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4 Commits
b068400cb5
...
cae81c0dae
| Author | SHA1 | Date | |
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cae81c0dae | ||
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2e667d6ec3 | ||
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9455084711 | ||
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eef5d2f2ea |
@@ -528,10 +528,7 @@ classdef Signal
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y_margin = 0.05 * y_range;
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ylim([y_min - y_margin, y_max + y_margin]);
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% Set ticks automatically, avoid overpopulation
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try
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yticks(round(linspace(y_min, y_max, min(10, max(4, ceil(y_range/10))))));
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end
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yticks(-200:10:200);
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grid on;
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% Add legend if not already present
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@@ -16,11 +16,13 @@ end
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%% Execution toggles
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run_conventional_ffe = 1;
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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_ma = 0;
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run_hpf = 0;
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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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@@ -49,6 +51,7 @@ if plot_output_signals
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"fignum", 400, ...
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"normalize", true);
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end
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%% Conventional FFE
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if run_conventional_ffe
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eq_ffe = FFE_plain( ...
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@@ -73,11 +76,103 @@ if run_conventional_ffe
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"plain_ffe", "baseline", block_update);
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output.(char(storageName)) = ffe_results;
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eq_noise_ffe = equalized_signal - Symbols;
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if plot_output_signals
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plotEqSignals(equalized_signal,Symbols,options,400,-1);
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end
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end
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%% Moving Average Filter -> Conventional FFE
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if run_ma
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eq_ffe = FFE_plain( ...
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"sps", eq_sps, ...
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"order", eq_order, ...
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"decide", false, ...
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"adaption_technique", eq_adaption, ...
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"len_tr", eq_len_tr, ...
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"epochs_tr", eq_epochs_tr, ...
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"mu_tr", eq_mu_tr, ...
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"dd_mode", true, ...
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"epochs_dd", eq_epochs_dd, ...
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"mu_dd", 0.0012, ...
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"optmize_mus", false, ...
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"plot_mu_optimization", options.debug_plots, ...
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"save_debug", eq_save_debug);
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%%% APPLY MA filter
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movavg_window_length = options.userParameters.bufferlen;
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movavg_window_symmetry = "causal"; % "causal" or "noncausal"
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switch movavg_window_symmetry
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case "causal"
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dc_estimate = movmean(Scpe_sig.signal, ...
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[movavg_window_length-1,0],"Endpoints","shrink");
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case "noncausal"
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dc_estimate = movmean(Scpe_sig.signal, ...
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movavg_window_length,"Endpoints","shrink");
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otherwise
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error("mpi_recipe_dev:InvalidMovAvgSymmetry", ...
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"movavg_window_symmetry must be 'causal' or 'noncausal'.");
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end
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Scpe_sig_filt = Scpe_sig;
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Scpe_sig_filt.signal = Scpe_sig.signal - dc_estimate;
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clear dc_estimate;
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storageName = "MovAvg_plus_conventional_FFE";
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[ffe_results,equalized_signal] = runFfe(eq_ffe, "Moving-average + conventional FFE", ...
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Scpe_sig_filt, Symbols, Tx_bits, options);
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clear Scpe_sig_filt;
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ffe_results = attachMpiReductionConfig(ffe_results, eq_ffe, storageName, ...
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"MovAvg_plus_conventional_FFE", char(movavg_window_symmetry), block_update);
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output.(char(storageName)) = ffe_results;
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if plot_output_signals
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plotEqSignals(equalized_signal,Symbols,options,400,-1);
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end
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end
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%% High Pass Filter -> Conventional FFE
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if run_hpf
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eq_ffe = FFE_plain( ...
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"sps", eq_sps, ...
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"order", eq_order, ...
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"decide", false, ...
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"adaption_technique", eq_adaption, ...
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"len_tr", eq_len_tr, ...
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"epochs_tr", eq_epochs_tr, ...
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"mu_tr", eq_mu_tr, ...
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"dd_mode", true, ...
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"epochs_dd", eq_epochs_dd, ...
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"mu_dd", 0.0012, ...
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"optmize_mus", false, ...
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"plot_mu_optimization", options.debug_plots, ...
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"save_debug", eq_save_debug);
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%%% APPLY HPF in MHZ order
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f = Filter("f_cutoff",options.userParameters.hpf,"filterType","butterworth","lowpass",0,"filtdegree",4,"fs",options.fsym*2);
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Scpe_sig_filt = f.process(Scpe_sig);
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storageName = "HPF_plus_conventional_FFE";
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[ffe_results,equalized_signal] = runFfe(eq_ffe, "Conventional FFE", ...
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Scpe_sig_filt, Symbols, Tx_bits, options);
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clear Scpe_sig_filt;
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ffe_results = attachMpiReductionConfig(ffe_results, eq_ffe, storageName, ...
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"HPF_plus_conventional_FFE", "hpf", block_update);
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output.(char(storageName)) = ffe_results;
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if plot_output_signals
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plotEqSignals(equalized_signal,Symbols,options,400,-1);
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end
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end
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%% A2 tracked-level decision
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if run_a2_tracked_levels
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eq_ffe = FFE_A2TrackedLevels( ...
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@@ -92,8 +187,8 @@ if run_a2_tracked_levels
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"epochs_dd", eq_epochs_dd, ...
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"mu_dd", 0.012, ...
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"dc_smoothing_a2", 1, ...
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"dc_level_avg_bufferlength_a2", 112, ...
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"dc_level_update_blocklength_a2", block_update, ...
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"dc_level_avg_bufferlength_a2", 112, ... %war 112
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"dc_level_update_blocklength_a2", block_update, ... % war block_update
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"dc_level_weights_a2", [1], ...
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"save_debug", eq_save_debug);
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@@ -154,7 +249,7 @@ if run_a1
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"epochs_dd", eq_epochs_dd, ...
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"mu_dd", 0.012, ...
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"dc_smoothing_a1", 1, ...
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"dc_avg_bufferlength_a1", 2048, ...
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"dc_avg_bufferlength_a1", 2048, ... %was 2048
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"dc_avg_update_blocklength_a1", block_update, ...
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"save_debug", eq_save_debug);
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@@ -188,9 +283,9 @@ 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", 20, ...
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"dc_tracking_optimization_max_evals", 30, ...
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"plot_mu_optimization", options.debug_plots, ...
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"save_debug", eq_save_debug);
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@@ -201,7 +296,19 @@ 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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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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% ylim([-70 -30]);
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if plot_output_signals
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plotEqSignals(equalized_signal,Symbols,options,450,-1);
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end
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end
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@@ -5,8 +5,14 @@ arguments
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options.fignum (1,1) double = NaN % Default to NaN if not provided
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options.displayname (1,:) char = '' % Default to an empty string if not provided
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options.color = [];
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options.colormode (1,1) string {mustBeMember(options.colormode, ["diverging", "qualitative", "paired"])} = "diverging";
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end
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persistent nextGroupIdx
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if isempty(nextGroupIdx)
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nextGroupIdx = 0;
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end
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% Determine the figure number to use or create a new figure
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if isnan(options.fignum)
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fig = figure; % Create a new figure and get its handle
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@@ -15,16 +21,16 @@ end
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end
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hold on
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ax = gca;
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% N = numel(ax.Children);
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if isempty(options.color)
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N = sum(arrayfun(@(x) strcmp(x.LineStyle, '-'), ax.Children));
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cmap = linspecer(8);
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options.color = cmap(mod(N, size(cmap, 1)) + 1, :);
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linesBefore = findall(ax, 'Type', 'Line');
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useAutomaticColor = isempty(options.color);
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if useAutomaticColor
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options.color = selectEQNoisePSDColor(ax, options.colormode);
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end
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% Ensure the figure is ready before calling spectrum
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eq_noise = eq_noise - mean(eq_noise.signal);
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eq_noise.spectrum("displayname", options.displayname, "fignum", fig.Number, "normalizeTo0dB", 0,"color",options.color,"fft_length",4096*2);
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eq_noise.spectrum("displayname", options.displayname, "fignum", fig.Number, "normalizeTo0dB", 0,"color",options.color,"fft_length",4096);
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if ~isnan(options.postfilter_taps)
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% Hold on to the figure for further plotting
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@@ -44,7 +50,88 @@ end
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legend('show');
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end
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if useAutomaticColor
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nextGroupIdx = nextGroupIdx + 1;
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newLines = getNewLines(ax, linesBefore);
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tagEQNoisePSDLines(newLines, nextGroupIdx);
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recolorEQNoisePSDLines(ax, options.colormode);
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end
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xlim([-eq_noise.fs/2* 1e-9 eq_noise.fs/2* 1e-9]);
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% ylim([-15, 0]);
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end
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function color = selectEQNoisePSDColor(ax, colormode)
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if colormode == "qualitative"
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N = sum(arrayfun(@(x) strcmp(x.LineStyle, '-'), ax.Children));
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cmap = linspecer(8);
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color = cmap(mod(N, size(cmap, 1)) + 1, :);
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else
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color = [0 0 0];
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end
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end
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function newLines = getNewLines(ax, linesBefore)
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linesAfter = findall(ax, 'Type', 'Line');
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newLines = linesAfter(~ismember(linesAfter, linesBefore));
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end
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function tagEQNoisePSDLines(lines, groupIdx)
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for lineIdx = 1:numel(lines)
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setappdata(lines(lineIdx), 'showEQNoisePSDGroupIdx', groupIdx);
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end
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end
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function recolorEQNoisePSDLines(ax, colormode)
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if colormode == "qualitative"
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return
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end
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lines = findall(ax, 'Type', 'Line');
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groupIdx = NaN(size(lines));
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for lineIdx = 1:numel(lines)
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if isappdata(lines(lineIdx), 'showEQNoisePSDGroupIdx')
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groupIdx(lineIdx) = getappdata(lines(lineIdx), 'showEQNoisePSDGroupIdx');
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end
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end
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lines = lines(~isnan(groupIdx));
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groupIdx = groupIdx(~isnan(groupIdx));
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if isempty(lines)
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return
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end
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groups = unique(groupIdx, 'stable');
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groups = sort(groups);
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cols = getEQNoisePSDColormap(colormode, numel(groups));
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for groupColorIdx = 1:numel(groups)
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sameGroup = groupIdx == groups(groupColorIdx);
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set(lines(sameGroup), 'Color', cols(groupColorIdx, :));
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end
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end
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function cols = getEQNoisePSDColormap(colormode, numColors)
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switch colormode
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case "paired"
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cols = cbrewer2('paired', numColors);
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otherwise
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cols = getDivergingWithoutBrightCenter(numColors);
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end
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end
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function cols = getDivergingWithoutBrightCenter(numColors)
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numBaseColors = max(6, numColors);
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centerExcludeFraction = 0.3;
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baseCols = cbrewer2('RdYlBu', numBaseColors);
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centerIdx = (numBaseColors + 1) / 2;
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excludeHalfWidth = centerExcludeFraction * numBaseColors / 2;
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keepIdx = find(abs((1:numBaseColors) - centerIdx) > excludeHalfWidth);
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sampleIdx = round(linspace(1, numel(keepIdx), numColors));
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cols = baseCols(keepIdx(sampleIdx), :);
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end
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@@ -6,10 +6,12 @@ function beautifyBERplot(options)
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% beautifyBERplot
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% beautifyBERplot("polyfit",true,"polyorder",1)
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% beautifyBERplot("polyfit",true,"fitmethod","pchip")
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% beautifyBERplot("changemarkers",true)
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arguments
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options.logscale (1,1) logical = 1
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options.setmarkers (1,1) logical = 1
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options.changemarkers (1,1) logical = 0
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options.setcolors (1,1) logical = 1
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options.polyfit (1,1) logical = 0
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options.polyorder (1,1) double = 2
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@@ -42,9 +44,11 @@ if n > 0
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end
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end
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markers = {'o','s','o','o','^','v','d','>'};
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lw = 0.8;
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ms = 2;
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% Simple marker set that exports cleanly with common MATLAB-to-TikZ workflows.
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markers = {'o','square','diamond','^','v','>','<','pentagram'};
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lw = 1;
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ms = 5;
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applyMarkers = options.setmarkers || options.changemarkers;
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% ---------------------------------------------------------
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% Apply line/scatter + marker styling
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@@ -59,8 +63,8 @@ for i = 1:n
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dataObj.Color = cmap(i,:);
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end
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if options.setmarkers
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if strcmp(dataObj.Marker,'none')
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if applyMarkers
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if options.changemarkers || strcmp(dataObj.Marker,'none')
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dataObj.Marker = markers{mod(i-1,numel(markers))+1};
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end
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dataObj.MarkerSize = ms;
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@@ -80,11 +84,11 @@ for i = 1:n
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end
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end
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if options.setmarkers
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if strcmp(dataObj.Marker,'none')
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if applyMarkers
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if options.changemarkers || strcmp(dataObj.Marker,'none')
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dataObj.Marker = markers{mod(i-1,numel(markers))+1};
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end
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dataObj.SizeData = ms^2;
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dataObj.SizeData = ms;
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end
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end
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end
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@@ -22,24 +22,8 @@ matlab2tikz(char(filename), ...
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'tick label style={/pgf/number format/fixed, /pgf/number format/1000 sep={}}', ...
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'every axis/.append style={font=\scriptsize}', ...
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'legend columns=1', ...
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'legend style={at={(0.02,0.98)}, anchor=north west, font=\scriptsize, draw=black!100, rounded corners=2pt, inner sep=1pt, fill=white, column sep=2pt}' ...
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'legend style={at={(0.98,0.98)}, anchor=north east, font=\scriptsize, draw=black!100, rounded corners=2pt, inner sep=1pt, fill=white, column sep=2pt}' ...
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});
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% matlab2tikz(char(filename), ...
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% 'width','\fwidth', ...
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% 'height','\fheight', ...
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% 'showInfo',false, ...
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% 'extraAxisOptions',{ ...
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% 'legend style={font=\footnotesize}', ...
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% 'xlabel style={font=\color{white!15!black},font=\small},',...
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% 'ylabel style={font=\color{white!15!black},font=\small},',...
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% 'legend columns=1', ...
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% 'every axis/.append style={font=\scriptsize}',...
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% 'legend columns=1',...
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% 'legend style={at={(0.02,0.98)},font=\footnotesize,draw=black!60,rounded corners=2pt,inner sep=1pt,fill=white,column sep=6pt,anchor= north west}',...
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% 'legend style={at={(0.02,0.98)},draw=white!0!white,font=\scriptsize,inner sep=0.1pt,fill=white,column sep=1pt,anchor= north west}',...
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% 'every axis/.append style={font=\scriptsize}',...
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% 'scaled ticks=false', ...
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% 'tick label style={/pgf/number format/fixed, /pgf/number format/1000 sep={}}' ...
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% });
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end
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@@ -2234,12 +2234,12 @@ function [tikzMarkerSize, isDefault] = ...
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% the acute angle (at the top and the bottom of the diamond)
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% is a manually measured 75 degrees (in TikZ, and MATLAB
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% probably very similar); use this as a base for calculations
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tikzMarkerSize = matlabMarkerSize(:) / 2 / atan(75/2 *pi/180);
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tikzMarkerSize = matlabMarkerSize(:) / 1.7 ;%/ atan(75/2 *pi/180);
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case {'^','v','<','>'}
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% for triangles, matlab takes the height
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% and tikz the circumcircle radius;
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% the triangles are always equiangular
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tikzMarkerSize = matlabMarkerSize(:) / 2 * (2/3);
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tikzMarkerSize = matlabMarkerSize(:) / 1.7;% * (2/3);
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otherwise
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error('matlab2tikz:translateMarkerSize', ...
