %s", displayName, regimeName);
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else
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legendText = sprintf("%s, %s, PAM %.0f", ...
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displayName, regimeName, pamLevel);
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end
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plot(x(valid), y(valid), ...
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"LineStyle", lineStyle, ...
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"Marker", marker, ...
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"MarkerSize", 3.5, ...
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"LineWidth", 1, ...
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"Color", algColor, ...
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"MarkerFaceColor", "w", ...
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"MarkerEdgeColor", algColor, ...
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"DisplayName", legendText);
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end
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end
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end
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xlabel("SIR (dB)");
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ylabel("Std. dev. of log_{10}(BER)");
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title("BER spread over SIR by delay regime and algorithm");
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xlim([15, 45]);
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grid on;
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box on;
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legend("Location", "northeast", "Interpreter", "none");
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if exist("beautifyBERplot", "file")
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beautifyBERplot("logscale", false, "setcolors", false, "setmarkers", false);
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end
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%% Local helpers
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function values = numericColumn(values)
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@@ -487,6 +562,38 @@ function yFit = fitLogBer(x, y, xFit, fitOrder)
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yFit = 10 .^ polyval(coeff, xFit);
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end
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function spreadTable = berSpreadSummary(cleanData, groupVars)
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summaryGroups = groupsummary(cleanData, groupVars);
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sirExactTable = groupsummary(cleanData, groupVars, "median", "sir_exact");
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summaryGroups = sortrows(summaryGroups, groupVars);
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sirExactTable = sortrows(sirExactTable, groupVars);
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summaryGroups.sir_exact = sirExactTable.median_sir_exact;
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stdLogBer = NaN(height(summaryGroups), 1);
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stdBer = NaN(height(summaryGroups), 1);
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for groupIdx = 1:height(summaryGroups)
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rowMask = true(height(cleanData), 1);
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for varIdx = 1:numel(groupVars)
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varName = groupVars(varIdx);
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rowMask = rowMask & cleanData.(char(varName)) == ...
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summaryGroups.(char(varName))(groupIdx);
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end
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berValues = cleanData.BER(rowMask);
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berValues = berValues(isfinite(berValues) & berValues > 0);
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if isempty(berValues)
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continue
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end
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stdLogBer(groupIdx) = std(log10(berValues), 0, "omitnan");
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stdBer(groupIdx) = std(berValues, 0, "omitnan");
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end
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spreadTable = summaryGroups;
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spreadTable.std_log10_BER = stdLogBer;
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spreadTable.std_BER = stdBer;
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end
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function label = algorithmDisplayName(algorithmName)
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algorithmName = string(algorithmName);
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switch algorithmName
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@@ -532,3 +639,18 @@ function marker = algorithmMarker(algorithmName, algorithmMarkers)
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end
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marker = algorithmMarkers{mod(markerIdx - 1, numel(algorithmMarkers)) + 1};
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end
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function lineStyle = pathRegimeLineStyle(regimeName)
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switch string(regimeName)
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case "0-1 m"
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lineStyle = "-";
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case "10-100 m"
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lineStyle = "--";
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case "300 m"
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lineStyle = ":";
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case "1000 m"
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lineStyle = "-.";
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otherwise
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lineStyle = "-";
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end
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end
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@@ -132,7 +132,7 @@ summaryTable = addBerStdBounds(summaryTable, cleanData, groupVars);
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blockUpdates = unique(cleanData.block_update(isfinite(cleanData.block_update))).';
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blockUpdates = sort(blockUpdates);
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helperBlockUpdates = unique(blockUpdates([1, end]), "stable");
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helperBlockUpdates = unique(blockUpdates([1, end-3]), "stable");
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requiredSirRows = table();
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for pamIdx = 1:numel(selectedPamLevels)
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@@ -214,14 +214,14 @@ for pamIdx = 1:numel(selectedPamLevels)
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sirValues, boundCenterBer, boundLowerBer, boundUpperBer, ...
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boundMode, boundaryPolyfitOrderMax);
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[hl, hp] = boundedline(xBand, centerBand, yBounds, ...
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'alpha', 'transparency', 0.1, ...
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'cmap', algColor, ...
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'nan', 'fill', ...
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'orientation', 'vert');
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set(hl, "LineStyle", "none", 'LineWidth', 1, "Marker", "none", ...
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"HandleVisibility", "off", "DisplayName", char(displayName));
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set(hp, "LineStyle", "-", "HandleVisibility", "off", "Marker", "none");
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% [hl, hp] = boundedline(xBand, centerBand, yBounds, ...
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% 'alpha', 'transparency', 0.1, ...
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% 'cmap', algColor, ...
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% 'nan', 'fill', ...
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% 'orientation', 'vert');
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% set(hl, "LineStyle", "none", 'LineWidth', 1, "Marker", "none", ...
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% "HandleVisibility", "off", "DisplayName", char(displayName));
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% set(hp, "LineStyle", "-", "HandleVisibility", "off", "Marker", "none");
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end
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plot(sirValues(valid), meanBer(valid), ...
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@@ -242,11 +242,11 @@ for pamIdx = 1:numel(selectedPamLevels)
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xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
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yFit = 10 .^ polyval(fitCoeff, xFit);
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% plot(xFit, yFit, ...
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% "LineStyle", "--", ...
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% "LineWidth", 1.1, ...
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% "Color", algColor, ...
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% "HandleVisibility", "off");
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plot(xFit, yFit, ...
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"LineStyle", "--", ...
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"LineWidth", 1.1, ...
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"Color", algColor, ...
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"HandleVisibility", "off");
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end
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end
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end
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Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,608 @@
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%% BER over SIR from saved MPI simulation warehouses grouped by linewidth
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% Loads a saved simulation warehouse and plots one BER-vs-SIR curve per
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% linewidth for each algorithm.
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% clear;
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% clc;
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%% Load data
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resultFile = "";
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if resultFile == ""
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resultDir = fullfile(fileparts(mfilename("fullpath")), "results");
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files = dir(fullfile(resultDir, "mpi_simulation_*.mat"));
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if isempty(files)
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error("PLOT_mpi_simulation_linewidth_vs_sir:NoResultFiles", ...
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"No mpi_simulation_*.mat files found in %s.", resultDir);
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end
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[~, newestIdx] = max([files.datenum]);
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resultFile = fullfile(files(newestIdx).folder, files(newestIdx).name);
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end
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loaded = load(resultFile, "wh", "simulation_config");
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wh = loaded.wh;
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fprintf("Loaded MPI simulation warehouse:\n%s\n", resultFile);
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wh.showInfo;
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%% Plot settings
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useBoundedLines = true;
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usePolyfit = true;
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polyfitOrderMax = 4;
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boundaryPolyfitOrderMax = 4;
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fecBerThreshold = 3.8e-3;
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maxBerForPlot = 0.1;
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crossingSirWindow = [15 40];
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boundMode = "fitStd";
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algorithmMarkers = {'o','square','diamond','^','v','>','<','pentagram'};
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%% Collect and clean
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cleanData = collectMpiSimulationBerRows(wh);
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cleanData = normalizeBerColumnName(cleanData);
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cleanData = cleanData(isfinite(cleanData.BER) & cleanData.BER > 0 & ...
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cleanData.BER < maxBerForPlot, :);
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if isempty(cleanData)
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warning("PLOT_mpi_simulation_linewidth_vs_sir:NoRows", ...
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"No valid BER rows remain for plotting.");
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return
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end
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if all(~isfinite(cleanData.laser_linewidth))
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if isfield(loaded, "simulation_config") && isfield(loaded.simulation_config, "laser_linewidth")
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cleanData.laser_linewidth(:) = loaded.simulation_config.laser_linewidth;
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else
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cleanData.laser_linewidth(:) = 0;
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end
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end
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cleanData.clean_keep = true(height(cleanData), 1);
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groupId = findgroups(cleanData.storage_name, cleanData.block_update, ...
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cleanData.laser_linewidth, cleanData.sir);
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for curGroup = unique(groupId(isfinite(groupId))).'
