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This commit is contained in:
Silas Oettinghaus
@@ -18,11 +18,11 @@ end
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run_conventional_ffe = 1;
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run_ma = 0;
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run_hpf = 0;
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run_a2_tracked_levels = 1;
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run_a2_residual = 1;
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run_a1 = 1;
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run_a2_tracked_levels = 0;
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run_a2_residual = 0;
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run_a1 = 0;
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run_tracking_adaptive = 1;
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plot_output_signals = 0;
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plot_output_signals = 1;
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%% Shared fixed EQ settings
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eq_sps = 2;
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@@ -283,7 +283,7 @@ if run_tracking_adaptive
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"dc_tracking_persistence_gain", 0, ...
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"dc_tracking_buffer_len", block_update, ...
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"optmize_mus", false, ...
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"optimize_dc_tracking_params", true, ...
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"optimize_dc_tracking_params", false, ...
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"dc_tracking_optimization_len", 2^15, ...
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"dc_tracking_optimization_max_evals", 30, ...
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"plot_mu_optimization", options.debug_plots, ...
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@@ -296,12 +296,12 @@ if run_tracking_adaptive
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"dc_tracking", "adaptive", block_update);
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output.(char(storageName)) = ffe_results;
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% fignum = 106;
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%
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% eq_noise = equalized_signal - Symbols;
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% dn = sprintf("FFE DCT; SIR: %d dB",options.dataTable.sir);
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% showEQNoisePSD(eq_noise, "fignum", fignum, "displayname", dn,"colormode","diverging");
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% ylim([-70 -30]);
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fignum = 106;
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eq_noise = equalized_signal - Symbols;
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dn = sprintf("FFE DCT; SIR: %d dB",options.dataTable.sir);
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showEQNoisePSD(eq_noise, "fignum", fignum, "displayname", dn,"colormode","diverging");
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ylim([-70 -30]);
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%
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% dn = sprintf("FFE only; SIR: %d dB",options.dataTable.sir);
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% showEQNoisePSD(eq_noise_ffe, "fignum", fignum+1, "displayname", dn,"colormode","diverging");
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@@ -0,0 +1,505 @@
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%% BER over SIR for PAM4 algorithm and baudrate comparison at 1000 m MPI delay
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% 1) gather block_update = 1 data from the database
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% 2) clean data and keep the 1000 m delay regime
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% 3) plot one BER-over-SIR figure grouped by algorithm and baudrate
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clear; clc;
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%% 1) Gather data
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studyName = "block_update_sweep";
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selectedBlockUpdate = 1;
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selectedPamLevel = 4;
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selectedPathLength = 1000;
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selectedSymbolrates = [96e9 112e9];
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algorithmSelection = table( ...
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["dc_tracking"; ...
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"plain_ffe"; ...
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"a2_tracked_levels"; ...
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"a2_residual"; ...
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"a1_moving_average"], ...
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[true; true; false; false; false], ...
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'VariableNames', ["algorithm", "enabled"]);
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selectedAlgorithms = algorithmSelection.algorithm(algorithmSelection.enabled);
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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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maxBerForPlot = 0.1;
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scatterKeepFraction = 0.2;
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boundMode = "fitStd"; % "fitStd", "directStd", "fitCI", or "directCI"
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confidenceLevel = 0.95;
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db = DBHandler( ...
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"dataBase", "labor", ...
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"type", "mysql", ...
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"server", "192.168.178.192", ...
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"user", "silas", ...
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"password", "silas");
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db.refresh();
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fp = QueryFilter();
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fp.where('Runs', 'interference_path_length', 'EQUALS', selectedPathLength);
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fp.where('Runs', 'fiber_length', 'EQUALS', 0);
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fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
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fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
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fp.where('Runs', 'pam_level', 'EQUALS', selectedPamLevel);
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% fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
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fp.where('MpiReductionResults', 'block_update', 'EQUALS', selectedBlockUpdate);
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selectedFields = { ...
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'Runs.run_id', ...
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'Runs.sir', ...
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'Runs.pam_level', ...
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'Runs.symbolrate', ...
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'Runs.interference_path_length', ...
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'Runs.power_mpi_interference', ...
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'Runs.power_pd_in', ...
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'Runs.v_bias', ...
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'Runs.loop_id', ...
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'MpiReductionResults.occurrence_idx', ...
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'MpiReductionResults.storage_name', ...
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'MpiReductionResults.algorithm', ...
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'MpiReductionResults.algorithm_variant', ...