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'Unknown matlabMarker ''%s''.', matlabMarker);
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BIN
mpiana_13_07_2026.zip
Normal file
BIN
mpiana_13_07_2026.zip
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@@ -1,243 +0,0 @@
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%% Simple BER over SIR plot from the recombined warehouses
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% This script intentionally keeps the mechanics visible:
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||||
% 1) load the run_id warehouse and the grouped config warehouse
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||||
% 2) loop over occupied config cells
|
||||
% 3) read the physical coordinates of the cell
|
||||
% 4) extract all BER values stored in that cell
|
||||
% 5) plot individual BER dots and one larger mean marker per SIR
|
||||
|
||||
clear; clc;
|
||||
|
||||
scriptDir = fileparts("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\MPI_revisit\algorithms\");
|
||||
|
||||
|
||||
runWhFile = fullfile(scriptDir, "combined_by_run_id_results.mat");
|
||||
configWhFile = fullfile(scriptDir, "combined_by_config_results.mat");
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||||
|
||||
runWhData = load(runWhFile);
|
||||
configWhData = load(configWhFile);
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||||
|
||||
wh_run_id_combined = runWhData.wh_run_id_combined;
|
||||
wh_config_combined = configWhData.wh_config_combined;
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||||
algorithmStorageNames = configWhData.algorithmStorageNames;
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||||
|
||||
fprintf("Loaded run_id warehouse:\n %s\n", runWhFile);
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||||
wh_run_id_combined.showInfo;
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||||
|
||||
fprintf("\nLoaded config-grouped warehouse:\n %s\n", configWhFile);
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||||
wh_config_combined.showInfo;
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||||
|
||||
%% Plot settings
|
||||
|
||||
pathLengthToPlot = 1000; % use 300 to see duplicate run_ids per config; set 1000 for the previous path
|
||||
selectedPamLevels = 8; %wh_config_combined.parameter.pam_level.values;
|
||||
selectedAlgorithms = algorithmStorageNames([1,2,3,5]);
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||||
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||||
useBoundedLines = true; % switch uncertainty bands on/off here
|
||||
usePolyfit = true; % switch fitted dashed trend lines on/off here
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polyfitOrderMax = 4;
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||||
|
||||
|
||||
% SIR vs BER: one subplot per PAM level, all algorithms compared
|
||||
|
||||
if exist("linspecer", "file")
|
||||
algColors = linspecer(numel(selectedAlgorithms));
|
||||
else
|
||||
algColors = lines(numel(selectedAlgorithms));
|
||||
end
|
||||
|
||||
figure(); clf;
|
||||
tiledlayout(numel(selectedPamLevels), 1, "TileSpacing", "compact");
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||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
pamLevel = selectedPamLevels(pamIdx);
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||||
nexttile; hold on;
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for algIdx = 1:numel(selectedAlgorithms)
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||||
algorithmName = selectedAlgorithms{algIdx};
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||||
algColor = algColors(algIdx, :);
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||||
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||||
berTable = buildBerTable(wh_config_combined, algorithmName, pathLengthToPlot);
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berTable = berTable(berTable.pam_level == pamLevel, :);
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||||
% berTable(berTable.sir == 23,:) = [];
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||||
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||||
if isempty(berTable)
|
||||
continue
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end
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|
||||
sirValues = unique(berTable.sir(:).');
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||||
sirValues = sort(sirValues);
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|
||||
meanBer = nan(size(sirValues));
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||||
minBer = nan(size(sirValues));
|
||||
maxBer = nan(size(sirValues));
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||||
|
||||
for sirIdx = 1:numel(sirValues)
|
||||
sirValue = sirValues(sirIdx);
|
||||
sirRows = berTable(berTable.sir == sirValue, :);
|
||||
berValues = [sirRows.ber_values{:}];
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||||
berValues = berValues(isfinite(berValues));
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||||
berValues = berValues(berValues<0.1);
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||||
berValues = rmoutliers(berValues);
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||||
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||||
|
||||
if isempty(berValues)
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||||
continue
|
||||
end
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||||
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||||
scatter(repmat(sirValue, size(berValues)), berValues, ...
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||||
30, ...
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"Marker", ".", ...
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"MarkerEdgeColor", algColor, ...
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"MarkerFaceColor", algColor, ...
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||||
"HandleVisibility", "off");
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||||
|
||||
meanBer(sirIdx) = mean(berValues, "omitnan");
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||||
minBer(sirIdx) = min(berValues);
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||||
maxBer(sirIdx) = max(berValues);
|
||||
end
|
||||
|
||||
valid = isfinite(sirValues) & isfinite(meanBer);
|
||||
if ~any(valid)
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||||
continue
|
||||
end
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||||
|
||||
if useBoundedLines && exist("boundedline", "file")
|
||||
yLower = max(meanBer - minBer, 0);
|
||||
yUpper = max(maxBer - meanBer, 0);
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||||
yBounds = [yLower(:), yUpper(:)];
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||||
|
||||
[hl, hp] = boundedline(sirValues(valid).', meanBer(valid).', yBounds(valid, :), ...
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||||
'alpha', 'transparency', 0.08, ...
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||||
'cmap', algColor, ...
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||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
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||||
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
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||||
set(hp, "LineStyle", "none", "HandleVisibility", "off");
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||||
end
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||||
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||||
scatter(sirValues(valid), meanBer(valid), ...
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||||
20, ...
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||||
"Marker", "o", ...
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||||
"MarkerEdgeColor", algColor, ...
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||||
"MarkerFaceColor", algColor, ...
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||||
"LineWidth", 1, ...
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||||
"DisplayName", algorithmName);
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||||
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||||
if usePolyfit
|
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fitMask = valid & meanBer > 0;
|
||||
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);
|
||||
xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
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||||
yFit = 10 .^ polyval(fitCoeff, xFit);
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||||
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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, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
end
|
||||
end
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||||
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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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ylim([9e-5, 0.1]);
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title(sprintf("PAM %.0f, path %.0f m", pamLevel, pathLengthToPlot));
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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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xlim([15,45]);
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grid on;
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||||
box on;
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||||
legend("Location", "best", "Interpreter", "none");
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||||
end
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||||
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||||
%% Local helpers
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||||
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||||
function berTable = buildBerTable(wh, storageName, pathLength)
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||||
rows = struct( ...
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||||
"pam_level", {}, ...
|
||||
"symbolrate", {}, ...
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||||
"interference_path_length", {}, ...
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||||
"sir", {}, ...
|
||||
"block_update", {}, ...
|
||||
"run_ids", {}, ...
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||||
"n_run_ids", {}, ...
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||||
"ber_values", {}, ...
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||||
"n_ber_values", {});
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||||
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||||
for linIdx = 1:numel(wh.sto.(storageName))
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||||
storedValue = wh.sto.(storageName){linIdx};
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if isempty(storedValue)
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continue
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||||
end
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||||
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||||
phys = linearIndexToStruct(wh, linIdx);
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||||
if phys.interference_path_length ~= pathLength
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||||
continue
|
||||
end
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||||
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||||
berValues = extractBerValues(storedValue);
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||||
if isempty(berValues)
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||||
continue
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||||
end
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||||
runIds = wh.sto.run_id{linIdx};
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||||
if isempty(runIds)
|
||||
runIds = NaN;
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||||
end
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||||
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||||
row = struct();
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||||
row.pam_level = phys.pam_level;
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row.symbolrate = phys.symbolrate;
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row.interference_path_length = phys.interference_path_length;
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row.sir = phys.sir;
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row.block_update = phys.block_update;
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row.run_ids = {runIds};
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||||
row.n_run_ids = numel(runIds);
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row.ber_values = {berValues};
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||||
row.n_ber_values = numel(berValues);
|
||||
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||||
rows(end+1) = row; %#ok<AGROW>
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||||
end
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||||
|
||||
if isempty(rows)
|
||||
berTable = struct2table(rows);
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||||
else
|
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berTable = struct2table(rows);
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||||
berTable = sortrows(berTable, ["pam_level", "sir", "symbolrate"]);
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||||
end
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||||
end
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||||
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||||
function physStruct = linearIndexToStruct(wh, linIdx)
|
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[physValues, physNames] = wh.getPhysIndicesByLinIndex(linIdx);
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physStruct = struct();
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||||
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||||
for paramIdx = 1:numel(physNames)
|
||||
physStruct.(char(physNames{paramIdx})) = physValues{paramIdx};
|
||||
end
|
||||
end
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||||
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function berValues = extractBerValues(value)
|
||||
berValues = [];
|
||||
|
||||
if isstruct(value)
|
||||
if isfield(value, "metrics") && hasBer(value.metrics)
|
||||
berValues(end+1) = value.metrics.BER;
|
||||
end
|
||||
elseif iscell(value)
|
||||
for valueIdx = 1:numel(value)
|
||||
berValues = [berValues, extractBerValues(value{valueIdx})]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function tf = hasBer(metrics)
|
||||
tf = (isstruct(metrics) && isfield(metrics, "BER")) || ...
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||||
(isobject(metrics) && isprop(metrics, "BER"));
|
||||
end
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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
|
||||
% 2) clean data and keep the 1000 m delay regime
|
||||
% 3) plot one BER-over-SIR figure grouped by algorithm and baudrate
|
||||
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||||
clear; clc;
|
||||
|
||||
%% 1) Gather data
|
||||
|
||||
studyName = "block_update_sweep";
|
||||
selectedBlockUpdate = 1;
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||||
selectedPamLevel = 4;
|
||||
selectedPathLength = 1000;
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||||
|
||||
selectedSymbolrates = [96e9 112e9];
|
||||
|
||||
algorithmSelection = table( ...
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||||
["dc_tracking"; ...
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||||
"plain_ffe"; ...
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||||
"a2_tracked_levels"; ...
|
||||
"a2_residual"; ...
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||||
"a1_moving_average"], ...
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||||
[true; true; false; false; false], ...
|
||||
'VariableNames', ["algorithm", "enabled"]);
|
||||
selectedAlgorithms = algorithmSelection.algorithm(algorithmSelection.enabled);
|
||||
|
||||
useBoundedLines = true;
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||||
usePolyfit = true;
|
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polyfitOrderMax = 4;
|
||||
boundaryPolyfitOrderMax = 4;
|
||||
maxBerForPlot = 0.1;
|
||||
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");
|
||||
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);
|
||||
% fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
|
||||
fp.where('MpiReductionResults', 'block_update', 'EQUALS', selectedBlockUpdate);
|
||||
|
||||
selectedFields = { ...
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||||
'Runs.run_id', ...
|
||||
'Runs.sir', ...
|
||||
'Runs.pam_level', ...
|
||||
'Runs.symbolrate', ...
|
||||
'Runs.interference_path_length', ...
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||||
'Runs.power_mpi_interference', ...
|
||||
'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', ...
|
||||
'MpiReductionResults.storage_name', ...
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||||
'MpiReductionResults.algorithm', ...
|
||||
'MpiReductionResults.algorithm_variant', ...
|
||||
'MpiReductionResults.eq_class', ...
|
||||
'MpiReductionResults.block_update', ...
|
||||
'MpiReductionResults.BER', ...
|
||||
'MpiReductionResults.BER_precoded', ...
|
||||
'MpiReductionResults.SNR', ...
|
||||
'MpiReductionResults.GMI', ...
|
||||
'MpiReductionResults.AIR'};
|
||||
selectedFields = selectedFields(:);
|
||||
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d MPI reduction rows.\n", height(rawData));
|
||||
|
||||
%% 2) Clean data
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
|
||||
"interference_path_length", "occurrence_idx", "block_update", ...
|
||||
"power_mpi_interference", "BER", "BER_precoded", "SNR", "GMI", "AIR"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
|
||||
for fieldIdx = 1:numel(stringFields)
|
||||
fieldName = stringFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
data(data.run_id == 3866, :) = [];
|
||||
data(data.run_id == 3865, :) = [];
|
||||
data(data.run_id == 3796, :) = [];
|
||||
data(data.run_id == 3797, :) = [];
|
||||
data(data.run_id == 3798, :) = [];
|
||||
data(data.run_id == 4002, :) = [];
|
||||
data(data.run_id == 4200, :) = [];
|
||||
data(data.run_id == 4199, :) = [];
|
||||
data(data.loop_id == 91, :) = [];
|
||||
data(data.loop_id == 59, :) = [];
|
||||
|
||||
data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
|
||||
data.sir_exact = -7 - data.power_mpi_interference;
|
||||
|
||||
data = data(data.pam_level == selectedPamLevel, :);
|
||||
data = data(data.interference_path_length == selectedPathLength, :);
|
||||
data = data(ismember(data.symbolrate, selectedSymbolrates), :);
|
||||
data = data(ismember(data.algorithm, selectedAlgorithms), :);
|
||||
selectedAlgorithms = selectedAlgorithms(ismember(selectedAlgorithms, unique(data.algorithm, "stable")));
|
||||
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_baudrate_comparison_pam4_1000m:NoRows", ...
|
||||
"No rows remain after BER/study/block/PAM/symbolrate/algorithm filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
data.clean_keep = true(height(data), 1);
|
||||
groupId = findgroups(data.symbolrate, data.algorithm, data.sir);
|
||||
for curGroup = unique(groupId(isfinite(groupId))).'
|
||||
rowMask = groupId == curGroup;
|
||||
berValues = data.BER(rowMask);
|
||||
if nnz(rowMask) > 3
|
||||
data.clean_keep(rowMask) = ~isoutlier(berValues);
|
||||
end
|
||||
end
|
||||
cleanData = data(data.clean_keep, :);
|
||||
|
||||
groupVars = ["symbolrate", "algorithm", "sir"];
|
||||
summaryTable = groupsummary(cleanData, groupVars, ...
|
||||
{"mean", "min", "max"}, "BER");
|
||||
sirExactTable = groupsummary(cleanData, groupVars, "median", "sir_exact");
|
||||
summaryTable = sortrows(summaryTable, groupVars);
|
||||
sirExactTable = sortrows(sirExactTable, groupVars);
|
||||
summaryTable.sir_exact = sirExactTable.median_sir_exact;
|
||||
summaryTable = addBerIntervalBounds(summaryTable, cleanData, groupVars, confidenceLevel);
|
||||
|
||||
fprintf("Cleaned to %d rows across %d baudrate/algorithm/SIR groups.\n", ...
|
||||
height(cleanData), height(summaryTable));
|
||||
disp(groupcounts(cleanData, ["symbolrate", "algorithm"]));
|
||||
|
||||
%% 3) Plot BER-over-SIR algorithm comparison with baudrate curves
|
||||
|
||||
figure(); hold on;
|
||||
|
||||
for algIdx = 1:numel(selectedAlgorithms)
|
||||
algorithmName = selectedAlgorithms(algIdx);
|
||||
algColor = algorithmColor(algorithmName);
|
||||
displayName = algorithmDisplayName(algorithmName);
|
||||
|
||||
for rateIdx = 1:numel(selectedSymbolrates)
|
||||
symbolrate = selectedSymbolrates(rateIdx);
|
||||
[marker, lineStyle] = symbolrateStyle(symbolrate);
|
||||
|
||||
rawMask = cleanData.symbolrate == symbolrate & ...
|
||||
cleanData.algorithm == algorithmName;
|
||||
curveMask = summaryTable.symbolrate == symbolrate & ...
|
||||
summaryTable.algorithm == algorithmName;
|
||||
|
||||
if ~any(curveMask)
|
||||
continue
|
||||
end
|
||||
|
||||
scatterRows = downsampleRows(cleanData(rawMask, :), scatterKeepFraction);
|
||||
% scatter(scatterRows.sir_exact, scatterRows.BER, ...