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rowMask = groupId == curGroup;
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berValues = cleanData.BER(rowMask);
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if nnz(rowMask) > 3
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cleanData.clean_keep(rowMask) = ~isoutlier(berValues);
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end
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end
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cleanData = cleanData(cleanData.clean_keep, :);
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groupVars = ["storage_name", "algorithm", "block_update", "laser_linewidth", "sir"];
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summaryTable = groupsummary(cleanData, groupVars, {"mean", "min", "max"}, "BER");
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summaryTable = sortrows(summaryTable, groupVars);
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summaryTable.sir_exact = summaryTable.sir;
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summaryTable = addBerStdBounds(summaryTable, cleanData, groupVars);
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selectedBlockUpdates = unique(cleanData.block_update(isfinite(cleanData.block_update))).';
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selectedAlgorithms = unique(cleanData.storage_name, "stable").';
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selectedLinewidths = unique(cleanData.laser_linewidth(isfinite(cleanData.laser_linewidth))).';
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selectedLinewidths = sort(selectedLinewidths);
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requiredSirRows = table();
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for blockIdx = 1:numel(selectedBlockUpdates)
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blockUpdate = selectedBlockUpdates(blockIdx);
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for algIdx = 1:numel(selectedAlgorithms)
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storageName = selectedAlgorithms(algIdx);
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algorithmName = string(summaryTable.algorithm(find(summaryTable.storage_name == storageName, 1, "first")));
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for linewidthIdx = 1:numel(selectedLinewidths)
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laserLinewidth = selectedLinewidths(linewidthIdx);
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curveMask = summaryTable.storage_name == storageName & ...
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summaryTable.block_update == blockUpdate & ...
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summaryTable.laser_linewidth == laserLinewidth;
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sirValues = summaryTable.sir_exact(curveMask).';
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meanBer = summaryTable.mean_BER(curveMask).';
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[~, ~, requiredSir, fitOrder] = fitBerAtFec( ...
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sirValues, meanBer, polyfitOrderMax, fecBerThreshold, crossingSirWindow);
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newRow = table(storageName, algorithmName, blockUpdate, ...
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laserLinewidth, requiredSir, fitOrder, nnz(isfinite(sirValues) & isfinite(meanBer)), ...
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'VariableNames', {'storage_name', 'algorithm', 'block_update', ...
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'laser_linewidth', 'required_sir', 'fit_order', 'n_points'});
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requiredSirRows = [requiredSirRows; newRow]; %#ok<AGROW>
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end
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end
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end
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fprintf("Cleaned to %d simulation BER rows across %d storage/block/linewidth/SIR groups.\n", ...
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height(cleanData), height(summaryTable));
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disp(groupcounts(cleanData, ["storage_name", "block_update", "laser_linewidth"]));
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disp(requiredSirRows);
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%% Plot BER vs SIR, linewidth as curve family
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for blockIdx = 1:numel(selectedBlockUpdates)
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blockUpdate = selectedBlockUpdates(blockIdx);
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figure();
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clf;
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tiledlayout(numel(selectedAlgorithms), 1, "TileSpacing", "compact");
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for algIdx = 1:numel(selectedAlgorithms)
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storageName = selectedAlgorithms(algIdx);
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nexttile;
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hold on;
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for linewidthIdx = 1:numel(selectedLinewidths)
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laserLinewidth = selectedLinewidths(linewidthIdx);
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lineColor = linewidthColor(linewidthIdx, numel(selectedLinewidths));
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marker = algorithmMarker(storageName, algorithmMarkers);
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displayName = sprintf("%s, %s", ...
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algorithmDisplayName(storageName), linewidthLabel(laserLinewidth));
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rawMask = cleanData.storage_name == storageName & ...
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cleanData.block_update == blockUpdate & ...
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cleanData.laser_linewidth == laserLinewidth;
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curveMask = summaryTable.storage_name == storageName & ...
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summaryTable.block_update == blockUpdate & ...
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summaryTable.laser_linewidth == laserLinewidth;
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if ~any(curveMask)
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continue
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end
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scatterRows = cleanData(rawMask, :);
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scatter(scatterRows.sir, scatterRows.BER, ...
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22, ...
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"Marker", ".", ...
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"MarkerEdgeColor", lineColor, ...
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"MarkerFaceColor", lineColor, ...
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"HandleVisibility", "off");
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sirValues = summaryTable.sir_exact(curveMask).';
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meanBer = summaryTable.mean_BER(curveMask).';
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boundCenterBer = summaryTable.std_center_BER(curveMask).';
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boundLowerBer = summaryTable.std_lower_BER(curveMask).';
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boundUpperBer = summaryTable.std_upper_BER(curveMask).';
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valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
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if useBoundedLines && exist("boundedline", "file") && any(valid)
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[xBand, centerBand, yBounds] = berStdBounds( ...
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sirValues, boundCenterBer, boundLowerBer, boundUpperBer, ...
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boundMode, boundaryPolyfitOrderMax);
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[hl, hp] = boundedline(xBand, centerBand, yBounds, ...
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'alpha', 'transparency', 0.08, ...
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'cmap', lineColor, ...
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'nan', 'fill', ...
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'orientation', 'vert');
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set(hl, "LineStyle", "none", "LineWidth", 1, "Marker", "none", ...
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"HandleVisibility", "off", "DisplayName", displayName);
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set(hp, "LineStyle", "-", "HandleVisibility", "off", "Marker", "none");
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end
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plot(sirValues(valid), meanBer(valid), ...
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"LineStyle", "-", ...
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"Marker", marker, ...
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"MarkerSize", 3, ...
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"LineWidth", 1, ...
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"Color", lineColor, ...
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"MarkerFaceColor", "w", ...
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"MarkerEdgeColor", lineColor, ...
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"DisplayName", displayName, ...
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"HandleVisibility", "on");
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if usePolyfit
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fitMask = valid & meanBer > 0;
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if nnz(fitMask) >= 2
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fitOrder = min(polyfitOrderMax, nnz(fitMask) - 1);
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fitCoeff = polyfit(sirValues(fitMask), log10(meanBer(fitMask)), fitOrder);
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xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
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yFit = 10 .^ polyval(fitCoeff, xFit);
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plot(xFit, yFit, ...
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"LineStyle", "--", ...
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"LineWidth", 1.1, ...
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"Color", lineColor, ...
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"HandleVisibility", "off");
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end
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end
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end
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yline(2.2e-4, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
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yline(fecBerThreshold, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
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yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
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title(sprintf("%s, block update %.0f", algorithmDisplayName(storageName), blockUpdate));
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xlabel("SIR (dB)");
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ylabel("BER");
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set(gca, "YScale", "log");
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ylim([9e-5, maxBerForPlot]);
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xlim(crossingSirWindow);
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grid on;
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box on;
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legend("Location", "northeast", "Interpreter", "none");
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if exist("beautifyBERplot", "file")
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beautifyBERplot("logscale", true, "setcolors", false, ...
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"setmarkers", false, "changemarkers", false);
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end
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end
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end
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%% Plot required SIR at FEC over linewidth
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for blockIdx = 1:numel(selectedBlockUpdates)
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blockUpdate = selectedBlockUpdates(blockIdx);
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figure();
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clf;
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hold on;
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for algIdx = 1:numel(selectedAlgorithms)
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storageName = selectedAlgorithms(algIdx);
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algColor = algorithmColor(storageName);
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marker = algorithmMarker(storageName, algorithmMarkers);
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displayName = algorithmDisplayName(storageName);
|
||||
rowMask = requiredSirRows.storage_name == storageName & ...
|
||||
requiredSirRows.block_update == blockUpdate;
|
||||
|
||||
x = requiredSirRows.laser_linewidth(rowMask).';
|
||||
y = requiredSirRows.required_sir(rowMask).';
|
||||
[x, sortIdx] = sort(x);
|
||||
y = y(sortIdx);
|
||||
valid = isfinite(x) & isfinite(y);
|
||||
|
||||
if ~any(valid)
|
||||
continue
|
||||
end
|
||||
|
||||
plot(x(valid), y(valid), ...
|
||||
"LineWidth", 1.4, ...
|
||||
"LineStyle", "-", ...
|
||||
"Marker", marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
"Color", algColor, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", algColor, ...
|
||||
"DisplayName", char(displayName));
|
||||
end
|
||||
|
||||
set(gca, "XScale", "log");
|
||||
xticks(selectedLinewidths);
|
||||
xticklabels(arrayfun(@linewidthLabel, selectedLinewidths, "UniformOutput", false));
|
||||
ylim(crossingSirWindow);
|
||||
grid on;
|
||||
box on;
|
||||
xlabel("Laser linewidth");
|
||||
ylabel(sprintf("Required SIR at BER = %.1e (dB)", fecBerThreshold));
|
||||
title(sprintf("Required SIR over linewidth, block update %.0f", blockUpdate));
|
||||
legend("Location", "best", "Interpreter", "none");
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", false, "setcolors", false, ...