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'MpiReductionResults.eq_class', ...
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'MpiReductionResults.block_update', ...
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'MpiReductionResults.BER', ...
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'MpiReductionResults.BER_precoded', ...
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'MpiReductionResults.SNR', ...
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'MpiReductionResults.GMI', ...
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'MpiReductionResults.AIR'};
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selectedFields = selectedFields(:);
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[rawData, query] = db.queryDB(fp, selectedFields);
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disp(query);
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fprintf("Fetched %d MPI reduction rows.\n", height(rawData));
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%% 2) Clean data
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data = rawData;
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numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
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"interference_path_length", "occurrence_idx", "block_update", ...
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"power_mpi_interference", "BER", "BER_precoded", "SNR", "GMI", "AIR"];
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for fieldIdx = 1:numel(numericFields)
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fieldName = numericFields(fieldIdx);
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if ismember(fieldName, string(data.Properties.VariableNames))
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data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
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end
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end
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stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
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for fieldIdx = 1:numel(stringFields)
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fieldName = stringFields(fieldIdx);
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if ismember(fieldName, string(data.Properties.VariableNames))
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data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
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end
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end
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data(data.run_id == 3866, :) = [];
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data(data.run_id == 3865, :) = [];
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data(data.run_id == 3796, :) = [];
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data(data.run_id == 3797, :) = [];
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data(data.run_id == 3798, :) = [];
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data(data.run_id == 4002, :) = [];
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data(data.run_id == 4200, :) = [];
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data(data.run_id == 4199, :) = [];
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data(data.loop_id == 91, :) = [];
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data(data.loop_id == 59, :) = [];
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data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
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data.sir_exact = -7 - data.power_mpi_interference;
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data = data(data.pam_level == selectedPamLevel, :);
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data = data(data.interference_path_length == selectedPathLength, :);
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data = data(ismember(data.symbolrate, selectedSymbolrates), :);
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data = data(ismember(data.algorithm, selectedAlgorithms), :);
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selectedAlgorithms = selectedAlgorithms(ismember(selectedAlgorithms, unique(data.algorithm, "stable")));
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if isempty(data)
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warning("PLOT_mpi_reduction_db_baudrate_comparison_pam4_1000m:NoRows", ...
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"No rows remain after BER/study/block/PAM/symbolrate/algorithm filtering.");
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return
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end
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data.clean_keep = true(height(data), 1);
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groupId = findgroups(data.symbolrate, data.algorithm, data.sir);
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for curGroup = unique(groupId(isfinite(groupId))).'
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rowMask = groupId == curGroup;
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berValues = data.BER(rowMask);
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if nnz(rowMask) > 3
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data.clean_keep(rowMask) = ~isoutlier(berValues);
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end
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end
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cleanData = data(data.clean_keep, :);
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groupVars = ["symbolrate", "algorithm", "sir"];
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summaryTable = groupsummary(cleanData, groupVars, ...
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{"mean", "min", "max"}, "BER");
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sirExactTable = groupsummary(cleanData, groupVars, "median", "sir_exact");
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summaryTable = sortrows(summaryTable, groupVars);
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sirExactTable = sortrows(sirExactTable, groupVars);
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summaryTable.sir_exact = sirExactTable.median_sir_exact;
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summaryTable = addBerIntervalBounds(summaryTable, cleanData, groupVars, confidenceLevel);
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fprintf("Cleaned to %d rows across %d baudrate/algorithm/SIR groups.\n", ...
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height(cleanData), height(summaryTable));
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disp(groupcounts(cleanData, ["symbolrate", "algorithm"]));
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%% 3) Plot BER-over-SIR algorithm comparison with baudrate curves
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figure(); hold on;
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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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displayName = algorithmDisplayName(algorithmName);
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for rateIdx = 1:numel(selectedSymbolrates)
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symbolrate = selectedSymbolrates(rateIdx);
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[marker, lineStyle] = symbolrateStyle(symbolrate);
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rawMask = cleanData.symbolrate == symbolrate & ...
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cleanData.algorithm == algorithmName;
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curveMask = summaryTable.symbolrate == symbolrate & ...
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summaryTable.algorithm == algorithmName;
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if ~any(curveMask)
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continue
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end
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scatterRows = downsampleRows(cleanData(rawMask, :), scatterKeepFraction);
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% scatter(scatterRows.sir_exact, scatterRows.BER, ...
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% 14, ...
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% "Marker", marker, ...
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% "MarkerEdgeColor", algColor, ...