|
||||
% 14, ...
|
||||
% "Marker", marker, ...
|
||||
% "MarkerEdgeColor", algColor, ...
|
||||
% "MarkerFaceColor", algColor, ...
|
||||
% "MarkerEdgeAlpha", 0.25, ...
|
||||
% "MarkerFaceAlpha", 0.25, ...
|
||||
% "HandleVisibility", "off");
|
||||
|
||||
sirValues = summaryTable.sir_exact(curveMask).';
|
||||
meanBer = summaryTable.mean_BER(curveMask).';
|
||||
[boundCenterBer, boundLowerBer, boundUpperBer] = ...
|
||||
selectBerBounds(summaryTable(curveMask, :), boundMode);
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
|
||||
|
||||
if useBoundedLines && exist("boundedline", "file") && any(valid)
|
||||
[xBand, centerBand, yBounds] = berIntervalBounds( ...
|
||||
sirValues, boundCenterBer, boundLowerBer, boundUpperBer, ...
|
||||
boundMode, boundaryPolyfitOrderMax);
|
||||
|
||||
[hl, hp] = boundedline(xBand, centerBand, yBounds, ...
|
||||
'alpha', 'transparency', 0.08, ...
|
||||
'cmap', algColor, ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
|
||||
set(hp, "LineStyle", "-", "HandleVisibility", "off", "Marker", "none");
|
||||
end
|
||||
|
||||
plot(sirValues(valid), meanBer(valid), ...
|
||||
"LineStyle", lineStyle, ...
|
||||
"Marker", marker, ...
|
||||
"MarkerSize", 3.5, ...
|
||||
"LineWidth", 1, ...
|
||||
"Color", algColor, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", algColor, ...
|
||||
"DisplayName", sprintf("%s, %.0f GBd", displayName, symbolrate * 1e-9));
|
||||
|
||||
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
|
||||
end
|
||||
|
||||
yline(2.2e-4, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(3.8e-3, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
|
||||
% title(sprintf("PAM %.0f, %.0f m, block update %.0f", ...
|
||||
% selectedPamLevel, selectedPathLength, selectedBlockUpdate));
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
set(gca, "YScale", "log");
|
||||
ylim([3e-5, maxBerForPlot]);
|
||||
xlim([14, 45]);
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "northeast", "Interpreter", "none");
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, ...
|
||||
"setmarkers", false, "changemarkers", false);
|
||||
end
|
||||
|
||||
% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/mpi/ber_vs_sir_pam4_baudrates_1000m.tikz","cleanfigure",1);
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function values = stringColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
elseif ischar(values)
|
||||
values = string(values);
|
||||
end
|
||||
values = strip(string(values));
|
||||
end
|
||||
|
||||
function rows = downsampleRows(rows, keepFraction)
|
||||
if keepFraction >= 1 || height(rows) <= 1
|
||||
return
|
||||
end
|
||||
|
||||
keepEvery = max(1, round(1 / keepFraction));
|
||||
keepIdx = 1:keepEvery:height(rows);
|
||||
rows = rows(keepIdx, :);
|
||||
end
|
||||
|
||||
function summaryTable = addBerIntervalBounds(summaryTable, cleanData, groupVars, confidenceLevel)
|
||||
nGroups = height(summaryTable);
|
||||
ciCenter = NaN(nGroups, 1);
|
||||
ciLower = NaN(nGroups, 1);
|
||||
ciUpper = NaN(nGroups, 1);
|
||||
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
|
||||
|
||||
[ciCenter(groupIdx), ciLower(groupIdx), ciUpper(groupIdx)] = ...
|
||||
logBerMeanConfidenceInterval(cleanData.BER(rowMask), confidenceLevel);
|
||||
[stdCenter(groupIdx), stdLower(groupIdx), stdUpper(groupIdx)] = ...
|
||||
logBerMeanStdInterval(cleanData.BER(rowMask));
|
||||
end
|
||||
|
||||
summaryTable.ci_center_BER = ciCenter;
|
||||
summaryTable.ci_lower_BER = ciLower;
|
||||
summaryTable.ci_upper_BER = ciUpper;
|
||||
summaryTable.std_center_BER = stdCenter;
|
||||
summaryTable.std_lower_BER = stdLower;
|
||||
summaryTable.std_upper_BER = stdUpper;
|
||||
end
|
||||
|
||||
function [centerBer, lowerBer, upperBer] = logBerMeanConfidenceInterval(berValues, confidenceLevel)
|
||||
berValues = berValues(isfinite(berValues) & berValues > 0);
|
||||
if isempty(berValues)
|
||||
centerBer = NaN;
|
||||
lowerBer = NaN;
|
||||
upperBer = NaN;
|
||||
return
|
||||
end
|
||||
|
||||
logBer = log10(berValues(:));
|
||||
centerLog = mean(logBer, "omitnan");
|
||||
|
||||
if numel(logBer) < 2
|
||||
centerBer = 10 .^ centerLog;
|
||||
lowerBer = centerBer;
|
||||
upperBer = centerBer;
|
||||
return
|
||||
end
|
||||
|
||||
alpha = 1 - confidenceLevel;
|
||||
if exist("tinv", "file")
|
||||
tCritical = tinv(1 - alpha/2, numel(logBer) - 1);
|
||||
else
|
||||
tCritical = 1.96;
|
||||
end
|
||||
|
||||
halfWidth = tCritical * std(logBer, 0, "omitnan") / sqrt(numel(logBer));
|
||||
centerBer = 10 .^ centerLog;
|
||||
lowerBer = 10 .^ (centerLog - halfWidth);
|
||||
upperBer = 10 .^ (centerLog + halfWidth);
|
||||
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 [centerBer, lowerBer, upperBer] = selectBerBounds(curveSummary, boundMode)
|
||||
if boundMode == "fitCI" || boundMode == "directCI"
|
||||
centerBer = curveSummary.ci_center_BER.';
|
||||
lowerBer = curveSummary.ci_lower_BER.';
|
||||
upperBer = curveSummary.ci_upper_BER.';
|
||||
else
|
||||
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
|
||||
@@ -1,254 +0,0 @@
|
||||
%% BER over SIR from MpiReductionResults
|
||||
% 1) gather data from the database
|
||||
% 2) clean data and remove per-curve outliers
|
||||
% 3) plot BER over SIR grouped by algorithm
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Gather data
|
||||
|
||||
studyName = "pam4_112_greater_3153";
|
||||
pathLengthToPlot = 0;
|
||||
selectedBlockUpdate = 1; % set [] to pool all block_update values
|
||||
selectedPamLevels = []; % set [] to use all PAM levels in the query result
|
||||
selectedAlgorithms = []; % set [] to use all algorithms in the query result
|
||||
|
||||
useBoundedLines = true;
|
||||
usePolyfit = true;
|
||||
polyfitOrderMax = 4;
|
||||
maxBerForPlot = 0.1;
|
||||
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'interference_path_length', 'EQUALS', pathLengthToPlot);
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||
fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
|
||||
if ~isempty(selectedBlockUpdate)
|
||||
fp.where('MpiReductionResults', 'block_update', 'EQUALS', selectedBlockUpdate);
|
||||
end
|
||||
|
||||
selectedFields = { ...
|
||||
'Runs.run_id', ...
|
||||
'Runs.sir', ...
|
||||
'Runs.pam_level', ...
|
||||
'Runs.symbolrate', ...
|
||||
'Runs.interference_path_length', ...
|
||||
'Runs.power_mpi_interference', ...
|
||||
'MpiReductionResults.occurrence_idx', ...
|
||||
'MpiReductionResults.storage_name', ...
|
||||
'MpiReductionResults.algorithm', ...
|
||||
'MpiReductionResults.algorithm_variant', ...
|
||||
'MpiReductionResults.eq_class', ...
|
||||
'MpiReductionResults.block_update', ...
|
||||
'MpiReductionResults.BER', ...
|
||||
'MpiReductionResults.BER_precoded', ...
|
||||
'MpiReductionResults.SNR', ...
|
||||
'MpiReductionResults.GMI', ...
|
||||
'MpiReductionResults.AIR'};
|
||||
selectedFields = selectedFields(:);
|
||||
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d MPI reduction rows.\n", height(rawData));
|
||||
|
||||
%% 2) Clean data
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
|
||||
"interference_path_length", "occurrence_idx", "block_update", ...
|
||||
"power_mpi_interference", "BER", "BER_precoded", "SNR", "GMI", "AIR"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
|
||||
for fieldIdx = 1:numel(stringFields)
|
||||
fieldName = stringFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
|
||||
data.sir_exact = -7 - data.power_mpi_interference;
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_ber_vs_sir:NoRows", ...
|
||||
"No rows remain after initial BER/path/study/block filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
if isempty(selectedPamLevels)
|
||||
selectedPamLevels = unique(data.pam_level(isfinite(data.pam_level))).';
|
||||
else
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
end
|
||||
|
||||
if isempty(selectedAlgorithms)
|
||||
selectedAlgorithms = unique(data.algorithm, "stable").';
|
||||
else
|
||||
selectedAlgorithms = string(selectedAlgorithms);
|
||||
data = data(ismember(data.algorithm, selectedAlgorithms), :);
|
||||
end
|
||||
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_ber_vs_sir:NoSelectedRows", ...
|
||||
"No rows remain after selectedPamLevels/selectedAlgorithms filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
data.clean_keep = true(height(data), 1);
|
||||
[groupId, groupPam, groupAlgorithm, groupSir] = findgroups(data.pam_level, data.algorithm, data.sir);
|
||||
for groupIdx = 1:max(groupId)
|
||||
rowMask = groupId == groupIdx;
|
||||
berValues = data.BER(rowMask);
|
||||
if nnz(rowMask) > 3
|
||||
data.clean_keep(rowMask) = ~isoutlier(berValues);
|
||||
end
|
||||
end
|
||||
cleanData = data(data.clean_keep, :);
|
||||
|
||||
summaryTable = groupsummary(cleanData, ["pam_level", "algorithm", "sir"], ...
|
||||
{"mean", "min", "max"}, "BER");
|
||||
sirExactTable = groupsummary(cleanData, ["pam_level", "algorithm", "sir"], ...
|
||||
"median", "sir_exact");
|
||||
summaryTable.sir_exact = sirExactTable.median_sir_exact;
|
||||
summaryTable = sortrows(summaryTable, ["pam_level", "algorithm", "sir"]);
|
||||
|
||||
fprintf("Cleaned to %d rows across %d PAM/algorithm/SIR groups.\n", ...
|
||||
height(cleanData), height(summaryTable));
|
||||
disp(groupcounts(cleanData, ["pam_level", "algorithm"]));
|
||||
|
||||
%% 3) Plot data
|
||||
|
||||
if exist("linspecer", "file")
|
||||
algColors = linspecer(numel(selectedAlgorithms));
|
||||
else
|
||||
algColors = lines(numel(selectedAlgorithms));
|
||||
end
|
||||
|
||||
figure(); clf;
|
||||
tiledlayout(numel(selectedPamLevels), 1, "TileSpacing", "compact");
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
pamLevel = selectedPamLevels(pamIdx);
|
||||
nexttile; hold on;
|
||||
|
||||
for algIdx = 1:numel(selectedAlgorithms)
|
||||
algorithmName = selectedAlgorithms(algIdx);
|
||||
algColor = algColors(algIdx, :);
|
||||
rawMask = cleanData.pam_level == pamLevel & cleanData.algorithm == algorithmName;
|
||||
curveMask = summaryTable.pam_level == pamLevel & summaryTable.algorithm == algorithmName;
|
||||
|
||||
if ~any(curveMask)
|
||||
continue
|
||||
end
|
||||
scatter(cleanData.sir_exact(rawMask), cleanData.BER(rawMask), ...
|
||||
30, ...
|
||||
"Marker", ".", ...
|
||||
"MarkerEdgeColor", algColor, ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
sirValues = summaryTable.sir_exact(curveMask).';
|
||||
meanBer = summaryTable.mean_BER(curveMask).';
|
||||
minBer = summaryTable.min_BER(curveMask).';
|
||||
maxBer = summaryTable.max_BER(curveMask).';
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
|
||||
|
||||
if useBoundedLines && exist("boundedline", "file") && any(valid)
|
||||
yLower = max(meanBer - minBer, 0);
|
||||
yUpper = max(maxBer - meanBer, 0);
|
||||
yBounds = [yLower(:), yUpper(:)];
|
||||
|
||||
[hl, hp] = boundedline(sirValues(valid).', meanBer(valid).', yBounds(valid, :), ...
|
||||
'alpha', 'transparency', 0.08, ...
|
||||
'cmap', algColor, ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
|
||||
set(hp, "LineStyle", "none", "HandleVisibility", "off");
|
||||
end
|
||||
|
||||
plot(sirValues(valid), meanBer(valid), ...
|
||||
"LineStyle", "none", ...
|
||||
"Marker", "o", ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.2, ...
|
||||
"Color", algColor, ...
|
||||
"MarkerFaceColor", algColor, ...
|
||||
"DisplayName", char(algorithmName));
|
||||
|
||||
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(3.8e-3, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
|
||||
title(sprintf("PAM %.0f, path %.0f m, block update %s", ...
|
||||
pamLevel, pathLengthToPlot, blockUpdateLabel(selectedBlockUpdate)));
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
set(gca, "YScale", "log");
|
||||
ylim([9e-5, maxBerForPlot]);
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
|
||||
end
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function values = stringColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
elseif ischar(values)
|
||||
values = string(values);
|
||||
end
|
||||
values = strip(string(values));
|
||||
end
|
||||
|
||||
function label = blockUpdateLabel(selectedBlockUpdate)
|
||||
if isempty(selectedBlockUpdate)
|
||||
label = "pooled";
|
||||
else
|
||||
label = string(selectedBlockUpdate);
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,534 @@
|
||||
%% BER over SIR by MPI delay/coherence regime from MpiReductionResults
|
||||
% 1) gather data from the database
|
||||
% 2) clean data and assign path-length regimes
|
||||
% 3) plot one BER-over-SIR figure per regime grouped by algorithm
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Gather data
|
||||
|
||||
studyName = "block_update_sweep";
|
||||
selectedBlockUpdate = 1; % set [] to pool all block_update values
|
||||
selectedPamLevels = 6; % set [] to use all PAM levels in the query result
|
||||
|
||||
algorithmSelection = table( ...
|
||||
["plain_ffe"; ...
|
||||
"a2_tracked_levels"; ...
|
||||
"a2_residual"; ...
|
||||
"a1_moving_average"; ...
|
||||
"dc_tracking"], ...
|
||||
[true; true; true; true; true], ...
|
||||
'VariableNames', ["algorithm", "enabled"]);
|
||||
selectedAlgorithms = algorithmSelection.algorithm(algorithmSelection.enabled);
|
||||
|
||||
useBoundedLines = true;
|
||||
usePolyfit = true;
|
||||
polyfitOrderMax = 4;
|
||||
boundaryPolyfitOrderMax = 4;
|
||||
maxBerForPlot = 0.1;
|
||||
scatterKeepFraction = 0.2;
|
||||
boundMode = "fitStd"; % "fitStd", "directStd", "fitCI", or "directCI"
|
||||
confidenceLevel = 0.95;
|
||||
regimeNames = ["0-1 m", "10-100 m", "300 m", "1000 m"];
|
||||
algorithmMarkers = {'o','square','diamond','^','v','>','<','pentagram'};
|
||||
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
% fp.where('Runs','run_id','GREATER_EQUAL',3153);
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||
fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
|
||||
% fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
|
||||
if ~isempty(selectedBlockUpdate)
|
||||
fp.where('MpiReductionResults', 'block_update', 'EQUALS', selectedBlockUpdate);
|
||||
end
|
||||
if isscalar(selectedPamLevels) && ~isempty(selectedPamLevels)
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', selectedPamLevels);
|
||||
end
|
||||
|
||||
selectedFields = { ...