|
||||
"setmarkers", false, "changemarkers", false);
|
||||
end
|
||||
end
|
||||
|
||||
%% Local helpers
|
||||
function data = collectMpiSimulationBerRows(wh)
|
||||
storageNames = string(fieldnames(wh.sto));
|
||||
data = table();
|
||||
|
||||
for storageIdx = 1:numel(storageNames)
|
||||
storageName = storageNames(storageIdx);
|
||||
storage = wh.sto.(char(storageName));
|
||||
|
||||
for linIdx = 1:numel(storage)
|
||||
package = storage{linIdx};
|
||||
ber = extractPackageBer(package);
|
||||
if ~isfinite(ber)
|
||||
continue
|
||||
end
|
||||
|
||||
[physValues, physNames] = wh.getPhysIndicesByLinIndex(linIdx);
|
||||
phys = struct();
|
||||
for physIdx = 1:numel(physNames)
|
||||
phys.(char(physNames{physIdx})) = physValues{physIdx};
|
||||
end
|
||||
|
||||
algorithm = extractPackageAlgorithm(package, storageName);
|
||||
newRow = table( ...
|
||||
storageName, ...
|
||||
algorithm, ...
|
||||
readPhysValue(phys, "sir", NaN), ...
|
||||
readPhysValue(phys, "block_update", NaN), ...
|
||||
readPhysValue(phys, "random_key", NaN), ...
|
||||
readPhysValue(phys, "laser_linewidth", NaN), ...
|
||||
ber, ...
|
||||
'VariableNames', {'storage_name', 'algorithm', 'sir', ...
|
||||
'block_update', 'random_key', 'laser_linewidth', 'BER'});
|
||||
data = [data; newRow]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function data = normalizeBerColumnName(data)
|
||||
variableNames = string(data.Properties.VariableNames);
|
||||
if ismember("BER", variableNames)
|
||||
return
|
||||
end
|
||||
|
||||
if ismember("ber", variableNames)
|
||||
data.Properties.VariableNames(variableNames == "ber") = {'BER'};
|
||||
return
|
||||
end
|
||||
|
||||
error("PLOT_mpi_simulation_linewidth_vs_sir:MissingBerColumn", ...
|
||||
"Could not find a BER or ber column in the simulation BER table.");
|
||||
end
|
||||
|
||||
function value = readPhysValue(phys, name, defaultValue)
|
||||
if isfield(phys, name)
|
||||
value = phys.(name);
|
||||
else
|
||||
value = defaultValue;
|
||||
end
|
||||
end
|
||||
|
||||
function ber = extractPackageBer(package)
|
||||
ber = NaN;
|
||||
if isempty(package)
|
||||
return
|
||||
end
|
||||
|
||||
if iscell(package)
|
||||
package = package{1};
|
||||
end
|
||||
|
||||
if isstruct(package) && isfield(package, "metrics")
|
||||
metrics = package.metrics;
|
||||
if isstruct(metrics) && isfield(metrics, "BER")
|
||||
ber = metrics.BER;
|
||||
elseif isobject(metrics) && isprop(metrics, "BER")
|
||||
ber = metrics.BER;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function algorithm = extractPackageAlgorithm(package, fallbackName)
|
||||
algorithm = fallbackName;
|
||||
if isempty(package)
|
||||
return
|
||||
end
|
||||
|
||||
if iscell(package)
|
||||
package = package{1};
|
||||
end
|
||||
|
||||
if isstruct(package) && isfield(package, "mpi_reduction_config") && ...
|
||||
isfield(package.mpi_reduction_config, "algorithm")
|
||||
algorithm = string(package.mpi_reduction_config.algorithm);
|
||||
end
|
||||
end
|
||||
|
||||
function summaryTable = addBerStdBounds(summaryTable, cleanData, groupVars)
|
||||
nGroups = height(summaryTable);
|
||||
stdCenter = NaN(nGroups, 1);
|
||||
stdLower = NaN(nGroups, 1);
|
||||
stdUpper = NaN(nGroups, 1);
|
||||
|
||||
for groupIdx = 1:nGroups
|
||||
rowMask = true(height(cleanData), 1);
|
||||
for varIdx = 1:numel(groupVars)
|
||||
varName = groupVars(varIdx);
|
||||
rowMask = rowMask & cleanData.(char(varName)) == summaryTable.(char(varName))(groupIdx);
|
||||
end
|
||||
|
||||
[stdCenter(groupIdx), stdLower(groupIdx), stdUpper(groupIdx)] = ...
|
||||
logBerMeanStdInterval(cleanData.BER(rowMask));
|
||||
end
|
||||
|
||||
summaryTable.std_center_BER = stdCenter;
|
||||
summaryTable.std_lower_BER = stdLower;
|
||||
summaryTable.std_upper_BER = stdUpper;
|
||||
end
|
||||
|
||||
function [centerBer, lowerBer, upperBer] = logBerMeanStdInterval(berValues)
|
||||
berValues = berValues(isfinite(berValues) & berValues > 0);
|
||||
if isempty(berValues)
|
||||
centerBer = NaN;
|
||||
lowerBer = NaN;
|
||||
upperBer = NaN;
|
||||
return
|
||||
end
|
||||
|
||||
logBer = log10(berValues(:));
|
||||
centerLog = mean(logBer, "omitnan");
|
||||
stdLog = std(logBer, 0, "omitnan");
|
||||
|
||||
centerBer = 10 .^ centerLog;
|
||||
lowerBer = 10 .^ (centerLog - stdLog);
|
||||
upperBer = 10 .^ (centerLog + stdLog);
|
||||
end
|
||||
|
||||
function [xBand, centerBand, yBounds] = berStdBounds(sirValues, centerBer, lowerBer, upperBer, boundMode, maxOrder)
|
||||
if boundMode == "directStd"
|
||||
[xBand, centerBand, yBounds] = directBerBounds(sirValues, centerBer, lowerBer, upperBer);
|
||||
return
|
||||
end
|
||||
|
||||
[xBand, centerBand, yBounds] = fittedBerBounds(sirValues, centerBer, lowerBer, upperBer, maxOrder);
|
||||
end
|
||||
|
||||
function [xBand, centerBand, yBounds] = directBerBounds(sirValues, centerBer, lowerBer, upperBer)
|
||||
valid = isfinite(sirValues) & isfinite(centerBer) & isfinite(lowerBer) & ...
|
||||
isfinite(upperBer) & centerBer > 0 & lowerBer > 0 & upperBer > 0;
|
||||
|
||||
xBand = sirValues(valid).';
|
||||
centerBand = centerBer(valid).';
|
||||
lowerBand = lowerBer(valid).';
|
||||
upperBand = upperBer(valid).';
|
||||
|
||||
[xBand, orderIdx] = sort(xBand(:));
|
||||
centerBand = centerBand(orderIdx);
|
||||
lowerBand = lowerBand(orderIdx);
|
||||
upperBand = upperBand(orderIdx);
|
||||
|
||||
lowerTmp = min(lowerBand, upperBand);
|
||||
upperBand = max(lowerBand, upperBand);
|
||||
lowerBand = lowerTmp;
|
||||
|
||||
centerBand = min(max(centerBand, lowerBand), upperBand);
|
||||
yBounds = [max(centerBand - lowerBand, 0), max(upperBand - centerBand, 0)];
|
||||
end
|
||||
|
||||
function [xBand, centerBand, yBounds] = fittedBerBounds(sirValues, meanBer, minBer, maxBer, maxOrder)
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & isfinite(minBer) & ...