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% "MarkerFaceColor", algColor, ...
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% "MarkerEdgeAlpha", 0.25, ...
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% "MarkerFaceAlpha", 0.25, ...
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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, boundLowerBer, boundUpperBer] = ...
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selectBerBounds(summaryTable(curveMask, :), boundMode);
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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] = berIntervalBounds( ...
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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', algColor, ...
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'nan', 'fill', ...
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'orientation', 'vert');
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set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
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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", 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", sprintf("%s, %.0f GBd", displayName, symbolrate * 1e-9));
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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", 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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end
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yline(2.2e-4, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
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yline(3.8e-3, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
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yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
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% title(sprintf("PAM %.0f, %.0f m, block update %.0f", ...
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% selectedPamLevel, selectedPathLength, selectedBlockUpdate));
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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([3e-5, maxBerForPlot]);
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xlim([14, 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", true, "setcolors", false, ...
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"setmarkers", false, "changemarkers", false);
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end
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/mpi/ber_vs_sir_pam4_baudrates_1000m.tikz","cleanfigure",1);
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%% Local helpers
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function values = numericColumn(values)
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if iscell(values)
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values = string(values);
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end
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if isstring(values) || ischar(values)
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values = str2double(values);
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end
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values = double(values);
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end
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function values = stringColumn(values)
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if iscell(values)
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values = string(values);
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elseif ischar(values)
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values = string(values);
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end
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values = strip(string(values));
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end
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function rows = downsampleRows(rows, keepFraction)
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if keepFraction >= 1 || height(rows) <= 1
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return
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end
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keepEvery = max(1, round(1 / keepFraction));
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keepIdx = 1:keepEvery:height(rows);
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rows = rows(keepIdx, :);
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end
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function summaryTable = addBerIntervalBounds(summaryTable, cleanData, groupVars, confidenceLevel)
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nGroups = height(summaryTable);
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ciCenter = NaN(nGroups, 1);
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ciLower = NaN(nGroups, 1);
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ciUpper = NaN(nGroups, 1);
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stdCenter = NaN(nGroups, 1);
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stdLower = NaN(nGroups, 1);
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stdUpper = NaN(nGroups, 1);
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for groupIdx = 1:nGroups
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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)) == summaryTable.(char(varName))(groupIdx);
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end
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[ciCenter(groupIdx), ciLower(groupIdx), ciUpper(groupIdx)] = ...
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logBerMeanConfidenceInterval(cleanData.BER(rowMask), confidenceLevel);
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[stdCenter(groupIdx), stdLower(groupIdx), stdUpper(groupIdx)] = ...
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logBerMeanStdInterval(cleanData.BER(rowMask));
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end
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summaryTable.ci_center_BER = ciCenter;
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summaryTable.ci_lower_BER = ciLower;
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summaryTable.ci_upper_BER = ciUpper;
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summaryTable.std_center_BER = stdCenter;
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summaryTable.std_lower_BER = stdLower;
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summaryTable.std_upper_BER = stdUpper;
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end