|
||||
'Runs.run_id', ...
|
||||
'Runs.sir', ...
|
||||
'Runs.pam_level', ...
|
||||
'Runs.symbolrate', ...
|
||||
'Runs.interference_path_length', ...
|
||||
'Runs.power_mpi_interference', ...
|
||||
'Runs.power_pd_in', ...
|
||||
'Runs.v_bias', ...
|
||||
'Runs.loop_id', ...
|
||||
'MpiReductionResults.occurrence_idx', ...
|
||||
'MpiReductionResults.storage_name', ...
|
||||
'MpiReductionResults.algorithm', ...
|
||||
'MpiReductionResults.algorithm_variant', ...
|
||||
'MpiReductionResults.eq_class', ...
|
||||
'MpiReductionResults.block_update', ...
|
||||
'MpiReductionResults.BER', ...
|
||||
'MpiReductionResults.BER_precoded', ...
|
||||
'MpiReductionResults.SNR', ...
|
||||
'MpiReductionResults.GMI', ...
|
||||
'MpiReductionResults.AIR'};
|
||||
selectedFields = selectedFields(:);
|
||||
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d MPI reduction rows.\n", height(rawData));
|
||||
|
||||
%% 2) Clean data and assign regimes
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
|
||||
"interference_path_length", "occurrence_idx", "block_update", ...
|
||||
"power_mpi_interference", "BER", "BER_precoded", "SNR", "GMI", "AIR"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
|
||||
for fieldIdx = 1:numel(stringFields)
|
||||
fieldName = stringFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
% data = data(data.run_id>3153,:);
|
||||
data(data.run_id == 3866, :) = [];
|
||||
data(data.run_id == 3865, :) = [];
|
||||
data(data.run_id == 3796, :) = [];
|
||||
data(data.run_id == 3797, :) = [];
|
||||
data(data.run_id == 3798, :) = [];
|
||||
data(data.run_id == 4002, :) = [];
|
||||
data(data.run_id == 4200, :) = [];
|
||||
data(data.run_id == 4199, :) = [];
|
||||
data(data.loop_id == 91, :) = [];
|
||||
data(data.loop_id == 59, :) = [];
|
||||
|
||||
data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
|
||||
data.sir_exact = -7 - data.power_mpi_interference;
|
||||
|
||||
data.path_regime = strings(height(data), 1);
|
||||
data.path_regime(data.interference_path_length == 0 | ...
|
||||
data.interference_path_length == 1) = "0-1 m";
|
||||
data.path_regime(data.interference_path_length >= 10 & ...
|
||||
data.interference_path_length <= 100) = "10-100 m";
|
||||
data.path_regime(data.interference_path_length == 300) = "300 m";
|
||||
data.path_regime(data.interference_path_length == 1000) = "1000 m";
|
||||
data = data(data.path_regime ~= "", :);
|
||||
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_delay_regimes:NoRows", ...
|
||||
"No rows remain after BER/study/block/regime filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
if isempty(selectedPamLevels)
|
||||
selectedPamLevels = unique(data.pam_level(isfinite(data.pam_level))).';
|
||||
else
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
end
|
||||
|
||||
data = data(ismember(data.algorithm, selectedAlgorithms), :);
|
||||
selectedAlgorithms = selectedAlgorithms(ismember(selectedAlgorithms, unique(data.algorithm, "stable")));
|
||||
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_delay_regimes:NoSelectedRows", ...
|
||||
"No rows remain after selectedPamLevels/selectedAlgorithms filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
data.clean_keep = true(height(data), 1);
|
||||
groupId = findgroups(data.path_regime, data.pam_level, data.algorithm, data.sir);
|
||||
for curGroup = unique(groupId(isfinite(groupId))).'
|
||||
rowMask = groupId == curGroup;
|
||||
berValues = data.BER(rowMask);
|
||||
if nnz(rowMask) > 3
|
||||
data.clean_keep(rowMask) = ~isoutlier(berValues);
|
||||
end
|
||||
end
|
||||
cleanData = data(data.clean_keep, :);
|
||||
|
||||
groupVars = ["path_regime", "pam_level", "algorithm", "sir"];
|
||||
summaryTable = groupsummary(cleanData, groupVars, ...
|
||||
{"mean", "min", "max"}, "BER");
|
||||
sirExactTable = groupsummary(cleanData, groupVars, "median", "sir_exact");
|
||||
summaryTable = sortrows(summaryTable, groupVars);
|
||||
sirExactTable = sortrows(sirExactTable, groupVars);
|
||||
summaryTable.sir_exact = sirExactTable.median_sir_exact;
|
||||
summaryTable = addBerIntervalBounds(summaryTable, cleanData, groupVars, confidenceLevel);
|
||||
|
||||
fprintf("Cleaned to %d rows across %d regime/PAM/algorithm/SIR groups.\n", ...
|
||||
height(cleanData), height(summaryTable));
|
||||
disp(groupcounts(cleanData, ["path_regime", "pam_level", "algorithm"]));
|
||||
|
||||
%% 3) Plot one BER-over-SIR figure per delay/coherence regime
|
||||
|
||||
for regimeIdx = 1:numel(regimeNames)
|
||||
regimeName = regimeNames(regimeIdx);
|
||||
regimeDataMask = cleanData.path_regime == regimeName;
|
||||
if ~any(regimeDataMask)
|
||||
fprintf("Skipping regime %s: no rows.\n", regimeName);
|
||||
continue
|
||||
end
|
||||
|
||||
figure(); clf;
|
||||
tiledlayout(numel(selectedPamLevels), 1, "TileSpacing", "compact");
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
pamLevel = selectedPamLevels(pamIdx);
|
||||
nexttile; hold on;
|
||||
|
||||
for algIdx = 1:numel(selectedAlgorithms)
|
||||
algorithmName = selectedAlgorithms(algIdx);
|
||||
algColor = algorithmColor(algorithmName);
|
||||
marker = algorithmMarker(algorithmName, algorithmMarkers);
|
||||
displayName = algorithmDisplayName(algorithmName);
|
||||
|
||||
rawMask = cleanData.path_regime == regimeName & ...
|
||||
cleanData.pam_level == pamLevel & ...
|
||||
cleanData.algorithm == algorithmName;
|
||||
curveMask = summaryTable.path_regime == regimeName & ...
|
||||
summaryTable.pam_level == pamLevel & ...
|
||||
summaryTable.algorithm == algorithmName;
|
||||
|
||||
if ~any(curveMask)
|
||||
continue
|
||||
end
|
||||
|
||||
if regimeIdx == 2 || regimeIdx == 1
|
||||
scatterKeepFraction = 0.2;
|
||||
else
|
||||
scatterKeepFraction = 1;
|
||||
end
|
||||
|
||||
scatterRows = downsampleRows(cleanData(rawMask, :), scatterKeepFraction);
|
||||
scatter(scatterRows.sir_exact, scatterRows.BER, ...
|
||||
26, ...
|
||||
"Marker", ".", ...
|
||||
"MarkerEdgeColor", algColor, ...
|
||||
"MarkerFaceColor", algColor, ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
sirValues = summaryTable.sir_exact(curveMask).';
|
||||
meanBer = summaryTable.mean_BER(curveMask).';
|
||||
[boundCenterBer, boundLowerBer, boundUpperBer] = ...
|
||||
selectBerBounds(summaryTable(curveMask, :), boundMode);
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
|
||||
|
||||
if useBoundedLines && exist("boundedline", "file") && any(valid)
|
||||
[xBand, centerBand, yBounds] = berIntervalBounds( ...
|
||||
sirValues, boundCenterBer, boundLowerBer, boundUpperBer, ...
|
||||
boundMode, boundaryPolyfitOrderMax);
|
||||
|
||||
[hl, hp] = boundedline(xBand, centerBand, yBounds, ...
|
||||
'alpha', 'transparency', 0.18, ...
|
||||
'cmap', algColor, ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
set(hl, "LineStyle", "-",'LineWidth',1, "Marker", "none", "HandleVisibility", "on","DisplayName", char(displayName));
|
||||
set(hp, "LineStyle", "-", "HandleVisibility", "off","Marker", "none");
|
||||
end
|
||||
|
||||
plot(sirValues(valid), meanBer(valid), ...
|
||||
"LineStyle", "none", ...
|
||||
"Marker", marker, ...
|
||||
"MarkerSize", 3, ...
|
||||
"LineWidth", 1, ...
|
||||
"Color", algColor, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", algColor, ...
|
||||
"DisplayName", char(displayName),'HandleVisibility','off');
|
||||
|
||||
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(3.8e-3, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
|
||||
title(sprintf("PAM %.0f, interference path regime %s", pamLevel, regimeName));
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
set(gca, "YScale", "log");
|
||||
ylim([9e-5, maxBerForPlot]);
|
||||
xlim([15, 45]);
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "northeast", "Interpreter", "none");
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
|
||||
end
|
||||
end
|
||||
% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/mpi/ber_vs_sir_" + regimeName + ".tikz","cleanfigure",1);
|
||||
end
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function values = stringColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
elseif ischar(values)
|
||||
values = string(values);
|
||||
end
|
||||
values = strip(string(values));
|
||||
end
|
||||
|
||||
function rows = downsampleRows(rows, keepFraction)
|
||||
if keepFraction >= 1 || height(rows) <= 1
|
||||
return
|
||||
end
|
||||
|
||||
keepEvery = max(1, round(1 / keepFraction));
|
||||
keepIdx = 1:keepEvery:height(rows);
|
||||
rows = rows(keepIdx, :);
|
||||
end
|
||||
|
||||
function summaryTable = addBerIntervalBounds(summaryTable, cleanData, groupVars, confidenceLevel)
|
||||
nGroups = height(summaryTable);
|
||||
ciCenter = NaN(nGroups, 1);
|
||||
ciLower = NaN(nGroups, 1);
|
||||
ciUpper = NaN(nGroups, 1);
|
||||
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
|
||||
|
||||
[ciCenter(groupIdx), ciLower(groupIdx), ciUpper(groupIdx)] = ...
|
||||
logBerMeanConfidenceInterval(cleanData.BER(rowMask), confidenceLevel);
|
||||
[stdCenter(groupIdx), stdLower(groupIdx), stdUpper(groupIdx)] = ...
|
||||
logBerMeanStdInterval(cleanData.BER(rowMask));
|
||||
end
|
||||
|
||||
summaryTable.ci_center_BER = ciCenter;
|
||||
summaryTable.ci_lower_BER = ciLower;
|
||||
summaryTable.ci_upper_BER = ciUpper;
|
||||
summaryTable.std_center_BER = stdCenter;
|
||||
summaryTable.std_lower_BER = stdLower;
|
||||
summaryTable.std_upper_BER = stdUpper;
|
||||
end
|
||||
|
||||
function [centerBer, lowerBer, upperBer] = logBerMeanConfidenceInterval(berValues, confidenceLevel)
|
||||
berValues = berValues(isfinite(berValues) & berValues > 0);
|
||||
if isempty(berValues)
|
||||
centerBer = NaN;
|
||||
lowerBer = NaN;
|
||||
upperBer = NaN;
|
||||
return
|
||||
end
|
||||
|
||||
logBer = log10(berValues(:));
|
||||
centerLog = mean(logBer, "omitnan");
|
||||
|
||||
if numel(logBer) < 2
|
||||
centerBer = 10 .^ centerLog;
|
||||
lowerBer = centerBer;
|
||||
upperBer = centerBer;
|
||||
return
|
||||
end
|
||||
|
||||
alpha = 1 - confidenceLevel;
|
||||
if exist("tinv", "file")
|
||||
tCritical = tinv(1 - alpha/2, numel(logBer) - 1);
|
||||
else
|
||||
tCritical = 1.96;
|
||||
end
|
||||
|
||||
halfWidth = tCritical * std(logBer, 0, "omitnan") / sqrt(numel(logBer));
|
||||
centerBer = 10 .^ centerLog;
|
||||
lowerBer = 10 .^ (centerLog - halfWidth);
|
||||
upperBer = 10 .^ (centerLog + halfWidth);
|
||||
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 [centerBer, lowerBer, upperBer] = selectBerBounds(curveSummary, boundMode)
|
||||
if boundMode == "fitCI" || boundMode == "directCI"
|
||||
centerBer = curveSummary.ci_center_BER.';
|
||||
lowerBer = curveSummary.ci_lower_BER.';
|
||||
upperBer = curveSummary.ci_upper_BER.';
|
||||
else
|
||||
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 = algorithmMarker(algorithmName, algorithmMarkers)
|
||||
algorithmOrder = ["plain_ffe", "a2_tracked_levels", "a2_residual", ...
|
||||
"a1_moving_average", "dc_tracking"];
|
||||
markerIdx = find(algorithmOrder == string(algorithmName), 1);
|
||||
if isempty(markerIdx)
|
||||
markerIdx = 1;
|
||||
end
|
||||
marker = algorithmMarkers{mod(markerIdx - 1, numel(algorithmMarkers)) + 1};
|
||||
end
|
||||
@@ -0,0 +1,514 @@
|
||||
%% BER over SIR by MPI delay/coherence regime and PAM level
|
||||
% 1) gather block_update = 1 data from the database
|
||||
% 2) clean data and assign path-length regimes
|
||||
% 3) plot PAM4/PAM6/PAM8 BER-over-SIR curves in the same figure
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Gather data
|
||||
|
||||
studyName = "block_update_sweep";
|
||||
selectedBlockUpdate = 1;
|
||||
selectedPamLevels = [4 6 8];
|
||||
|
||||
algorithmSelection = table( ...
|
||||
["dc_tracking"; ...
|
||||
"plain_ffe"; ...
|
||||
"a2_tracked_levels"; ...
|
||||
"a2_residual"; ...
|
||||
"a1_moving_average"], ...
|
||||
[true; true; false; false; false], ...
|
||||
'VariableNames', ["algorithm", "enabled"]);
|
||||
selectedAlgorithms = algorithmSelection.algorithm(algorithmSelection.enabled);
|
||||
|
||||
useBoundedLines = true;
|
||||
usePolyfit = true;
|
||||
polyfitOrderMax = 4;
|
||||
boundaryPolyfitOrderMax = 4;
|
||||
maxBerForPlot = 0.1;
|
||||
scatterKeepFraction = 0.2;
|
||||
boundMode = "fitStd"; % "fitStd", "directStd", "fitCI", or "directCI"
|
||||
confidenceLevel = 0.95;
|
||||
regimeNames = ["0-1 m", "10-100 m", "300 m", "1000 m"];
|
||||
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||
fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
|
||||
fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
|
||||
fp.where('MpiReductionResults', 'block_update', 'EQUALS', selectedBlockUpdate);
|
||||
|
||||
selectedFields = { ...
|
||||
'Runs.run_id', ...
|
||||
'Runs.sir', ...
|
||||
'Runs.pam_level', ...
|
||||
'Runs.symbolrate', ...
|
||||
'Runs.interference_path_length', ...
|
||||
'Runs.power_mpi_interference', ...
|
||||
'Runs.power_pd_in', ...