|
||||
isfinite(maxBer) & meanBer > 0 & minBer > 0 & maxBer > 0;
|
||||
|
||||
x = sirValues(valid);
|
||||
yMean = meanBer(valid);
|
||||
yMin = minBer(valid);
|
||||
yMax = maxBer(valid);
|
||||
|
||||
if numel(x) < 2
|
||||
xBand = x(:);
|
||||
centerBand = yMean(:);
|
||||
yBounds = [max(yMean(:) - yMin(:), 0), max(yMax(:) - yMean(:), 0)];
|
||||
return
|
||||
end
|
||||
|
||||
[x, orderIdx] = sort(x(:));
|
||||
yMean = yMean(orderIdx);
|
||||
yMin = yMin(orderIdx);
|
||||
yMax = yMax(orderIdx);
|
||||
|
||||
xBand = linspace(min(x), max(x), 300).';
|
||||
fitOrder = min(maxOrder, numel(unique(x)) - 1);
|
||||
|
||||
if fitOrder < 1
|
||||
centerBand = interp1(x, yMean, xBand, "linear", "extrap");
|
||||
lowerBand = interp1(x, yMin, xBand, "linear", "extrap");
|
||||
upperBand = interp1(x, yMax, xBand, "linear", "extrap");
|
||||
else
|
||||
centerBand = fitLogBer(x, yMean, xBand, fitOrder);
|
||||
lowerBand = fitLogBer(x, yMin, xBand, fitOrder);
|
||||
upperBand = fitLogBer(x, yMax, xBand, fitOrder);
|
||||
end
|
||||
|
||||
lowerTmp = min(lowerBand, upperBand);
|
||||
upperBand = max(lowerBand, upperBand);
|
||||
lowerBand = lowerTmp;
|
||||
|
||||
centerBand = min(max(centerBand, lowerBand), upperBand);
|
||||
yBounds = [max(centerBand - lowerBand, 0), max(upperBand - centerBand, 0)];
|
||||
end
|
||||
|
||||
function yFit = fitLogBer(x, y, xFit, fitOrder)
|
||||
coeff = polyfit(x, log10(y), fitOrder);
|
||||
yFit = 10 .^ polyval(coeff, xFit);
|
||||
end
|
||||
|
||||
function [xFit, yFit, requiredSir, fitOrder] = fitBerAtFec( ...
|
||||
sirValues, meanBer, polyfitOrderMax, fecBerThreshold, crossingSirWindow)
|
||||
|
||||
xFit = NaN;
|
||||
yFit = NaN;
|
||||
requiredSir = NaN;
|
||||
fitOrder = NaN;
|
||||
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0 & ...
|
||||
sirValues >= crossingSirWindow(1) & sirValues <= crossingSirWindow(2);
|
||||
if nnz(valid) < 2
|
||||
return
|
||||
end
|
||||
|
||||
sirValues = sirValues(valid);
|
||||
meanBer = meanBer(valid);
|
||||
[sirValues, sortIdx] = sort(sirValues);
|
||||
meanBer = meanBer(sortIdx);
|
||||
|
||||
fitOrder = min(polyfitOrderMax, nnz(valid) - 1);
|
||||
fitCoeff = polyfit(sirValues, log10(meanBer), fitOrder);
|
||||
xFit = linspace(max(min(sirValues), crossingSirWindow(1)), ...
|
||||
min(max(sirValues), crossingSirWindow(2)), 300);
|
||||
yFit = 10 .^ polyval(fitCoeff, xFit);
|
||||
|
||||
thresholdMask = isfinite(yFit) & yFit <= fecBerThreshold;
|
||||
if ~any(thresholdMask)
|
||||
return
|
||||
end
|
||||
|
||||
firstThresholdIdx = find(thresholdMask, 1, "first");
|
||||
if firstThresholdIdx == 1
|
||||
requiredSir = xFit(firstThresholdIdx);
|
||||
return
|
||||
end
|
||||
|
||||
xPair = xFit(firstThresholdIdx - 1:firstThresholdIdx);
|
||||
yPair = log10(yFit(firstThresholdIdx - 1:firstThresholdIdx));
|
||||
if all(isfinite(yPair)) && diff(yPair) ~= 0
|
||||
requiredSir = interp1(yPair, xPair, log10(fecBerThreshold), ...
|
||||
"linear", "extrap");
|
||||
else
|
||||
requiredSir = xFit(firstThresholdIdx);
|
||||
end
|
||||
|
||||
if requiredSir < crossingSirWindow(1) || requiredSir > crossingSirWindow(2)
|
||||
requiredSir = NaN;
|
||||
end
|
||||
end
|
||||
|
||||
function label = algorithmDisplayName(algorithmName)
|
||||
algorithmName = string(algorithmName);
|
||||
switch algorithmName
|
||||
case {"plain_ffe", "conventional_ffe"}
|
||||
label = "FFE only";
|
||||
case "a2_tracked_levels"
|
||||
label = "ACT";
|
||||
case "a2_residual"
|
||||
label = "L-DCA";
|
||||
case "a1_moving_average"
|
||||
label = "DCA";
|
||||
case "dc_tracking"
|
||||
label = "DCT";
|
||||
otherwise
|
||||
label = algorithmName;
|
||||
end
|
||||
end
|
||||
|
||||
function color = algorithmColor(algorithmName)
|
||||
algorithmName = string(algorithmName);
|
||||
switch algorithmName
|
||||
case {"plain_ffe", "conventional_ffe"}
|
||||
color = [0.3467 0.5360 0.6907];
|
||||
case "a2_tracked_levels"
|
||||
color = [0.9153 0.2816 0.2878];
|
||||
case "a2_residual"
|
||||
color = [0.4416 0.7490 0.4322];
|
||||
case "a1_moving_average"
|
||||
color = [1.0000 0.5984 0.2000];
|
||||
case "dc_tracking"
|
||||
color = [0.6769 0.4447 0.7114];
|
||||
otherwise
|
||||
color = [0 0 0];
|
||||
end
|
||||
end
|
||||
|
||||
function color = linewidthColor(linewidthIdx, nLinewidths)
|
||||
if nLinewidths <= 1
|
||||
color = [0.3467 0.5360 0.6907];
|
||||
return
|
||||
end
|
||||
|
||||
if exist("cbrewer2", "file")
|
||||
colors = cbrewer2("Set1", max(nLinewidths, 3));
|
||||
else
|
||||
colors = lines(nLinewidths);
|
||||
end
|
||||
|
||||
color = colors(linewidthIdx, :);
|
||||
end
|
||||
|
||||
function label = linewidthLabel(laserLinewidth)
|
||||
if abs(laserLinewidth) >= 1e6
|
||||
label = sprintf("%.3g MHz", laserLinewidth * 1e-6);
|
||||
elseif abs(laserLinewidth) >= 1e3
|
||||
label = sprintf("%.3g kHz", laserLinewidth * 1e-3);
|
||||
else
|
||||
label = sprintf("%.3g Hz", laserLinewidth);
|
||||
end
|
||||
end
|
||||
|
||||
function marker = algorithmMarker(algorithmName, algorithmMarkers)
|
||||
algorithmOrder = ["conventional_ffe", "dc_tracking", "a2_tracked_levels", ...
|
||||
"a2_residual", "a1_moving_average"];
|
||||
markerIdx = find(algorithmOrder == string(algorithmName), 1);
|
||||
if isempty(markerIdx)
|
||||
markerIdx = 1;
|
||||
end
|
||||
marker = algorithmMarkers{mod(markerIdx - 1, numel(algorithmMarkers)) + 1};
|
||||
end
|
||||
214
projects/Diss/MPI_revisit/simulation/mpi_simulation_worker.m
Normal file
214
projects/Diss/MPI_revisit/simulation/mpi_simulation_worker.m
Normal file
@@ -0,0 +1,214 @@
|
||||
function output = mpi_simulation_worker(userParameters, simulation_config)
|
||||
%MPI_SIMULATION_WORKER Build one decorrelated MPI point and run the DSP recipe.
|
||||
|
||||
arguments
|
||||
userParameters struct
|
||||
simulation_config struct
|
||||
end
|
||||
|
||||
config = applyDefaults(simulation_config);
|
||||
|
||||
[Scpe_sig_raw, Symbols, Tx_bits, dataTable] = buildMpiScopeSignal(userParameters, config);
|
||||
|
||||
output = config.recipe(Scpe_sig_raw, Symbols, Tx_bits, ...
|
||||
"fsym", config.fsym, ...
|
||||
"M", config.M, ...
|
||||
"duob_mode", config.duob_mode, ...
|
||||
"dataTable", dataTable, ...
|
||||
"userParameters", userParameters, ...
|
||||
"debug_plots", config.debug_plots);
|
||||
end
|
||||
|
||||
function config = applyDefaults(config)
|
||||
defaults = struct( ...
|
||||
"M", 4, ...
|
||||
"fsym", 112e9, ...
|
||||
"mpi_path_meter", 1000, ...
|
||||
"laser_linewidth", 150e3, ...
|
||||
"recipe", @mpi_recipe_dev, ...
|
||||
"debug_plots", false, ...
|
||||
"fdac", 256e9, ...
|
||||
"fadc", 256e9, ...
|
||||
"kover", 16, ...
|
||||
"random_key", 1, ...
|
||||
"rcalpha", 0.05, ...
|
||||
"duob_mode", db_mode.no_db, ...
|
||||
"vbias_rel", 0.5, ...
|
||||
"u_pi", 3, ...