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function [centerBer, lowerBer, upperBer] = logBerMeanConfidenceInterval(berValues, confidenceLevel)
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berValues = berValues(isfinite(berValues) & berValues > 0);
|
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if isempty(berValues)
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centerBer = NaN;
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lowerBer = NaN;
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upperBer = NaN;
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return
|
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end
|
||||
|
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logBer = log10(berValues(:));
|
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centerLog = mean(logBer, "omitnan");
|
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|
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if numel(logBer) < 2
|
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centerBer = 10 .^ centerLog;
|
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lowerBer = centerBer;
|
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upperBer = centerBer;
|
||||
return
|
||||
end
|
||||
|
||||
alpha = 1 - confidenceLevel;
|
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if exist("tinv", "file")
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tCritical = tinv(1 - alpha/2, numel(logBer) - 1);
|
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else
|
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tCritical = 1.96;
|
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end
|
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|
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halfWidth = tCritical * std(logBer, 0, "omitnan") / sqrt(numel(logBer));
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centerBer = 10 .^ centerLog;
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lowerBer = 10 .^ (centerLog - halfWidth);
|
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upperBer = 10 .^ (centerLog + halfWidth);
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||||
end
|
||||
|
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function [centerBer, lowerBer, upperBer] = logBerMeanStdInterval(berValues)
|
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berValues = berValues(isfinite(berValues) & berValues > 0);
|
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if isempty(berValues)
|
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centerBer = NaN;
|
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lowerBer = NaN;
|
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upperBer = NaN;
|
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return
|
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end
|
||||
|
||||
logBer = log10(berValues(:));
|
||||
centerLog = mean(logBer, "omitnan");
|
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stdLog = std(logBer, 0, "omitnan");
|
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|
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centerBer = 10 .^ centerLog;
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lowerBer = 10 .^ (centerLog - stdLog);
|
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upperBer = 10 .^ (centerLog + stdLog);
|
||||
end
|
||||
|
||||
function [centerBer, lowerBer, upperBer] = selectBerBounds(curveSummary, boundMode)
|
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if boundMode == "fitCI" || boundMode == "directCI"
|
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centerBer = curveSummary.ci_center_BER.';
|
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lowerBer = curveSummary.ci_lower_BER.';
|
||||
upperBer = curveSummary.ci_upper_BER.';
|
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else
|
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centerBer = curveSummary.std_center_BER.';
|
||||
lowerBer = curveSummary.std_lower_BER.';
|
||||
upperBer = curveSummary.std_upper_BER.';
|
||||
end
|
||||
end
|
||||
|
||||
function [xBand, centerBand, yBounds] = berIntervalBounds(sirValues, centerBer, lowerBer, upperBer, boundMode, maxOrder)
|
||||
if boundMode == "directCI" || 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"
|
||||
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"
|
||||
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, lineStyle] = symbolrateStyle(symbolrate)
|
||||
switch symbolrate
|
||||
case 72e9
|
||||
marker = "square";
|
||||
lineStyle = "--";
|
||||
case 96e9
|
||||
marker = "o";
|
||||
lineStyle = "-";
|
||||
case 112e9
|
||||
marker = "diamond";
|
||||
lineStyle = ":";
|
||||
otherwise
|
||||
marker = "^";
|
||||
lineStyle = "-.";
|
||||
end
|
||||
end
|
||||
@@ -3,8 +3,8 @@ dsp_options = struct();
|
||||
dsp_options.mode = "run_id";
|
||||
dsp_options.recipe = @mpi_recipe_dev;
|
||||
dsp_options.append_to_db = false;
|
||||
dsp_options.start_occurence = 1;
|
||||
dsp_options.max_occurences = 15;
|
||||
dsp_options.start_occurence = 5;
|
||||
dsp_options.max_occurences = 1;
|
||||
dsp_options.debug_plots = false;
|
||||
|
||||
write_mpi_reduction_db = 0;
|
||||
@@ -37,7 +37,6 @@ db = DBHandler("dataBase", [dsp_options.dataBase],...
|
||||
"user", dsp_options.user, "password", dsp_options.password);
|
||||
|
||||
%%
|
||||
|
||||
pamformats = [4,6,8];
|
||||
baudrates = [112e9,96e9,72e9];
|
||||
|
||||
@@ -48,10 +47,10 @@ for i = 1
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','run_id','GREATER_EQUAL',3153);
|
||||
fp.where('Runs', 'symbolrate', 'EQUALS', B); % 72 96 112
|
||||
fp.where('Runs', 'symbolrate','EQUALS',B); % 72 96 112
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||
fp.where('Runs', 'interference_path_length', 'EQUALS', 1000);
|
||||
fp.where('Runs', 'sir', 'LESS_EQUAL', 50);
|
||||
fp.where('Runs', 'sir', 'LESS_EQUAL', 20);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||
% fp.where('Runs', 'is_mpi', 'EQUALS', 1);
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', M);
|
||||
@@ -62,11 +61,12 @@ for i = 1
|
||||
[~, sortIdx] = sort(dataTable.sir, 'descend');
|
||||
dataTable = dataTable(sortIdx, :);
|
||||
|
||||
% desired_sir = [15,22,26,30,38];
|
||||
% desired_sir = [22,30,38];
|
||||
desired_sir = [15,22,26,30,38];
|
||||
desired_sir = [22,38];
|
||||
% exclude_sr = [112e9];
|
||||
% % Filter dataTable to only keep rows with sir in desired_sir
|
||||
% isDesired = ismember(dataTable.sir, desired_sir);
|
||||
% dataTable = dataTable(isDesired, :);
|
||||
isDesired = ~ismember(dataTable.symbolrate, desired_sir);
|
||||
dataTable = dataTable(isDesired, :);
|
||||
% dataTable = dataTable(1,:);
|
||||
|
||||
run_ids = dataTable.run_id;
|
||||
|
||||
Reference in New Issue
Block a user