|
||||
'Runs.v_bias', ...
|
||||
'Runs.loop_id', ...
|
||||
'MpiReductionResults.occurrence_idx', ...
|
||||
'MpiReductionResults.storage_name', ...
|
||||
'MpiReductionResults.algorithm', ...
|
||||
'MpiReductionResults.algorithm_variant', ...
|
||||
'MpiReductionResults.eq_class', ...
|
||||
'MpiReductionResults.block_update', ...
|
||||
'MpiReductionResults.BER', ...
|
||||
'MpiReductionResults.BER_precoded', ...
|
||||
'MpiReductionResults.SNR', ...
|
||||
'MpiReductionResults.GMI', ...
|
||||
'MpiReductionResults.AIR'};
|
||||
selectedFields = selectedFields(:);
|
||||
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d MPI reduction rows.\n", height(rawData));
|
||||
|
||||
%% 2) Clean data and assign regimes
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
|
||||
"interference_path_length", "occurrence_idx", "block_update", ...
|
||||
"power_mpi_interference", "BER", "BER_precoded", "SNR", "GMI", "AIR"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
|
||||
for fieldIdx = 1:numel(stringFields)
|
||||
fieldName = stringFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
data(data.run_id == 3866, :) = [];
|
||||
data(data.run_id == 3865, :) = [];
|
||||
data(data.run_id == 3796, :) = [];
|
||||
data(data.run_id == 3797, :) = [];
|
||||
data(data.run_id == 3798, :) = [];
|
||||
data(data.run_id == 4002, :) = [];
|
||||
data(data.run_id == 4200, :) = [];
|
||||
data(data.run_id == 4199, :) = [];
|
||||
data(data.loop_id == 91, :) = [];
|
||||
data(data.loop_id == 59, :) = [];
|
||||
|
||||
data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
|
||||
data.sir_exact = -7 - data.power_mpi_interference;
|
||||
|
||||
data.path_regime = strings(height(data), 1);
|
||||
data.path_regime(data.interference_path_length == 0 | ...
|
||||
data.interference_path_length == 1) = "0-1 m";
|
||||
data.path_regime(data.interference_path_length >= 10 & ...
|
||||
data.interference_path_length <= 100) = "10-100 m";
|
||||
data.path_regime(data.interference_path_length == 300) = "300 m";
|
||||
data.path_regime(data.interference_path_length == 1000) = "1000 m";
|
||||
data = data(data.path_regime ~= "", :);
|
||||
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
data = data(ismember(data.algorithm, selectedAlgorithms), :);
|
||||
selectedAlgorithms = selectedAlgorithms(ismember(selectedAlgorithms, unique(data.algorithm, "stable")));
|
||||
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_delay_regimes_pam_comparison:NoRows", ...
|
||||
"No rows remain after BER/study/block/PAM/algorithm/regime filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
data.clean_keep = true(height(data), 1);
|
||||
groupId = findgroups(data.path_regime, data.pam_level, data.algorithm, data.sir);
|
||||
for curGroup = unique(groupId(isfinite(groupId))).'
|
||||
rowMask = groupId == curGroup;
|
||||
berValues = data.BER(rowMask);
|
||||
if nnz(rowMask) > 3
|
||||
data.clean_keep(rowMask) = ~isoutlier(berValues);
|
||||
end
|
||||
end
|
||||
cleanData = data(data.clean_keep, :);
|
||||
|
||||
groupVars = ["path_regime", "pam_level", "algorithm", "sir"];
|
||||
summaryTable = groupsummary(cleanData, groupVars, ...
|
||||
{"mean", "min", "max"}, "BER");
|
||||
sirExactTable = groupsummary(cleanData, groupVars, "median", "sir_exact");
|
||||
summaryTable = sortrows(summaryTable, groupVars);
|
||||
sirExactTable = sortrows(sirExactTable, groupVars);
|
||||
summaryTable.sir_exact = sirExactTable.median_sir_exact;
|
||||
summaryTable = addBerIntervalBounds(summaryTable, cleanData, groupVars, confidenceLevel);
|
||||
|
||||
fprintf("Cleaned to %d rows across %d regime/PAM/algorithm/SIR groups.\n", ...
|
||||
height(cleanData), height(summaryTable));
|
||||
disp(groupcounts(cleanData, ["path_regime", "pam_level", "algorithm"]));
|
||||
|
||||
%% 3) Plot PAM curves in one delay-regime comparison figure
|
||||
|
||||
|
||||
|
||||
for regimeIdx = 1:numel(regimeNames)
|
||||
regimeName = regimeNames(regimeIdx);
|
||||
figure(); hold on;
|
||||
|
||||
for algIdx = 1:numel(selectedAlgorithms)
|
||||
algorithmName = selectedAlgorithms(algIdx);
|
||||
algColor = algorithmColor(algorithmName);
|
||||
displayName = algorithmDisplayName(algorithmName);
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
pamLevel = selectedPamLevels(pamIdx);
|
||||
[marker, lineStyle] = pamStyle(pamLevel);
|
||||
|
||||
rawMask = cleanData.path_regime == regimeName & ...
|
||||
cleanData.pam_level == pamLevel & ...
|
||||
cleanData.algorithm == algorithmName;
|
||||
curveMask = summaryTable.path_regime == regimeName & ...
|
||||
summaryTable.pam_level == pamLevel & ...
|
||||
summaryTable.algorithm == algorithmName;
|
||||
|
||||
if ~any(curveMask)
|
||||
continue
|
||||
end
|
||||
|
||||
scatterRows = downsampleRows(cleanData(rawMask, :), scatterKeepFraction);
|
||||
% scatter(scatterRows.sir_exact, scatterRows.BER, ...
|
||||
% 14, ...
|
||||
% "Marker", marker, ...
|
||||
% "MarkerEdgeColor", algColor, ...
|
||||
% "MarkerFaceColor", algColor, ...
|
||||
% "MarkerEdgeAlpha", 0.35, ...
|
||||
% "MarkerFaceAlpha", 0.35, ...
|
||||
% "HandleVisibility", "off");
|
||||
|
||||
sirValues = summaryTable.sir_exact(curveMask).';
|
||||
meanBer = summaryTable.mean_BER(curveMask).';
|
||||
[boundCenterBer, boundLowerBer, boundUpperBer] = ...
|
||||
selectBerBounds(summaryTable(curveMask, :), boundMode);
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
|
||||
|
||||
if useBoundedLines && exist("boundedline", "file") && any(valid)
|
||||
[xBand, centerBand, yBounds] = berIntervalBounds( ...
|
||||
sirValues, boundCenterBer, boundLowerBer, boundUpperBer, ...
|
||||
boundMode, boundaryPolyfitOrderMax);
|
||||
|
||||
[hl, hp] = boundedline(xBand, centerBand, yBounds, ...
|
||||
'alpha', 'transparency', 0.08, ...
|
||||
'cmap', algColor, ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
|
||||
set(hp, "LineStyle", "-", "HandleVisibility", "off", "Marker", "none");
|
||||
end
|
||||
|
||||
plot(sirValues(valid), meanBer(valid), ...
|
||||
"LineStyle", lineStyle, ...
|
||||
"Marker", marker, ...
|
||||
"MarkerSize", 3.5, ...
|
||||
"LineWidth", 1, ...
|
||||
"Color", algColor, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", algColor, ...
|
||||
"DisplayName", sprintf("%s, PAM %.0f", displayName, pamLevel));
|
||||
|
||||
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
|
||||
end
|
||||
|
||||
yline(2.2e-4, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(3.8e-3, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
|
||||
title(sprintf("Interference path regime %s", regimeName));
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
set(gca, "YScale", "log");
|
||||
ylim([9e-5, maxBerForPlot]);
|
||||
xlim([15, 45]);
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "northeast", "Interpreter", "none");
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, ...
|
||||
"setmarkers", false, "changemarkers", false);
|
||||
end
|
||||
end
|
||||
|
||||
% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/mpi/ber_vs_sir_delay_regimes_pam_comparison.tikz","cleanfigure",1);
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function values = stringColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
elseif ischar(values)
|
||||
values = string(values);
|
||||
end
|
||||
values = strip(string(values));
|
||||
end
|
||||
|
||||
function rows = downsampleRows(rows, keepFraction)
|
||||
if keepFraction >= 1 || height(rows) <= 1
|
||||
return
|
||||
end
|
||||
|
||||
keepEvery = max(1, round(1 / keepFraction));
|
||||
keepIdx = 1:keepEvery:height(rows);
|
||||
rows = rows(keepIdx, :);
|
||||
end
|
||||
|
||||
function summaryTable = addBerIntervalBounds(summaryTable, cleanData, groupVars, confidenceLevel)
|
||||
nGroups = height(summaryTable);
|
||||
ciCenter = NaN(nGroups, 1);
|
||||
ciLower = NaN(nGroups, 1);
|
||||
ciUpper = NaN(nGroups, 1);
|
||||
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
|
||||
|
||||
[ciCenter(groupIdx), ciLower(groupIdx), ciUpper(groupIdx)] = ...
|
||||
logBerMeanConfidenceInterval(cleanData.BER(rowMask), confidenceLevel);
|
||||
[stdCenter(groupIdx), stdLower(groupIdx), stdUpper(groupIdx)] = ...
|
||||
logBerMeanStdInterval(cleanData.BER(rowMask));
|
||||
end
|
||||
|
||||
summaryTable.ci_center_BER = ciCenter;
|
||||
summaryTable.ci_lower_BER = ciLower;
|
||||
summaryTable.ci_upper_BER = ciUpper;
|
||||
summaryTable.std_center_BER = stdCenter;
|
||||
summaryTable.std_lower_BER = stdLower;
|
||||
summaryTable.std_upper_BER = stdUpper;
|
||||
end
|
||||
|
||||
function [centerBer, lowerBer, upperBer] = logBerMeanConfidenceInterval(berValues, confidenceLevel)
|
||||
berValues = berValues(isfinite(berValues) & berValues > 0);
|
||||
if isempty(berValues)
|
||||
centerBer = NaN;
|
||||
lowerBer = NaN;
|
||||
upperBer = NaN;
|
||||
return
|
||||
end
|
||||
|
||||
logBer = log10(berValues(:));
|
||||
centerLog = mean(logBer, "omitnan");
|
||||
|
||||
if numel(logBer) < 2
|
||||
centerBer = 10 .^ centerLog;
|
||||
lowerBer = centerBer;
|
||||
upperBer = centerBer;
|
||||
return
|
||||
end
|
||||
|
||||
alpha = 1 - confidenceLevel;
|
||||
if exist("tinv", "file")
|
||||
tCritical = tinv(1 - alpha/2, numel(logBer) - 1);
|
||||
else
|
||||
tCritical = 1.96;
|
||||
end
|
||||
|
||||
halfWidth = tCritical * std(logBer, 0, "omitnan") / sqrt(numel(logBer));
|
||||
centerBer = 10 .^ centerLog;
|
||||
lowerBer = 10 .^ (centerLog - halfWidth);
|
||||
upperBer = 10 .^ (centerLog + halfWidth);
|
||||
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 [centerBer, lowerBer, upperBer] = selectBerBounds(curveSummary, boundMode)
|
||||
if boundMode == "fitCI" || boundMode == "directCI"
|
||||
centerBer = curveSummary.ci_center_BER.';
|
||||
lowerBer = curveSummary.ci_lower_BER.';
|
||||
upperBer = curveSummary.ci_upper_BER.';
|
||||
else
|
||||
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] = pamStyle(pamLevel)
|
||||
switch pamLevel
|
||||
case 4
|
||||
lineStyle = "-";
|
||||
marker = "o";
|
||||
case 6
|
||||
lineStyle = "--";
|
||||
marker = "square";
|
||||
case 8
|
||||
lineStyle = ":";
|
||||
marker = "diamond";
|
||||
otherwise
|
||||
lineStyle = "-.";
|
||||
marker = "^";
|
||||
end
|
||||
end
|
||||
@@ -0,0 +1,346 @@
|
||||
%% BER over SIR by MPI delay/coherence regime and loop id
|
||||
% 1) gather dc_tracking data from the database
|
||||
% 2) clean data and assign path-length regimes
|
||||
% 3) plot one BER-over-SIR figure per regime grouped by loop_id
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Gather data
|
||||
|
||||
studyName = "pam4_112_greater_3153";
|
||||
selectedBlockUpdate = 1; % set [] to pool all block_update values
|
||||
selectedPamLevels = 4; % set [] to use all PAM levels in the query result
|
||||
selectedAlgorithm = "plain_ffe";
|
||||
|
||||
useBoundedLines = defaultUseBoundedLines();
|
||||
usePolyfit = true;
|
||||
polyfitOrderMax = 4;
|
||||
maxBerForPlot = 0.1;
|
||||
regimeNames = ["0-1 m", "10-100 m", "300 m", "1000 m"];
|
||||
loopMarkers = {'o','square','diamond','^','v','>','<','pentagram'};
|
||||
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||
fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
|
||||
fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
|
||||
fp.where('MpiReductionResults', 'algorithm', 'EQUALS', char(selectedAlgorithm));
|
||||
if ~isempty(selectedBlockUpdate)
|
||||
fp.where('MpiReductionResults', 'block_update', 'EQUALS', selectedBlockUpdate);
|
||||
end
|
||||
if isscalar(selectedPamLevels) && ~isempty(selectedPamLevels)
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', selectedPamLevels);
|
||||
end
|
||||
|
||||
selectedFields = { ...
|
||||
'Runs.run_id', ...
|
||||
'Runs.sir', ...
|
||||
'Runs.pam_level', ...
|
||||
'Runs.symbolrate', ...
|
||||
'Runs.interference_path_length', ...
|
||||
'Runs.power_mpi_interference', ...
|
||||
'Runs.power_pd_in', ...
|
||||
'Runs.v_bias', ...
|
||||
'Runs.loop_id', ...
|
||||
'MpiReductionResults.occurrence_idx', ...
|
||||
'MpiReductionResults.storage_name', ...
|
||||
'MpiReductionResults.algorithm', ...
|
||||
'MpiReductionResults.algorithm_variant', ...
|
||||
'MpiReductionResults.eq_class', ...
|
||||
'MpiReductionResults.block_update', ...
|
||||
'MpiReductionResults.BER', ...
|
||||
'MpiReductionResults.BER_precoded', ...
|
||||
'MpiReductionResults.SNR', ...
|
||||
'MpiReductionResults.GMI', ...
|
||||
'MpiReductionResults.AIR'};
|
||||
selectedFields = selectedFields(:);
|
||||
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d %s MPI reduction rows.\n", ...
|
||||
height(rawData), algorithmDisplayName(selectedAlgorithm));
|
||||
|
||||
%% 2) Clean data and assign regimes
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
|
||||
"interference_path_length", "occurrence_idx", "block_update", ...
|
||||
"power_mpi_interference", "power_pd_in", "v_bias", "loop_id", "BER", "BER_precoded", ...
|
||||
"SNR", "GMI", "AIR"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
|
||||
for fieldIdx = 1:numel(stringFields)
|
||||
fieldName = stringFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
data(data.run_id == 3866, :) = [];
|
||||
data(data.run_id == 3865, :) = [];
|
||||
data(data.run_id == 3796, :) = [];
|
||||
data(data.run_id == 3797, :) = [];
|
||||
data(data.run_id == 3798, :) = [];
|
||||
data(data.run_id == 4002, :) = [];
|
||||
data(data.run_id == 4200, :) = [];
|
||||
data(data.run_id == 4199, :) = [];
|
||||
data(data.loop_id == 91, :) = [];
|
||||
|
||||
|
||||
data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
|
||||
data = data(isfinite(data.v_bias), :);
|
||||
data.sir_exact = -7 - data.power_mpi_interference;
|
||||
|
||||
data.path_regime = strings(height(data), 1);
|
||||
data.path_regime(data.interference_path_length == 0 | ...