|
||||
"laser_wavelength", 1293, ...
|
||||
"link_length_km", 1, ...
|
||||
"rop", -9, ...
|
||||
"rx_bwl", 80e9, ...
|
||||
"scope_bwl", 110e9, ...
|
||||
"alpha", 0);
|
||||
|
||||
names = fieldnames(defaults);
|
||||
for nameIdx = 1:numel(names)
|
||||
name = names{nameIdx};
|
||||
if ~isfield(config, name) || isempty(config.(name))
|
||||
config.(name) = defaults.(name);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function [Scpe_sig, Symbols, Tx_bits, dataTable] = buildMpiScopeSignal(userParameters, config)
|
||||
sir = readUserParameter(userParameters, "sir", 30);
|
||||
randomKey = readUserParameter(userParameters, "random_key", config.random_key);
|
||||
laserLinewidth = readUserParameter(userParameters, "laser_linewidth", config.laser_linewidth);
|
||||
config.laser_linewidth = laserLinewidth;
|
||||
|
||||
Pform = Pulseformer( ...
|
||||
"fsym", config.fsym, ...
|
||||
"fdac", 4 * config.fsym, ...
|
||||
"pulse", "rrc", ...
|
||||
"pulselength", 16, ...
|
||||
"alpha", config.rcalpha);
|
||||
|
||||
vbias = -config.vbias_rel * config.u_pi;
|
||||
mainSource = buildOpticalSource(Pform, config, randomKey, randomKey + 1, vbias);
|
||||
interferenceSource = buildOpticalSource(Pform, config, randomKey + 100000, ...
|
||||
randomKey + 100001, vbias);
|
||||
|
||||
Symbols = mainSource.Symbols;
|
||||
Tx_bits = mainSource.Tx_bits;
|
||||
Opt_sig = applyDecorrelatedMpi(mainSource.Opt_sig, interferenceSource.Opt_sig, ...
|
||||
sir, config.link_length_km);
|
||||
|
||||
Rx_sig = Amplifier( ...
|
||||
"amp_mode", "ideal_no_noise", ...
|
||||
"gain_mode", "output_power", ...
|
||||
"amplification_db", config.rop).process(Opt_sig);
|
||||
|
||||
Rx_sig = Photodiode( ...
|
||||
"fsimu", config.fdac * config.kover, ...
|
||||
"dark_current", 2e-08, ...
|
||||
"responsivity", 1, ...
|
||||
"temperature", 20, ...
|
||||
"nep", 1.8e-11).process(Rx_sig);
|
||||
Rx_sig.signal = real(Rx_sig.signal);
|
||||
|
||||
Rx_sig = Filter( ...
|
||||
"filtdegree", 4, ...
|
||||
"f_cutoff", config.rx_bwl, ...
|
||||
"fs", config.fdac * config.kover, ...
|
||||
"filterType", filtertypes.butterworth, ...
|
||||
"active", true).process(Rx_sig);
|
||||
Rx_sig.signal = real(Rx_sig.signal);
|
||||
|
||||
scopeFilter = Filter( ...
|
||||
"filtdegree", 4, ...
|
||||
"f_cutoff", config.scope_bwl, ...
|
||||
"fs", config.fadc, ...
|
||||
"filterType", filtertypes.butterworth, ...
|
||||
"active", true);
|
||||
|
||||
Scpe_sig = Scope( ...
|
||||
"fsimu", config.fdac * config.kover, ...
|
||||
"fadc", config.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", scopeFilter).process(Rx_sig);
|
||||
Scpe_sig.signal = real(Scpe_sig.signal);
|
||||
|
||||
HighpassFilter = Filter( ...
|
||||
"filtdegree", 6, ...
|
||||
"f_cutoff", 1e6, ...
|
||||
"fs", config.fadc, ...
|
||||
"filterType", filtertypes.butterworth, ...
|
||||
"active", true, "lowpass",0);
|
||||
|
||||
Scpe_sig = HighpassFilter.process(Scpe_sig);
|
||||
|
||||
dataTable = table( ...
|
||||
sir, ...
|
||||
config.fsym, ...
|
||||
config.M, ...
|
||||
config.mpi_path_meter, ...
|
||||
laserLinewidth, ...
|
||||
randomKey, ...
|
||||
'VariableNames', {'sir', 'symbolrate', 'pam_level', ...
|
||||
'interference_path_length', 'laser_linewidth', 'random_key'});
|
||||
end
|
||||
|
||||
function source = buildOpticalSource(Pform, config, sourceRandomKey, laserRandomKey, vbias)
|
||||
[Digi_sig, Symbols, Tx_bits] = PAMsource( ...
|
||||
"fsym", config.fsym, ...
|
||||
"M", config.M, ...
|
||||
"order", 18, ...
|
||||
"useprbs", 0, ...
|
||||
"fs_out", config.fdac, ...
|
||||
"applyclipping", 0, ...
|
||||
"clipfactor", 1.5, ...
|
||||
"applypulseform", 1, ...
|
||||
"pulseformer", Pform, ...
|
||||
"randkey", sourceRandomKey, ...
|
||||
"duobinary_mode", config.duob_mode, ...
|
||||
"mrds_code", 0, ...
|
||||
"mrds_blocklength", 512).process();
|
||||
|
||||
El_sig = M8199A("kover", config.kover).process(Digi_sig);
|
||||
|
||||
|
||||
El_sig = Filter("f_cutoff",65e9,"filterType","butterworth","filtdegree",4,"fs",El_sig.fs).process(El_sig);
|
||||
|
||||
El_sig = El_sig.normalize("mode", "oneone");
|
||||
|
||||
scaling = 0.6 * (config.u_pi / 2 - abs(vbias - config.u_pi / 2));
|
||||
El_sig = El_sig .* scaling;
|
||||
|
||||
Opt_sig = EML( ...
|
||||
"mode", eml_mode.im_cosinus, ...
|
||||
"power", 3, ...
|
||||
"fsimu", El_sig.fs, ...
|
||||
"lambda", config.laser_wavelength, ...
|
||||
"bias", vbias, ...
|
||||
"u_pi", config.u_pi, ...
|
||||
"linewidth", config.laser_linewidth, ...
|
||||
"randomkey", laserRandomKey, ...
|
||||
"alpha", config.alpha).process(El_sig);
|
||||
|
||||
source = struct( ...
|
||||
"Opt_sig", Opt_sig, ...
|
||||
"Symbols", Symbols, ...
|
||||
"Tx_bits", Tx_bits);
|
||||
end
|
||||
|
||||
function Opt_sig = applyDecorrelatedMpi(main_sig, interference_sig, sir, linkLengthKm)
|
||||
interference_sig = Amplifier( ...
|
||||
"amp_mode", "ideal_no_noise", ...
|
||||
"gain_mode", "output_power", ...
|
||||
"amplification_db", main_sig.power - sir).process(interference_sig);
|
||||
|
||||
if numel(main_sig.signal) ~= numel(interference_sig.signal)
|
||||
minLength = min(numel(main_sig.signal), numel(interference_sig.signal));
|
||||
main_sig.signal = main_sig.signal(1:minLength);
|
||||
interference_sig.signal = interference_sig.signal(1:minLength);
|
||||
end
|
||||
combined_sig = main_sig + interference_sig;
|
||||
|
||||
Opt_sig = Fiber( ...
|
||||
"fsimu", combined_sig.fs, ...
|
||||
"fiber_length", linkLengthKm, ...
|
||||
"alpha", 0.3, ...
|
||||
"D", 0, ...
|
||||
"lambda0", 1310, ...
|
||||
"gamma", 0, ...
|
||||
"Dslope", 0.07).process(combined_sig);
|
||||
end
|
||||
|
||||
function value = readUserParameter(userParameters, name, defaultValue)
|
||||
if isfield(userParameters, name)
|
||||
value = userParameters.(name);
|
||||
else
|
||||
value = defaultValue;
|
||||
end
|
||||
end
|
||||
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476
projects/Diss/MPI_revisit/simulation/run_mpi_simulation_recipe.m
Normal file
476
projects/Diss/MPI_revisit/simulation/run_mpi_simulation_recipe.m
Normal file
@@ -0,0 +1,476 @@
|
||||
%RUN_MPI_SIMULATION_RECIPE Queue MPI simulation points and store DSP packages.