|
||||
data.interference_path_length == 1) = "0-1 m";
|
||||
data.path_regime(data.interference_path_length >= 10 & ...
|
||||
data.interference_path_length <= 100) = "10-100 m";
|
||||
data.path_regime(data.interference_path_length == 300) = "300 m";
|
||||
data.path_regime(data.interference_path_length == 1000) = "1000 m";
|
||||
data = data(data.path_regime ~= "", :);
|
||||
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_delay_regimes_v_bias:NoRows", ...
|
||||
"No rows remain after BER/study/block/regime/v_bias filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
if isempty(selectedPamLevels)
|
||||
selectedPamLevels = unique(data.pam_level(isfinite(data.pam_level))).';
|
||||
else
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
end
|
||||
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_delay_regimes_v_bias:NoSelectedRows", ...
|
||||
"No rows remain after selectedPamLevels filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
data.loop_id_group = round(data.loop_id);
|
||||
selectedLoopIds = unique(data.loop_id_group(isfinite(data.loop_id_group))).';
|
||||
|
||||
data.clean_keep = true(height(data), 1);
|
||||
groupId = findgroups(data.path_regime, data.pam_level, data.loop_id_group, data.sir);
|
||||
for curGroup = unique(groupId(isfinite(groupId))).'
|
||||
rowMask = groupId == curGroup;
|
||||
berValues = data.BER(rowMask);
|
||||
if nnz(rowMask) > 3
|
||||
data.clean_keep(rowMask) = ~isoutlier(berValues);
|
||||
end
|
||||
end
|
||||
cleanData = data(data.clean_keep, :);
|
||||
|
||||
groupVars = ["path_regime", "pam_level", "loop_id_group", "sir"];
|
||||
summaryTable = groupsummary(cleanData, groupVars, ...
|
||||
{"mean", "min", "max"}, "BER");
|
||||
sirExactTable = groupsummary(cleanData, groupVars, "mean", "sir_exact");
|
||||
biasTable = groupsummary(cleanData, groupVars, "median", "v_bias");
|
||||
loopTable = groupsummary(cleanData, groupVars, "median", "loop_id");
|
||||
pathLengthTable = groupsummary(cleanData, groupVars, "median", "interference_path_length");
|
||||
summaryTable = sortrows(summaryTable, groupVars);
|
||||
sirExactTable = sortrows(sirExactTable, groupVars);
|
||||
biasTable = sortrows(biasTable, groupVars);
|
||||
loopTable = sortrows(loopTable, groupVars);
|
||||
pathLengthTable = sortrows(pathLengthTable, groupVars);
|
||||
summaryTable.sir_exact = sirExactTable.mean_sir_exact;
|
||||
summaryTable.v_bias = biasTable.median_v_bias;
|
||||
summaryTable.loop_id = loopTable.median_loop_id;
|
||||
summaryTable.interference_path_length = pathLengthTable.median_interference_path_length;
|
||||
|
||||
fprintf("Cleaned to %d rows across %d regime/PAM/loop_id/SIR groups.\n", ...
|
||||
height(cleanData), height(summaryTable));
|
||||
disp(groupcounts(cleanData, ["path_regime", "pam_level", "loop_id_group"]));
|
||||
|
||||
|
||||
|
||||
%% 3) Plot one BER-over-SIR figure per delay/coherence regime
|
||||
|
||||
if exist("linspecer", "file")
|
||||
loopColors = linspecer(numel(selectedLoopIds));
|
||||
else
|
||||
loopColors = lines(numel(selectedLoopIds));
|
||||
end
|
||||
|
||||
for regimeIdx = 1:numel(regimeNames)
|
||||
regimeName = regimeNames(regimeIdx);
|
||||
regimeDataMask = cleanData.path_regime == regimeName;
|
||||
if ~any(regimeDataMask)
|
||||
fprintf("Skipping regime %s: no rows.\n", regimeName);
|
||||
continue
|
||||
end
|
||||
|
||||
figure(); clf;
|
||||
tiledlayout(numel(selectedPamLevels), 1, "TileSpacing", "compact");
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
pamLevel = selectedPamLevels(pamIdx);
|
||||
nexttile; hold on;
|
||||
|
||||
for loopIdx = 1:numel(selectedLoopIds)
|
||||
loopId = selectedLoopIds(loopIdx);
|
||||
loopColor = loopColors(loopIdx, :);
|
||||
marker = loopMarkers{mod(loopIdx - 1, numel(loopMarkers)) + 1};
|
||||
|
||||
rawMask = cleanData.path_regime == regimeName & ...
|
||||
cleanData.pam_level == pamLevel & ...
|
||||
cleanData.loop_id_group == loopId;
|
||||
curveMask = summaryTable.path_regime == regimeName & ...
|
||||
summaryTable.pam_level == pamLevel & ...
|
||||
summaryTable.loop_id_group == loopId;
|
||||
|
||||
if ~any(curveMask)
|
||||
continue
|
||||
end
|
||||
|
||||
rawRows = cleanData(rawMask, :);
|
||||
hScatter = scatter(rawRows.sir_exact, rawRows.BER, ...
|
||||
26, ...
|
||||
"Marker", ".", ...
|
||||
"MarkerEdgeColor", loopColor, ...
|
||||
"HandleVisibility", "off");
|
||||
addRawDataTips(hScatter, rawRows);
|
||||
|
||||
sirValues = summaryTable.sir_exact(curveMask).';
|
||||
meanBer = summaryTable.mean_BER(curveMask).';
|
||||
minBer = summaryTable.min_BER(curveMask).';
|
||||
maxBer = summaryTable.max_BER(curveMask).';
|
||||
curveLoopIds = summaryTable.loop_id(curveMask).';
|
||||
curvePathLengths = summaryTable.interference_path_length(curveMask).';
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
|
||||
|
||||
if useBoundedLines && exist("boundedline", "file") && any(valid)
|
||||
yLower = max(meanBer - minBer, 0);
|
||||
yUpper = max(maxBer - meanBer, 0);
|
||||
yBounds = [yLower(:), yUpper(:)];
|
||||
|
||||
[hl, hp] = boundedline(sirValues(valid).', meanBer(valid).', yBounds(valid, :), ...
|
||||
'alpha', 'transparency', 0.08, ...
|
||||
'cmap', loopColor, ...
|
||||
'nan', 'fill', ...
|
||||
'orientation', 'vert');
|
||||
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
|
||||
set(hp, "LineStyle", "none", "HandleVisibility", "off");
|
||||
end
|
||||
|
||||
plot(sirValues(valid), meanBer(valid), ...
|
||||
"LineStyle", "-", ...
|
||||
"Marker", marker, ...
|
||||
"MarkerSize", 5, ...
|
||||
"LineWidth", 1.2, ...
|
||||
"Color", loopColor, ...
|
||||
"MarkerFaceColor", "w", ...
|
||||
"MarkerEdgeColor", loopColor, ...
|
||||
"DisplayName", sprintf("loop %.0f, %.0f m", ...
|
||||
median(curveLoopIds, "omitnan"), ...
|
||||
median(curvePathLengths, "omitnan")));
|
||||
|
||||
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", loopColor, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
yline(2.2e-4, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(3.8e-3, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
yline(2e-2, "LineWidth", 1, "LineStyle", "--", "HandleVisibility", "off");
|
||||
|
||||
title(sprintf("PAM %.0f, %s, %s", ...
|
||||
pamLevel, regimeName, algorithmDisplayName(selectedAlgorithm)));
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
set(gca, "YScale", "log");
|
||||
ylim([9e-5, maxBerForPlot]);
|
||||
xlim([15, 45]);
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "best", "Interpreter", "tex");
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function values = stringColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
elseif ischar(values)
|
||||
values = string(values);
|
||||
end
|
||||
values = strip(string(values));
|
||||
end
|
||||
|
||||
function tf = defaultUseBoundedLines()
|
||||
tf = false;
|
||||
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 addRawDataTips(hScatter, rawRows)
|
||||
hScatter.DataTipTemplate.DataTipRows(1).Label = "SIR";
|
||||
hScatter.DataTipTemplate.DataTipRows(1).Format = "%.2f dB";
|
||||
hScatter.DataTipTemplate.DataTipRows(2).Label = "BER";
|
||||
hScatter.DataTipTemplate.DataTipRows(2).Format = "%.2e";
|
||||
hScatter.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("loop", rawRows.loop_id);
|
||||
hScatter.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("run_id", rawRows.run_id);
|
||||
hScatter.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("P_{rx}", rawRows.power_pd_in);
|
||||
hScatter.DataTipTemplate.DataTipRows(end).Format = "%.2f dBm";
|
||||
hScatter.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("delay", rawRows.interference_path_length);
|
||||
hScatter.DataTipTemplate.FontSize = 9;
|
||||
hScatter.DataTipTemplate.FontName = "arial";
|
||||
end
|
||||
Binary file not shown.
@@ -1,539 +0,0 @@
|
||||
% === DSP settings ===
|
||||
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.debug_plots = false;
|
||||
|
||||
dsp_options.database_type = "mysql";
|
||||
|
||||
dsp_options.dataBase = "labor";
|
||||
dsp_options.storage_path = "W:\labdata\ECOC Silas\ecoc_2025";
|
||||
|
||||
dsp_options.server = "192.168.178.192";
|
||||
dsp_options.port = 3306;
|
||||
dsp_options.user = "silas";
|
||||
dsp_options.password = "silas";
|
||||
|
||||
db = DBHandler("dataBase", [dsp_options.dataBase], ...
|
||||
"type", dsp_options.database_type, ...
|
||||
"server", dsp_options.server, ...
|
||||
"user", dsp_options.user, "password", dsp_options.password);
|
||||
|
||||
scriptDir = fileparts(mfilename("fullpath"));
|
||||
|
||||
whpam4_loaded = load(fullfile(scriptDir, "pam_4_results.mat"));
|
||||
whpam6_loaded = load(fullfile(scriptDir, "pam_6_results.mat"));
|
||||
whpam8_loaded = load(fullfile(scriptDir, "pam_8_results.mat"));
|
||||
|
||||
whpam4 = whpam4_loaded.obj;
|
||||
whpam6 = whpam6_loaded.obj;
|
||||
whpam8 = whpam8_loaded.obj;
|
||||
|
||||
whList = {whpam4, whpam6, whpam8};
|
||||
algorithmStorageNames = fieldnames(whpam4.sto);
|
||||
|
||||
%% Build a lossless run_id warehouse and cache run metadata
|
||||
|
||||
pamformats = [4, 6, 8];
|
||||
baudrates = [112e9, 96e9, 72e9];
|
||||
runMetaTable = queryRunMetadata(db, whList, pamformats, baudrates);
|
||||
|
||||
wh_run_id_combined = mergeDataStoragesByUnion(whList);
|
||||
wh_run_id_combined = addRunMetadataStorages(wh_run_id_combined, runMetaTable);
|
||||
|
||||
runIdSavePath = fullfile(scriptDir, "combined_by_run_id_results.mat");
|
||||
save(runIdSavePath, "wh_run_id_combined", "runMetaTable", "algorithmStorageNames", '-v7.3');
|
||||
|
||||
fprintf("Saved run_id-combined warehouse:\n %s\n", runIdSavePath);
|
||||
wh_run_id_combined.showInfo;
|
||||
|
||||
%% Build a grouped config warehouse for analysis views
|
||||
|
||||
configFields = ["pam_level", "symbolrate", "interference_path_length", "sir"];
|
||||
wh_config_combined = buildConfigWarehouse( ...
|
||||
wh_run_id_combined, runMetaTable, algorithmStorageNames, configFields);
|
||||
|
||||
configSavePath = fullfile(scriptDir, "combined_by_config_results.mat");
|
||||
save(configSavePath, "wh_config_combined", "runMetaTable", "algorithmStorageNames", '-v7.3');
|
||||
|
||||
fprintf("Saved config-grouped warehouse:\n %s\n", configSavePath);
|
||||
wh_config_combined.showInfo;
|
||||
|
||||
%% BER over SIR at fixed path length
|
||||
|
||||
plot_options = struct();
|
||||
plot_options.path_length = 1000;
|
||||
plot_options.use_boundedlines = true;
|
||||
plot_options.boundedline_alpha = 0.10;
|
||||
plot_options.polyfit_order_max = 3;
|
||||
plot_options.fec_ber_threshold = 2e-2;
|
||||
plot_options.figure_base = 7100;
|
||||
plot_options.xlim = [15 35];
|
||||
|
||||
plotBerOverSirForPath(wh_config_combined, algorithmStorageNames, plot_options);
|
||||
|
||||
%% Local functions
|
||||
|
||||
function runMetaTable = queryRunMetadata(db, whList, pamformats, baudrates)
|
||||
metaTables = {};
|
||||
metadataFields = db.getTableFieldNames('Runs');
|
||||
|
||||
for whIdx = 1:numel(whList)
|
||||
curWh = whList{whIdx};
|
||||
curRunIds = curWh.parameter.run_id.values(:);
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'run_id', 'GREATER_EQUAL', 3153);
|
||||
fp.where('Runs', 'symbolrate', 'EQUALS', baudrates(whIdx));
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', pamformats(whIdx));
|
||||
|
||||
[dataTable, ~] = db.queryDB(fp, metadataFields);
|
||||
dataTable.run_id = numericColumn(dataTable.run_id);
|
||||
dataTable = dataTable(ismember(dataTable.run_id, curRunIds), :);
|
||||
|
||||
missingRunIds = setdiff(curRunIds, dataTable.run_id);
|
||||
if ~isempty(missingRunIds)
|
||||
warning("recombine_warehouses:MissingMetadata", ...
|
||||
"%d run_id(s) from PAM %.0f warehouse were not found in the metadata query.", ...
|
||||
numel(missingRunIds), pamformats(whIdx));
|
||||
end
|
||||
|
||||
metaTables{end+1} = dataTable; %#ok<AGROW>
|
||||
end
|
||||
|
||||
runMetaTable = vertcat(metaTables{:});
|
||||
[~, uniqueIdx] = unique(numericColumn(runMetaTable.run_id), "stable");
|
||||
runMetaTable = runMetaTable(uniqueIdx, :);
|
||||
|
||||
numericFields = ["run_id", "pam_level", "symbolrate", "interference_path_length", "sir"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(runMetaTable.Properties.VariableNames))
|
||||
runMetaTable.(char(fieldName)) = numericColumn(runMetaTable.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
[~, sortIdx] = sort(runMetaTable.run_id);
|
||||
runMetaTable = runMetaTable(sortIdx, :);
|
||||
end
|
||||
|
||||
function x = numericColumn(x)
|
||||
if isnumeric(x)
|
||||
x = double(x);
|
||||
elseif iscell(x)
|
||||
x = str2double(string(x));
|
||||
elseif isstring(x) || ischar(x) || iscategorical(x)
|
||||
x = str2double(string(x));
|
||||
else
|
||||
x = double(x);
|
||||
end
|
||||
x = x(:);
|
||||
end
|
||||
|
||||
function whMerged = mergeDataStoragesByUnion(whList)
|
||||
templateWh = whList{1};
|
||||
paramNames = cellstr(templateWh.fn);
|
||||
|
||||
for whIdx = 2:numel(whList)
|
||||
if ~isequal(sort(cellstr(whList{whIdx}.fn)), sort(paramNames))
|
||||
error("recombine_warehouses:ParameterMismatch", ...
|
||||
"All input warehouses must use the same parameter names.");
|
||||
end
|
||||
end
|
||||
|
||||
mergedParams = struct();
|
||||
for paramIdx = 1:numel(paramNames)
|
||||
paramName = paramNames{paramIdx};
|
||||
values = [];
|
||||
|
||||
for whIdx = 1:numel(whList)
|
||||
newValues = whList{whIdx}.parameter.(paramName).values(:).';
|
||||
values = [values, newValues]; %#ok<AGROW>
|
||||
end
|
||||
|
||||
values = unique(values, "stable");
|
||||
if isnumeric(values)
|
||||
values = sort(values);
|
||||
end
|
||||
mergedParams.(paramName) = values;
|
||||
end
|
||||
|
||||
whMerged = DataStorage(mergedParams);
|
||||
|
||||
for whIdx = 1:numel(whList)
|
||||
curWh = whList{whIdx};
|
||||
curStorageNames = fieldnames(curWh.sto);
|
||||
|
||||
for storageIdx = 1:numel(curStorageNames)
|
||||
storageName = curStorageNames{storageIdx};
|
||||
if ~isfield(whMerged.sto, storageName)
|
||||
whMerged.addStorage(storageName);
|
||||
end
|
||||
|
||||
for linIdx = 1:numel(curWh.sto.(storageName))
|
||||
storedValue = curWh.sto.(storageName){linIdx};
|
||||
if isempty(storedValue)
|
||||
continue
|
||||
end
|
||||
|
||||
targetLinIdx = mapLinearIndex(curWh, whMerged, linIdx);
|
||||
existingValue = whMerged.sto.(storageName){targetLinIdx};
|
||||
whMerged.sto.(storageName){targetLinIdx} = mergeStoredValue(existingValue, storedValue);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function wh = addRunMetadataStorages(wh, runMetaTable)
|
||||
metadataStorageNames = ["meta_pam_level", "meta_symbolrate", ...
|
||||
"meta_interference_path_length", "meta_sir"];
|
||||
metadataTableFields = ["pam_level", "symbolrate", "interference_path_length", "sir"];
|
||||
|
||||
for fieldIdx = 1:numel(metadataStorageNames)
|
||||
if ~isfield(wh.sto, metadataStorageNames(fieldIdx))
|
||||
wh.addStorage(char(metadataStorageNames(fieldIdx)));
|
||||
end
|
||||
end
|
||||
|
||||
for linIdx = 1:wh.getLastLinIndice()
|
||||
physStruct = linearIndexToStruct(wh, linIdx);
|
||||
if ~isfield(physStruct, "run_id")
|
||||
continue
|
||||
end
|
||||
|
||||
metaIdx = find(runMetaTable.run_id == physStruct.run_id, 1);
|
||||
if isempty(metaIdx)
|
||||
continue
|
||||
end
|
||||
|
||||
for fieldIdx = 1:numel(metadataStorageNames)
|
||||
wh.sto.(char(metadataStorageNames(fieldIdx))){linIdx} = ...
|
||||
runMetaTable.(char(metadataTableFields(fieldIdx)))(metaIdx);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function whConfig = buildConfigWarehouse(whRun, runMetaTable, algorithmStorageNames, configFields)
|
||||
runParamNames = cellstr(whRun.fn);
|
||||
extraParamNames = setdiff(string(runParamNames), "run_id", "stable");
|
||||
|
||||
configParams = struct();
|
||||
for fieldIdx = 1:numel(configFields)
|
||||
fieldName = configFields(fieldIdx);
|
||||
values = unique(runMetaTable.(char(fieldName))(:).', "stable");
|
||||
if isnumeric(values)
|
||||
values = sort(values);
|
||||
end
|
||||
configParams.(char(fieldName)) = values;
|
||||
end
|
||||
|
||||
for paramIdx = 1:numel(extraParamNames)
|
||||
paramName = extraParamNames(paramIdx);
|
||||
configParams.(char(paramName)) = whRun.parameter.(char(paramName)).values;
|
||||
end
|
||||
|
||||
whConfig = DataStorage(configParams);
|
||||
for storageIdx = 1:numel(algorithmStorageNames)
|
||||
whConfig.addStorage(algorithmStorageNames{storageIdx});
|
||||
end
|
||||
whConfig.addStorage("run_id");
|
||||
|
||||
for storageIdx = 1:numel(algorithmStorageNames)
|
||||
storageName = algorithmStorageNames{storageIdx};
|
||||
|
||||
for linIdx = 1:numel(whRun.sto.(storageName))
|
||||
storedValue = whRun.sto.(storageName){linIdx};
|
||||
if isempty(storedValue)
|
||||
continue
|
||||
end
|
||||
|
||||
sourcePhys = linearIndexToStruct(whRun, linIdx);
|
||||
metaIdx = find(runMetaTable.run_id == sourcePhys.run_id, 1);
|
||||
if isempty(metaIdx)
|
||||
continue
|
||||
end
|
||||
|
||||
targetArgs = configTargetArgs(whConfig, sourcePhys, runMetaTable, metaIdx, configFields);
|
||||
targetLinIdx = whConfig.getIndicesByPhys(targetArgs);
|
||||
|
||||
existingValue = whConfig.sto.(storageName){targetLinIdx};
|
||||
whConfig.sto.(storageName){targetLinIdx} = mergeStoredValue(existingValue, storedValue);
|
||||
end
|
||||
end
|
||||
|
||||
for linIdx = 1:whRun.getLastLinIndice()
|
||||
sourcePhys = linearIndexToStruct(whRun, linIdx);
|
||||
metaIdx = find(runMetaTable.run_id == sourcePhys.run_id, 1);
|
||||
if isempty(metaIdx)
|
||||
continue
|
||||
end
|
||||
|
||||
targetArgs = configTargetArgs(whConfig, sourcePhys, runMetaTable, metaIdx, configFields);
|
||||
targetLinIdx = whConfig.getIndicesByPhys(targetArgs);
|
||||
whConfig.sto.run_id{targetLinIdx} = mergeUniqueNumeric( ...
|
||||
whConfig.sto.run_id{targetLinIdx}, sourcePhys.run_id);
|
||||
end
|
||||
end
|
||||
|
||||
function targetArgs = configTargetArgs(whConfig, sourcePhys, runMetaTable, metaIdx, configFields)
|
||||
targetArgs = cell(1, numel(whConfig.fn));
|
||||
|
||||
for paramIdx = 1:numel(whConfig.fn)
|
||||
paramName = string(whConfig.fn(paramIdx));
|
||||
if any(configFields == paramName)
|
||||
targetArgs{paramIdx} = runMetaTable.(char(paramName))(metaIdx);
|
||||
else
|
||||
targetArgs{paramIdx} = sourcePhys.(char(paramName));
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function targetLinIdx = mapLinearIndex(sourceWh, targetWh, sourceLinIdx)
|
||||
sourcePhys = linearIndexToStruct(sourceWh, sourceLinIdx);
|
||||
targetArgs = cell(1, numel(targetWh.fn));
|
||||
|
||||
for paramIdx = 1:numel(targetWh.fn)
|
||||
paramName = char(targetWh.fn(paramIdx));
|
||||
targetArgs{paramIdx} = sourcePhys.(paramName);
|
||||
end
|
||||
|
||||
targetLinIdx = targetWh.getIndicesByPhys(targetArgs);
|
||||
end
|
||||
|
||||
function physStruct = linearIndexToStruct(wh, linIdx)
|
||||
[physValues, physNames] = wh.getPhysIndicesByLinIndex(linIdx);
|
||||
physStruct = struct();
|
||||
|
||||
for paramIdx = 1:numel(physNames)
|
||||
physStruct.(char(physNames{paramIdx})) = physValues{paramIdx};
|
||||
end
|
||||
end
|
||||
|
||||
function out = mergeStoredValue(existingValue, newValue)
|
||||
if isempty(existingValue)
|
||||
out = newValue;
|
||||
return
|
||||
end
|
||||
|
||||
existingPackages = asPackageCell(existingValue);
|
||||
newPackages = asPackageCell(newValue);
|
||||
out = [existingPackages(:); newPackages(:)].';
|
||||
end
|
||||
|
||||
function packages = asPackageCell(value)
|
||||
if isempty(value)
|
||||
packages = {};
|
||||
elseif iscell(value)
|
||||
packages = value(:).';
|
||||
else
|
||||
packages = {value};
|
||||
end
|
||||
end
|
||||
|
||||
function out = mergeUniqueNumeric(existingValue, newValue)
|
||||
if isempty(existingValue)
|
||||
out = newValue;
|
||||
else
|
||||
out = unique([existingValue(:); newValue(:)].', "stable");
|
||||
end
|
||||
end
|
||||
|
||||
function plotBerOverSirForPath(whConfig, algorithmStorageNames, plotOptions)
|
||||
paramNames = string(whConfig.fn);
|
||||
|
||||
if ~any(paramNames == "pam_level") || ~any(paramNames == "interference_path_length") || ~any(paramNames == "sir")
|
||||
error("recombine_warehouses:MissingPlotParameters", ...
|
||||
"The config warehouse must contain pam_level, interference_path_length, and sir parameters.");
|
||||
end
|
||||
|
||||
pamLevels = whConfig.parameter.pam_level.values;
|
||||
hasBlockUpdate = any(paramNames == "block_update");
|
||||
if hasBlockUpdate
|
||||
blockUpdates = whConfig.parameter.block_update.values;
|
||||
else
|
||||
blockUpdates = NaN;
|
||||
end
|
||||
|
||||
if exist("linspecer", "file")
|
||||
curveColors = linspecer(max(numel(algorithmStorageNames), 1));
|
||||
else
|
||||
curveColors = lines(max(numel(algorithmStorageNames), 1));
|
||||
end
|
||||
|
||||
figureIdx = plotOptions.figure_base;
|
||||
for blockIdx = 1:numel(blockUpdates)
|
||||
for pamIdx = 1:numel(pamLevels)
|
||||
pamLevel = pamLevels(pamIdx);
|
||||
figureIdx = figureIdx + 1;
|
||||
figure(figureIdx); clf; hold on
|
||||
|
||||
for storageIdx = 1:numel(algorithmStorageNames)
|
||||
storageName = algorithmStorageNames{storageIdx};
|
||||
curveColor = curveColors(storageIdx, :);
|
||||
|
||||
filter = struct();
|
||||
filter.pam_level = pamLevel;
|
||||
filter.interference_path_length = plotOptions.path_length;
|
||||
if hasBlockUpdate
|
||||
filter.block_update = blockUpdates(blockIdx);
|
||||
end
|
||||
|
||||
[sirValues, berGroups] = collectBerBySir(whConfig, storageName, filter);
|
||||
if isempty(sirValues)
|
||||
continue
|
||||
end
|
||||
|
||||
[sirValues, sortIdx] = sort(sirValues);
|
||||
berGroups = berGroups(sortIdx);
|
||||
|
||||
berAvg = nan(1, numel(sirValues));
|
||||
berMin = nan(1, numel(sirValues));
|
||||
berMax = nan(1, numel(sirValues));
|
||||
|
||||
for sirIdx = 1:numel(sirValues)
|
||||
rawBer = berGroups{sirIdx};
|
||||
rawBer = rawBer(isfinite(rawBer));
|
||||
if isempty(rawBer)
|
||||
continue
|
||||
end
|
||||
|
||||
scatter(repmat(sirValues(sirIdx), size(rawBer)), rawBer, ...
|
||||
12, ...
|
||||
"Marker", ".", ...
|
||||
"MarkerEdgeColor", curveColor, ...
|
||||
"MarkerFaceColor", curveColor, ...
|
||||
"HandleVisibility", "off");
|
||||
|
||||
berAvg(sirIdx) = mean(rawBer, "omitnan");
|
||||
berMin(sirIdx) = min(rawBer);
|
||||
berMax(sirIdx) = max(rawBer);
|
||||
end
|
||||
|
||||
validAvg = isfinite(sirValues) & isfinite(berAvg);
|
||||
if ~any(validAvg)
|
||||
continue
|
||||
end
|
||||
|
||||
if plotOptions.use_boundedlines && exist("boundedline", "file")
|
||||
yLower = max(berAvg - berMin, 0);
|
||||
yUpper = max(berMax - berAvg, 0);
|
||||
yBounds = [yLower(:), yUpper(:)];
|
||||
|
||||
[hl, hp] = boundedline(sirValues(:), berAvg(:), yBounds, ...
|
||||
"alpha", "transparency", plotOptions.boundedline_alpha, ...
|
||||
"cmap", curveColor, ...
|
||||
"nan", "fill", ...
|
||||
"orientation", "vert");
|
||||
set(hl, "LineStyle", "none", "Marker", "none", "HandleVisibility", "off");
|
||||
set(hp, "HandleVisibility", "off", "LineStyle", "none");
|
||||
end
|
||||
|
||||
scatter(sirValues(validAvg), berAvg(validAvg), ...
|
||||
54, ...
|
||||
"Marker", "o", ...
|
||||
"MarkerEdgeColor", curveColor, ...
|
||||
"MarkerFaceColor", "none", ...
|
||||
"LineWidth", 1.2, ...
|
||||
"DisplayName", storageName);
|
||||
|
||||
fitMask = validAvg & berAvg > 0;
|
||||
if nnz(fitMask) >= 2
|
||||
fitOrder = min(plotOptions.polyfit_order_max, nnz(fitMask) - 1);
|
||||
fitCoeff = polyfit(sirValues(fitMask), log10(berAvg(fitMask)), fitOrder);
|
||||
xFit = linspace(min(sirValues(fitMask)), max(sirValues(fitMask)), 300);
|
||||
yFit = 10 .^ polyval(fitCoeff, xFit);
|
||||
|
||||
plot(xFit, yFit, ...
|
||||
"LineWidth", 1.2, ...
|
||||
"LineStyle", "--", ...
|
||||
"Marker", "none", ...
|
||||
"Color", curveColor, ...
|
||||
"HandleVisibility", "off");
|
||||
end
|
||||
end
|
||||
|
||||
if hasBlockUpdate
|
||||
title(sprintf("BER over SIR, PAM %.0f, path %.0f m, block update = %g", ...
|
||||
pamLevel, plotOptions.path_length, blockUpdates(blockIdx)));
|
||||
else
|
||||
title(sprintf("BER over SIR, PAM %.0f, path %.0f m", ...
|
||||
pamLevel, plotOptions.path_length));
|
||||
end
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
xlim(plotOptions.xlim);
|
||||
grid on
|
||||
box on
|
||||
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
|
||||
else
|
||||
set(gca, "YScale", "log");
|
||||
end
|
||||
legend("Location", "best", "Interpreter", "none");
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function [sirValues, berGroups] = collectBerBySir(whConfig, storageName, filter)
|
||||
sirValues = [];
|
||||
berGroups = {};
|
||||
|
||||
for linIdx = 1:numel(whConfig.sto.(storageName))
|
||||
storedValue = whConfig.sto.(storageName){linIdx};
|
||||
if isempty(storedValue)
|
||||
continue
|
||||
end
|
||||
|
||||
physStruct = linearIndexToStruct(whConfig, linIdx);
|
||||
if ~matchesFilter(physStruct, filter)
|
||||
continue
|
||||
end
|
||||
|
||||
berValues = extractBerValues(storedValue);
|
||||
if isempty(berValues)
|
||||
continue
|
||||
end
|
||||
|
||||
sirValue = physStruct.sir;
|
||||
sirIdx = find(sirValues == sirValue, 1);
|
||||
if isempty(sirIdx)
|
||||
sirValues(end+1) = sirValue; %#ok<AGROW>
|
||||
berGroups{end+1} = berValues; %#ok<AGROW>
|
||||
else
|
||||
berGroups{sirIdx} = [berGroups{sirIdx}, berValues]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function tf = matchesFilter(physStruct, filter)
|
||||
tf = true;
|
||||
filterFields = fieldnames(filter);
|
||||
|
||||
for fieldIdx = 1:numel(filterFields)
|
||||
fieldName = filterFields{fieldIdx};
|
||||
if ~isfield(physStruct, fieldName) || physStruct.(fieldName) ~= filter.(fieldName)
|
||||
tf = false;
|
||||
return
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function berValues = extractBerValues(value)
|
||||
berValues = [];
|
||||
|
||||
if isstruct(value)
|
||||
if isfield(value, "metrics") && isfield(value.metrics, "BER")
|
||||
berValues(end+1) = value.metrics.BER;
|
||||
end
|
||||
elseif iscell(value)
|
||||
for valueIdx = 1:numel(value)
|
||||
berValues = [berValues, extractBerValues(value{valueIdx})]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
Binary file not shown.