|
||||
|
||||
clear;
|
||||
% clc;
|
||||
|
||||
%% Fixed experiment-like simulation setup
|
||||
simulation_config = struct();
|
||||
simulation_config.M = 4;
|
||||
simulation_config.fsym = 112e9;
|
||||
simulation_config.mpi_path_meter = 1000;
|
||||
simulation_config.laser_linewidth = 150e3;%[150e3 150e3 250e3 500e3 750e3 1e6 5e6 10e6 20e6 50e6];
|
||||
simulation_config.laser_linewidths = simulation_config.laser_linewidth;
|
||||
simulation_config.recipe = @mpi_recipe_dev;
|
||||
simulation_config.debug_plots = true;
|
||||
simulation_config.waitbar = true;
|
||||
simulation_config.processing_mode = processingMode.parallel;
|
||||
simulation_config.num_workers = 0;
|
||||
simulation_config.random_keys = 1:10;
|
||||
|
||||
simulation_config.fdac = 256e9;
|
||||
simulation_config.fadc = 256e9;
|
||||
simulation_config.kover = 16;
|
||||
simulation_config.random_key = 1;
|
||||
simulation_config.rcalpha = 0.05;
|
||||
simulation_config.duob_mode = db_mode.no_db;
|
||||
simulation_config.vbias_rel = 0.5;
|
||||
simulation_config.u_pi = 3;
|
||||
simulation_config.laser_wavelength = 1310;
|
||||
simulation_config.link_length_km = 0.5;
|
||||
simulation_config.rop = -7.5;
|
||||
simulation_config.rx_bwl = 70e9;
|
||||
simulation_config.scope_bwl = 70e9;
|
||||
simulation_config.alpha = 0;
|
||||
|
||||
%% Warehouse sweep setup
|
||||
sweep_params = struct();
|
||||
sweep_params.sir = 15:3:45;
|
||||
sweep_params.block_update = 1;
|
||||
sweep_params.random_key = simulation_config.random_keys;
|
||||
sweep_params.laser_linewidth = simulation_config.laser_linewidths;
|
||||
|
||||
wh = DataStorage(sweep_params);
|
||||
|
||||
fprintf("Requested %d MPI simulation job(s).\n", wh.getLastLinIndice());
|
||||
|
||||
%% Run queued simulations
|
||||
[results, wh] = submitMpiSimulationJobs(wh, simulation_config, ...
|
||||
"mode", simulation_config.processing_mode, ...
|
||||
"waitbar", simulation_config.waitbar, ...
|
||||
"numWorkers", simulation_config.num_workers);
|
||||
|
||||
%% Save result artifact
|
||||
result_dir = fullfile(fileparts(mfilename("fullpath")), "results");
|
||||
if ~exist(result_dir, "dir")
|
||||
mkdir(result_dir);
|
||||
end
|
||||
|
||||
timestamp = string(datetime("now", "Format", "yyyyMMdd_HHmmss"));
|
||||
result_file = fullfile(result_dir, "mpi_simulation_" + timestamp + ".mat");
|
||||
save(result_file, "wh", "simulation_config", "results");
|
||||
fprintf("Saved MPI simulation warehouse to:\n%s\n", result_file);
|
||||
|
||||
%% BER inspection
|
||||
plotMpiSimulationBer(wh);
|
||||
|
||||
function plotMpiSimulationBer(wh)
|
||||
storageNames = string(fieldnames(wh.sto));
|
||||
if isempty(storageNames)
|
||||
warning("run_mpi_simulation_recipe:NoStorage", ...
|
||||
"The warehouse does not contain any stored DSP packages.");
|
||||
return
|
||||
end
|
||||
|
||||
useBoundedLines = true;
|
||||
usePolyfit = true;
|
||||
polyfitOrderMax = 4;
|
||||
boundaryPolyfitOrderMax = 4;
|
||||
fecBerThreshold = 3.8e-3;
|
||||
maxBerForPlot = 0.1;
|
||||
boundMode = "fitStd";
|
||||
algorithmMarkers = {'o','square','diamond','^','v','>','<','pentagram'};
|
||||
|
||||
cleanData = collectMpiSimulationBerRows(wh);
|
||||
cleanData = normalizeBerColumnName(cleanData);
|
||||
cleanData = cleanData(isfinite(cleanData.BER) & cleanData.BER > 0 & ...
|
||||
cleanData.BER < maxBerForPlot, :);
|
||||
|
||||
if isempty(cleanData)
|
||||
warning("run_mpi_simulation_recipe:NoBerRows", ...
|
||||
"No valid BER rows remain for plotting.");
|
||||
return
|
||||
end
|
||||
|
||||
cleanData.clean_keep = true(height(cleanData), 1);
|
||||
groupId = findgroups(cleanData.storage_name, cleanData.block_update, cleanData.sir);
|
||||
for curGroup = unique(groupId(isfinite(groupId))).'
|
||||
rowMask = groupId == curGroup;
|
||||
berValues = cleanData.BER(rowMask);
|
||||
if nnz(rowMask) > 3
|
||||
cleanData.clean_keep(rowMask) = ~isoutlier(berValues);
|
||||
end
|
||||
end
|
||||
cleanData = cleanData(cleanData.clean_keep, :);
|
||||
|
||||
groupVars = ["storage_name", "algorithm", "block_update", "sir"];
|
||||
summaryTable = groupsummary(cleanData, groupVars, {"mean", "min", "max"}, "BER");
|
||||
summaryTable = sortrows(summaryTable, groupVars);
|
||||
summaryTable.sir_exact = summaryTable.sir;
|
||||
summaryTable = addBerStdBounds(summaryTable, cleanData, groupVars);
|
||||
|
||||
selectedBlockUpdates = unique(cleanData.block_update(isfinite(cleanData.block_update))).';
|
||||
selectedAlgorithms = unique(cleanData.storage_name, "stable").';
|
||||
|
||||
fprintf("Cleaned to %d simulation BER rows across %d storage/block/SIR groups.\n", ...
|
||||
height(cleanData), height(summaryTable));
|
||||
disp(groupcounts(cleanData, ["storage_name", "block_update"]));
|
||||
|
||||
figure();
|
||||
clf;
|
||||
tiledlayout(numel(selectedBlockUpdates), 1, "TileSpacing", "compact");
|
||||
|
||||
for blockIdx = 1:numel(selectedBlockUpdates)
|
||||
blockUpdate = selectedBlockUpdates(blockIdx);
|
||||
nexttile; hold on;
|
||||
|
||||
for algIdx = 1:numel(selectedAlgorithms)
|
||||
storageName = selectedAlgorithms(algIdx);
|
||||
algColor = algorithmColor(storageName);
|
||||
marker = algorithmMarker(storageName, algorithmMarkers);
|
||||
displayName = algorithmDisplayName(storageName);
|
||||
|
||||
rawMask = cleanData.storage_name == storageName & ...
|
||||
cleanData.block_update == blockUpdate;
|
||||
curveMask = summaryTable.storage_name == storageName & ...
|
||||
summaryTable.block_update == blockUpdate;
|
||||
|
||||
if ~any(curveMask)
|
||||
continue
|
||||
end
|
||||
|
||||
scatterRows = cleanData(rawMask, :);
|
||||
scatter(scatterRows.sir, scatterRows.BER, ...
|
||||
26, ...
|
||||
"Marker", ".", ...
|
||||
"MarkerEdgeColor", algColor, ...
|
||||
"MarkerFaceColor", algColor, ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
sirValues = summaryTable.sir_exact(curveMask).';
|
||||
meanBer = summaryTable.mean_BER(curveMask).';
|
||||
boundCenterBer = summaryTable.std_center_BER(curveMask).';
|
||||
boundLowerBer = summaryTable.std_lower_BER(curveMask).';
|
||||
boundUpperBer = summaryTable.std_upper_BER(curveMask).';
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
|
||||
|
||||
if useBoundedLines && exist("boundedline", "file") && any(valid)
|
||||
[xBand, centerBand, yBounds] = berStdBounds( ...
|
||||
sirValues, boundCenterBer, boundLowerBer, boundUpperBer, ...
|
||||
boundMode, boundaryPolyfitOrderMax);
|
||||
|
||||
[hl, hp] = boundedline(xBand, centerBand, yBounds, ...
|
||||
'alpha', 'transparency', 0.1, ...
|
||||
'cmap', algColor, ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
set(hl, "LineStyle", "none", "LineWidth", 1, "Marker", "none", ...
|
||||
"HandleVisibility", "off", "DisplayName", char(displayName));
|
||||
set(hp, "LineStyle", "-", "HandleVisibility", "off", "Marker", "none");
|
||||
end
|
||||
|
||||
plot(sirValues(valid), meanBer(valid), ...
|
||||
"LineStyle", "-", ...
|
||||
"Marker", marker, ...
|
||||
"MarkerSize", 3, ...
|
||||
"LineWidth", 1, ...
|
||||
"Color", algColor, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", algColor, ...
|
||||
"DisplayName", char(displayName), ...
|
||||
"HandleVisibility", "on");
|
||||
|
||||
if usePolyfit
|
||||
fitMask = valid & meanBer > 0;
|
||||
if nnz(fitMask) >= 2
|
||||
fitOrder = min(polyfitOrderMax, nnz(fitMask) - 1);
|
||||
fitCoeff = polyfit(sirValues(fitMask), log10(meanBer(fitMask)), fitOrder);
|
||||
xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
|
||||
yFit = 10 .^ polyval(fitCoeff, xFit);
|
||||
plot(xFit, yFit, ...
|
||||
"LineStyle", "--", ...
|
||||
"LineWidth", 1.1, ...
|
||||
"Color", algColor, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
yline(2.2e-4, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(fecBerThreshold, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
|
||||
title(sprintf("Simulated MPI, block update %.0f", blockUpdate));
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
set(gca, "YScale", "log");
|
||||
ylim([9e-5, maxBerForPlot]);
|
||||
xlim([min(cleanData.sir) - 1, max(cleanData.sir) + 1]);
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "northeast", "Interpreter", "none");
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function data = collectMpiSimulationBerRows(wh)
|
||||
storageNames = string(fieldnames(wh.sto));
|
||||
data = table();
|
||||
|
||||
for storageIdx = 1:numel(storageNames)
|
||||
storageName = storageNames(storageIdx);
|
||||
storage = wh.sto.(char(storageName));
|
||||
|
||||
for linIdx = 1:numel(storage)
|
||||
package = storage{linIdx};
|
||||
ber = extractPackageBer(package);
|
||||
if ~isfinite(ber)
|
||||
continue
|
||||
end
|
||||
|
||||
[physValues, physNames] = wh.getPhysIndicesByLinIndex(linIdx);
|
||||
phys = struct();
|
||||
for physIdx = 1:numel(physNames)
|
||||
phys.(char(physNames{physIdx})) = physValues{physIdx};
|
||||
end
|
||||
|
||||
algorithm = extractPackageAlgorithm(package, storageName);
|
||||
newRow = table( ...
|
||||
storageName, ...
|
||||
algorithm, ...
|
||||
readPhysValue(phys, "sir", NaN), ...
|
||||
readPhysValue(phys, "block_update", NaN), ...
|
||||
readPhysValue(phys, "random_key", NaN), ...
|
||||
readPhysValue(phys, "laser_linewidth", NaN), ...
|
||||
ber, ...
|
||||
'VariableNames', {'storage_name', 'algorithm', 'sir', ...
|
||||
'block_update', 'random_key', 'laser_linewidth', 'BER'});
|
||||
data = [data; newRow]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function data = normalizeBerColumnName(data)
|
||||
variableNames = string(data.Properties.VariableNames);
|
||||
if ismember("BER", variableNames)
|
||||
return
|
||||
end
|
||||
|
||||
if ismember("ber", variableNames)
|
||||
data.Properties.VariableNames(variableNames == "ber") = {'BER'};
|
||||
return
|
||||
end
|
||||
|
||||
error("run_mpi_simulation_recipe:MissingBerColumn", ...
|
||||
"Could not find a BER or ber column in the simulation BER table.");
|
||||
end
|
||||
|
||||
function value = readPhysValue(phys, name, defaultValue)
|
||||
if isfield(phys, name)
|
||||
value = phys.(name);
|
||||
else
|
||||
value = defaultValue;
|
||||
end
|
||||
end
|
||||
|
||||
function ber = extractPackageBer(package)
|
||||
ber = NaN;
|
||||
if isempty(package)
|
||||
return
|
||||
end
|
||||
|
||||
if iscell(package)
|
||||
package = package{1};
|
||||
end
|
||||
|
||||
if isstruct(package) && isfield(package, "metrics")
|
||||
metrics = package.metrics;
|
||||
if isstruct(metrics) && isfield(metrics, "BER")
|
||||
ber = metrics.BER;
|
||||
elseif isobject(metrics) && isprop(metrics, "BER")
|
||||
ber = metrics.BER;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function algorithm = extractPackageAlgorithm(package, fallbackName)
|
||||
algorithm = fallbackName;
|
||||
if isempty(package)
|
||||
return
|
||||
end
|
||||
|
||||
if iscell(package)
|
||||
package = package{1};
|
||||
end
|
||||
|
||||
if isstruct(package) && isfield(package, "mpi_reduction_config") && ...
|
||||
isfield(package.mpi_reduction_config, "algorithm")
|
||||
algorithm = string(package.mpi_reduction_config.algorithm);
|
||||
end
|
||||
end
|
||||
|
||||
function summaryTable = addBerStdBounds(summaryTable, cleanData, groupVars)
|
||||
nGroups = height(summaryTable);
|
||||
stdCenter = NaN(nGroups, 1);
|
||||
stdLower = NaN(nGroups, 1);
|
||||
stdUpper = NaN(nGroups, 1);
|
||||
|
||||
for groupIdx = 1:nGroups
|
||||
rowMask = true(height(cleanData), 1);
|
||||
for varIdx = 1:numel(groupVars)
|
||||
varName = groupVars(varIdx);
|
||||
rowMask = rowMask & cleanData.(char(varName)) == summaryTable.(char(varName))(groupIdx);
|
||||
end
|
||||
|
||||
[stdCenter(groupIdx), stdLower(groupIdx), stdUpper(groupIdx)] = ...
|
||||
logBerMeanStdInterval(cleanData.BER(rowMask));
|
||||
end
|
||||
|
||||
summaryTable.std_center_BER = stdCenter;
|
||||
summaryTable.std_lower_BER = stdLower;
|
||||
summaryTable.std_upper_BER = stdUpper;
|
||||
end
|
||||
|
||||
function [centerBer, lowerBer, upperBer] = logBerMeanStdInterval(berValues)
|
||||
berValues = berValues(isfinite(berValues) & berValues > 0);
|
||||
if isempty(berValues)
|
||||
centerBer = NaN;
|
||||
lowerBer = NaN;
|
||||
upperBer = NaN;
|
||||
return
|
||||
end
|
||||
|
||||
logBer = log10(berValues(:));
|
||||
centerLog = mean(logBer, "omitnan");
|
||||
stdLog = std(logBer, 0, "omitnan");
|
||||
|
||||
centerBer = 10 .^ centerLog;
|
||||
lowerBer = 10 .^ (centerLog - stdLog);
|
||||
upperBer = 10 .^ (centerLog + stdLog);
|
||||
end
|
||||
|
||||
function [xBand, centerBand, yBounds] = berStdBounds(sirValues, centerBer, lowerBer, upperBer, boundMode, maxOrder)
|
||||
if boundMode == "directStd"
|
||||
[xBand, centerBand, yBounds] = directBerBounds(sirValues, centerBer, lowerBer, upperBer);
|
||||
return
|
||||
end
|
||||
|
||||
[xBand, centerBand, yBounds] = fittedBerBounds(sirValues, centerBer, lowerBer, upperBer, maxOrder);
|
||||
end
|
||||
|
||||
function [xBand, centerBand, yBounds] = directBerBounds(sirValues, centerBer, lowerBer, upperBer)
|
||||
valid = isfinite(sirValues) & isfinite(centerBer) & isfinite(lowerBer) & ...
|
||||
isfinite(upperBer) & centerBer > 0 & lowerBer > 0 & upperBer > 0;
|
||||
|
||||
xBand = sirValues(valid).';
|
||||
centerBand = centerBer(valid).';
|
||||
lowerBand = lowerBer(valid).';
|
||||
upperBand = upperBer(valid).';
|
||||
|
||||
[xBand, orderIdx] = sort(xBand(:));
|
||||
centerBand = centerBand(orderIdx);
|
||||
lowerBand = lowerBand(orderIdx);
|
||||
upperBand = upperBand(orderIdx);
|
||||
|
||||
lowerTmp = min(lowerBand, upperBand);
|
||||
upperBand = max(lowerBand, upperBand);
|
||||
lowerBand = lowerTmp;
|
||||
|
||||
centerBand = min(max(centerBand, lowerBand), upperBand);
|
||||
yBounds = [max(centerBand - lowerBand, 0), max(upperBand - centerBand, 0)];
|
||||
end
|
||||
|
||||
function [xBand, centerBand, yBounds] = fittedBerBounds(sirValues, meanBer, minBer, maxBer, maxOrder)
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & isfinite(minBer) & ...
|
||||
isfinite(maxBer) & meanBer > 0 & minBer > 0 & maxBer > 0;
|
||||
|
||||
x = sirValues(valid);
|
||||
yMean = meanBer(valid);
|
||||
yMin = minBer(valid);
|
||||
yMax = maxBer(valid);
|
||||
|
||||
if numel(x) < 2
|
||||
xBand = x(:);
|
||||
centerBand = yMean(:);
|
||||
yBounds = [max(yMean(:) - yMin(:), 0), max(yMax(:) - yMean(:), 0)];
|
||||
return
|
||||
end
|
||||
|
||||
[x, orderIdx] = sort(x(:));
|
||||
yMean = yMean(orderIdx);
|
||||
yMin = yMin(orderIdx);
|
||||
yMax = yMax(orderIdx);
|
||||
|
||||
xBand = linspace(min(x), max(x), 300).';
|
||||
fitOrder = min(maxOrder, numel(unique(x)) - 1);
|
||||
|
||||
if fitOrder < 1
|
||||
centerBand = interp1(x, yMean, xBand, "linear", "extrap");
|
||||
lowerBand = interp1(x, yMin, xBand, "linear", "extrap");
|
||||
upperBand = interp1(x, yMax, xBand, "linear", "extrap");
|
||||
else
|
||||
centerBand = fitLogBer(x, yMean, xBand, fitOrder);
|
||||
lowerBand = fitLogBer(x, yMin, xBand, fitOrder);
|
||||
upperBand = fitLogBer(x, yMax, xBand, fitOrder);
|
||||
end
|
||||
|
||||
lowerTmp = min(lowerBand, upperBand);
|
||||
upperBand = max(lowerBand, upperBand);
|
||||
lowerBand = lowerTmp;
|
||||
|
||||
centerBand = min(max(centerBand, lowerBand), upperBand);
|
||||
yBounds = [max(centerBand - lowerBand, 0), max(upperBand - centerBand, 0)];
|
||||
end
|
||||
|
||||
function yFit = fitLogBer(x, y, xFit, fitOrder)
|
||||
coeff = polyfit(x, log10(y), fitOrder);
|
||||
yFit = 10 .^ polyval(coeff, xFit);
|
||||
end
|
||||
|
||||
function label = algorithmDisplayName(algorithmName)
|
||||
algorithmName = string(algorithmName);
|
||||
switch algorithmName
|
||||
case {"plain_ffe", "conventional_ffe"}
|
||||
label = "FFE only";
|
||||
case "a2_tracked_levels"
|
||||
label = "ACT";
|
||||
case "a2_residual"
|
||||
label = "L-DCA";
|
||||
case "a1_moving_average"
|
||||
label = "DCA";
|
||||
case "dc_tracking"
|
||||
label = "DCT";
|
||||
otherwise
|
||||
label = algorithmName;
|
||||
end
|
||||
end
|
||||
|
||||
function color = algorithmColor(algorithmName)
|
||||
algorithmName = string(algorithmName);
|
||||
switch algorithmName
|
||||
case {"plain_ffe", "conventional_ffe"}
|
||||
color = [0.3467 0.5360 0.6907];
|
||||
case "a2_tracked_levels"
|
||||
color = [0.9153 0.2816 0.2878];
|
||||
case "a2_residual"
|
||||
color = [0.4416 0.7490 0.4322];
|
||||
case "a1_moving_average"
|
||||
color = [1.0000 0.5984 0.2000];
|
||||
case "dc_tracking"
|
||||
color = [0.6769 0.4447 0.7114];
|
||||
otherwise
|
||||
color = [0 0 0];
|
||||
end
|
||||
end
|
||||
|
||||
function marker = algorithmMarker(algorithmName, algorithmMarkers)
|
||||
algorithmOrder = ["conventional_ffe", "dc_tracking", "a2_tracked_levels", ...
|
||||
"a2_residual", "a1_moving_average"];
|
||||
markerIdx = find(algorithmOrder == string(algorithmName), 1);
|
||||
if isempty(markerIdx)
|
||||
markerIdx = 1;
|
||||
end
|
||||
marker = algorithmMarkers{mod(markerIdx - 1, numel(algorithmMarkers)) + 1};
|
||||
end
|
||||
@@ -0,0 +1,87 @@
|
||||
function [results, wh] = submitMpiSimulationJobs(wh, simulation_config, options)
|
||||
%SUBMITMPISIMULATIONJOBS Execute MPI simulation warehouse points via runBatch.
|
||||
|
||||
arguments
|
||||
wh DataStorage
|
||||
simulation_config struct
|
||||
options.mode = processingMode.serial
|
||||
options.waitbar (1,1) logical = true
|
||||
options.numWorkers (1,1) double {mustBeNonnegative, mustBeInteger} = 0
|
||||
options.idleTimeout (1,1) double {mustBePositive} = 300
|
||||
options.cancelExistingQueue (1,1) logical = true
|
||||
end
|
||||
|
||||
nJobs = wh.getLastLinIndice();
|
||||
jobs = repmat(struct("args", {{}}, "label", "", "meta", struct()), 1, nJobs);
|
||||
|
||||
for linIdx = 1:nJobs
|
||||
userParameters = buildUserParameters(wh, linIdx);
|
||||
jobs(linIdx).args = {userParameters, simulation_config};
|
||||
jobs(linIdx).label = buildJobLabel(userParameters, linIdx);
|
||||
jobs(linIdx).meta.lin_idx = linIdx;
|
||||
jobs(linIdx).meta.userParameters = userParameters;
|
||||
end
|
||||
|
||||
results = runBatch(@mpi_simulation_worker, jobs, ...
|
||||
"mode", options.mode, ...
|
||||
"waitbar", options.waitbar, ...
|
||||
"waitbarMessage", "Processing MPI simulations...", ...
|
||||
"numWorkers", options.numWorkers, ...
|
||||
"idleTimeout", options.idleTimeout, ...
|
||||
"cancelExistingQueue", options.cancelExistingQueue, ...
|
||||
"resultHandler", @storeResult, ...
|
||||
"errorHandler", @handleError);
|
||||
|
||||
function userParameters = buildUserParameters(storageWh, linIdx)
|
||||
userParameters = struct();
|
||||
if isempty(storageWh.getDimension())
|
||||
return
|
||||
end
|
||||
|
||||
[values, names] = storageWh.getPhysIndicesByLinIndex(linIdx);
|
||||
for paramIdx = 1:numel(names)
|
||||
userParameters.(char(names{paramIdx})) = values{paramIdx};
|
||||
end
|
||||
end
|
||||
|
||||
function label = buildJobLabel(userParameters, linIdx)
|
||||
label = sprintf("MPI sim job %d", linIdx);
|
||||
if isfield(userParameters, "sir")
|
||||
label = sprintf("%s, SIR %g dB", label, userParameters.sir);
|
||||
end
|
||||
if isfield(userParameters, "block_update")
|
||||
label = sprintf("%s, block %g", label, userParameters.block_update);
|
||||
end
|
||||
end
|
||||
|
||||
function storeResult(val, job, ~)
|
||||
if isempty(val) || ~isstruct(val)
|
||||
return
|
||||
end
|
||||
|
||||
storageNames = fieldnames(val);
|
||||
for storageIdx = 1:numel(storageNames)
|
||||
storageName = storageNames{storageIdx};
|
||||
if isempty(val.(storageName))
|
||||
continue
|
||||
end
|
||||
|
||||
ensureStorage(storageName);
|
||||
wh.addValueToStorageByLinIdx(val.(storageName), storageName, job.meta.lin_idx);
|
||||
end
|
||||
end
|
||||
|
||||
function ensureStorage(storageName)
|
||||
if ~isfield(wh.sto, storageName)
|
||||
wh.addStorage(storageName);
|
||||
end
|
||||
end
|
||||
|
||||
function handleError(ME, job, ~)
|
||||
fprintf("[%s] ERROR [%s]: %s\n", job.label, ME.identifier, ME.message);
|
||||
for st = ME.stack'
|
||||
fprintf(" %s:%d (%s)\n", st.file, st.line, st.name);
|
||||
end
|
||||
fprintf("Full report:\n%s\n", getReport(ME, "extended"));
|
||||
end
|
||||
end
|
||||
Reference in New Issue
Block a user