@@ -3,13 +3,13 @@ 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.start_occurence = 5;
|
||||
dsp_options.max_occurences = 1;
|
||||
dsp_options.debug_plots = true;
|
||||
dsp_options.debug_plots = false;
|
||||
|
||||
write_mpi_reduction_db = 0;
|
||||
stream_mpi_reduction_db = 0;
|
||||
mpi_reduction_study_name = "block_update_sweep";
|
||||
stream_mpi_reduction_db = write_mpi_reduction_db;
|
||||
mpi_reduction_study_name = "pam6_8_sweep";
|
||||
mpi_reduction_writer_path = fullfile(fileparts(mfilename('fullpath')),"db");
|
||||
addpath(mpi_reduction_writer_path);
|
||||
|
||||
@@ -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', 20);
|
||||
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);
|
||||
@@ -62,6 +61,12 @@ for i = 1
|
||||
[~, sortIdx] = sort(dataTable.sir, 'descend');
|
||||
dataTable = dataTable(sortIdx, :);
|
||||
|
||||
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.symbolrate, desired_sir);
|
||||
dataTable = dataTable(isDesired, :);
|
||||
% dataTable = dataTable(1,:);
|
||||
|
||||
run_ids = dataTable.run_id;
|
||||
@@ -69,7 +74,8 @@ for i = 1
|
||||
%% === Warehouse setup ===
|
||||
dsp_options.userParameters = struct();
|
||||
dsp_options.userParameters.block_update = 1;%[1,2,4,8,16,32,64,112,224,448,448*2,1024,2048,4096,8192,16384];%%logspace(-3.8,-1,22);%[linspace(2,4096,22)];
|
||||
% dsp_options.userParameters.block_update = linspace(1,224,22);
|
||||
% dsp_options.userParameters.hpf = [2e6:0.5e6:10e6];
|
||||
% dsp_options.userParameters.bufferlen = [112,224,448,448*2,1024,2048,4096,8192,16384];
|
||||
wh = DataStorage(dsp_options.userParameters);
|
||||
|
||||
%%
|
||||
@@ -85,7 +91,7 @@ for i = 1
|
||||
%% === Run ===
|
||||
% submitJobs returns the raw per-job results and the filled Warehouse. For a
|
||||
% single run_id and remove_dc = [0, 1], results is a 2-by-1 cell array.
|
||||
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.parallel, ...
|
||||
[results, wh] = submitJobs(run_ids, dsp_options, processingMode.serial, ...
|
||||
"wh", wh, ...
|
||||
"waitbar", true);
|
||||
|
||||
@@ -108,9 +114,10 @@ end
|
||||
storageNames = fieldnames(wh.sto);
|
||||
|
||||
x_base = dataTable.sir(:).';
|
||||
% x_base = dsp_options.userParameters.block_update;
|
||||
|
||||
figure(2026); clf; hold on
|
||||
x_base = dsp_options.userParameters.block_update;
|
||||
x_base = dsp_options.userParameters.bufferlen;
|
||||
% x_base = dsp_options.userParameters.hpf;
|
||||
figure(2029); hold on
|
||||
for storage_idx = 1:numel(storageNames)
|
||||
storageName = storageNames{storage_idx};
|
||||
result = wh.sto.(storageName);
|
||||
|
||||
@@ -0,0 +1,564 @@
|
||||
%% MPI reduction parallelization analysis from MpiReductionResults
|
||||
% A) Helper BER-over-SIR plots for first and last block_update.
|
||||
% B) Required SIR at FEC threshold over block_update.
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% 1) Gather data
|
||||
|
||||
studyName = "block_update_sweep";
|
||||
pathLengthToPlot = 1000;
|
||||
selectedPamLevels = 4; % set [] to use all PAM levels in the query result
|
||||
selectedAlgorithms = []; % set [] to use all algorithms in the query result
|
||||
|
||||
useBoundedLines = true;
|
||||
usePolyfit = true;
|
||||
polyfitOrderMax = 4;
|
||||
boundaryPolyfitOrderMax = 4;
|
||||
fecBerThreshold = 3.8e-3;
|
||||
maxBerForPlot = 0.1;
|
||||
scatterKeepFraction = 0.2;
|
||||
boundMode = "fitStd"; % "fitStd" or "directStd"
|
||||
algorithmMarkers = {'o','square','diamond','^','v','>','<','pentagram'};
|
||||
|
||||
db = DBHandler( ...
|
||||
"dataBase", "labor", ...
|
||||
"type", "mysql", ...
|
||||
"server", "192.168.178.192", ...
|
||||
"user", "silas", ...
|
||||
"password", "silas");
|
||||
db.refresh();
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'interference_path_length', 'EQUALS', pathLengthToPlot);
|
||||
fp.where('Runs', 'fiber_length', 'EQUALS', 0);
|
||||
fp.where('Runs', 'db_mode', 'EQUALS', '"no_db"');
|
||||
fp.where('MpiReductionResults', 'study_name', 'EQUALS', char(studyName));
|
||||
if isscalar(selectedPamLevels) && ~isempty(selectedPamLevels)
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', selectedPamLevels);
|
||||
end
|
||||
|
||||
selectedFields = { ...
|
||||
'Runs.run_id', ...
|
||||
'Runs.sir', ...
|
||||
'Runs.pam_level', ...
|
||||
'Runs.symbolrate', ...
|
||||
'Runs.interference_path_length', ...
|
||||
'Runs.power_mpi_interference', ...
|
||||
'MpiReductionResults.occurrence_idx', ...
|
||||
'MpiReductionResults.storage_name', ...
|
||||
'MpiReductionResults.algorithm', ...
|
||||
'MpiReductionResults.algorithm_variant', ...
|
||||
'MpiReductionResults.eq_class', ...
|
||||
'MpiReductionResults.block_update', ...
|
||||
'MpiReductionResults.BER', ...
|
||||
'MpiReductionResults.BER_precoded', ...
|
||||
'MpiReductionResults.SNR', ...
|
||||
'MpiReductionResults.GMI', ...
|
||||
'MpiReductionResults.AIR'};
|
||||
selectedFields = selectedFields(:);
|
||||
|
||||
[rawData, query] = db.queryDB(fp, selectedFields);
|
||||
disp(query);
|
||||
fprintf("Fetched %d MPI reduction rows.\n", height(rawData));
|
||||
|
||||
%% 2) Clean data and calculate required SIR
|
||||
|
||||
data = rawData;
|
||||
numericFields = ["run_id", "sir", "pam_level", "symbolrate", ...
|
||||
"interference_path_length", "occurrence_idx", "block_update", ...
|
||||
"power_mpi_interference", "BER", "BER_precoded", "SNR", "GMI", "AIR"];
|
||||
for fieldIdx = 1:numel(numericFields)
|
||||
fieldName = numericFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = numericColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
stringFields = ["storage_name", "algorithm", "algorithm_variant", "eq_class"];
|
||||
for fieldIdx = 1:numel(stringFields)
|
||||
fieldName = stringFields(fieldIdx);
|
||||
if ismember(fieldName, string(data.Properties.VariableNames))
|
||||
data.(char(fieldName)) = stringColumn(data.(char(fieldName)));
|
||||
end
|
||||
end
|
||||
|
||||
data = data(isfinite(data.BER) & data.BER > 0 & data.BER < maxBerForPlot, :);
|
||||
data.sir_exact = -7 - data.power_mpi_interference;
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_parallelization_vs_sir:NoRows", ...
|
||||
"No rows remain after initial BER/path/study filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
if isempty(selectedPamLevels)
|
||||
selectedPamLevels = unique(data.pam_level(isfinite(data.pam_level))).';
|
||||
else
|
||||
data = data(ismember(data.pam_level, selectedPamLevels), :);
|
||||
end
|
||||
|
||||
if isempty(selectedAlgorithms)
|
||||
selectedAlgorithms = unique(data.algorithm, "stable").';
|
||||
else
|
||||
selectedAlgorithms = string(selectedAlgorithms);
|
||||
data = data(ismember(data.algorithm, selectedAlgorithms), :);
|
||||
end
|
||||
|
||||
if isempty(data)
|
||||
warning("PLOT_mpi_reduction_db_parallelization_vs_sir:NoSelectedRows", ...
|
||||
"No rows remain after selectedPamLevels/selectedAlgorithms filtering.");
|
||||
return
|
||||
end
|
||||
|
||||
data.clean_keep = true(height(data), 1);
|
||||
groupId = findgroups(data.pam_level, data.algorithm, data.block_update, data.sir);
|
||||
for curGroup = unique(groupId(isfinite(groupId))).'
|
||||
rowMask = groupId == curGroup;
|
||||
berValues = data.BER(rowMask);
|
||||
if nnz(rowMask) > 3
|
||||
data.clean_keep(rowMask) = ~isoutlier(berValues);
|
||||
end
|
||||
end
|
||||
cleanData = data(data.clean_keep, :);
|
||||
|
||||
groupVars = ["pam_level", "algorithm", "block_update", "sir"];
|
||||
summaryTable = groupsummary(cleanData, groupVars, ...
|
||||
{"mean", "min", "max"}, "BER");
|
||||
sirExactTable = groupsummary(cleanData, groupVars, "median", "sir_exact");
|
||||
summaryTable = sortrows(summaryTable, groupVars);
|
||||
sirExactTable = sortrows(sirExactTable, groupVars);
|
||||
summaryTable.sir_exact = sirExactTable.median_sir_exact;
|
||||
summaryTable = addBerStdBounds(summaryTable, cleanData, groupVars);
|
||||
|
||||
blockUpdates = unique(cleanData.block_update(isfinite(cleanData.block_update))).';
|
||||
blockUpdates = sort(blockUpdates);
|
||||
helperBlockUpdates = unique(blockUpdates([1, end]), "stable");
|
||||
|
||||
requiredSirRows = table();
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
pamLevel = selectedPamLevels(pamIdx);
|
||||
for algIdx = 1:numel(selectedAlgorithms)
|
||||
algorithmName = selectedAlgorithms(algIdx);
|
||||
for blockIdx = 1:numel(blockUpdates)
|
||||
blockUpdate = blockUpdates(blockIdx);
|
||||
curveMask = summaryTable.pam_level == pamLevel & ...
|
||||
summaryTable.algorithm == algorithmName & ...
|
||||
summaryTable.block_update == blockUpdate;
|
||||
|
||||
sirValues = summaryTable.sir_exact(curveMask).';
|
||||
meanBer = summaryTable.mean_BER(curveMask).';
|
||||
[~, ~, requiredSir, fitOrder] = fitBerAtFec( ...
|
||||
sirValues, meanBer, polyfitOrderMax, fecBerThreshold);
|
||||
|
||||
newRow = table(pamLevel, algorithmName, blockUpdate, ...
|
||||
requiredSir, fitOrder, nnz(isfinite(sirValues) & isfinite(meanBer)), ...
|
||||
'VariableNames', ["pam_level", "algorithm", "block_update", ...
|
||||
"required_sir", "fit_order", "n_points"]);
|
||||
requiredSirRows = [requiredSirRows; newRow]; %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
fprintf("Cleaned to %d rows across %d PAM/algorithm/block/SIR groups.\n", ...
|
||||
height(cleanData), height(summaryTable));
|
||||
disp(groupcounts(cleanData, ["pam_level", "algorithm", "block_update"]));
|
||||
disp(requiredSirRows);
|
||||
|
||||
%% 3A) Helper BER-over-SIR plots for first and last block updates
|
||||
|
||||
% tiledlayout(numel(selectedPamLevels), numel(helperBlockUpdates), ...
|
||||
% "TileSpacing", "compact");
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
pamLevel = selectedPamLevels(pamIdx);
|
||||
for helperIdx = 1:numel(helperBlockUpdates)
|
||||
figure(); clf;
|
||||
blockUpdate = helperBlockUpdates(helperIdx);
|
||||
nexttile; hold on;
|
||||
|
||||
for algIdx = 1:numel(selectedAlgorithms)
|
||||
algorithmName = selectedAlgorithms(algIdx);
|
||||
algColor = algorithmColor(algorithmName);
|
||||
marker = algorithmMarker(algorithmName, algorithmMarkers);
|
||||
displayName = algorithmDisplayName(algorithmName);
|
||||
|
||||
rawMask = cleanData.pam_level == pamLevel & ...
|
||||
cleanData.algorithm == algorithmName & ...
|
||||
cleanData.block_update == blockUpdate;
|
||||
curveMask = summaryTable.pam_level == pamLevel & ...
|
||||
summaryTable.algorithm == algorithmName & ...
|
||||
summaryTable.block_update == blockUpdate;
|
||||
|
||||
if ~any(curveMask)
|
||||
continue
|
||||
end
|
||||
|
||||
scatterKeepFraction = 1;
|
||||
scatterRows = downsampleRows(cleanData(rawMask, :), scatterKeepFraction);
|
||||
scatter(scatterRows.sir_exact, 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("PAM %.0f, block update %.0f", pamLevel, blockUpdate));
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
set(gca, "YScale", "log");
|
||||
ylim([9e-5, 0.05]);
|
||||
xlim([15, 35]);
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "northeast", "Interpreter", "none");
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", true, "setcolors", false, "setmarkers", false);
|
||||
end
|
||||
% mat2tikz_improved(sprintf("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/04_Experimental_Evaluation/tikz/mpi/ber_vs_sir_blockupdate_v2_%d.tikz",blockUpdate));
|
||||
end
|
||||
end
|
||||
|
||||
%% 3B) Required SIR over block update
|
||||
|
||||
figure(); clf;
|
||||
tiledlayout(numel(selectedPamLevels), 1, "TileSpacing", "compact");
|
||||
|
||||
for pamIdx = 1:numel(selectedPamLevels)
|
||||
pamLevel = selectedPamLevels(pamIdx);
|
||||
nexttile; hold on;
|
||||
|
||||
for algIdx = 1:numel(selectedAlgorithms)
|
||||
algorithmName = selectedAlgorithms(algIdx);
|
||||
algColor = algorithmColor(algorithmName);
|
||||
marker = algorithmMarker(algorithmName, algorithmMarkers);
|
||||
displayName = algorithmDisplayName(algorithmName);
|
||||
rowMask = requiredSirRows.pam_level == pamLevel & ...
|
||||
requiredSirRows.algorithm == algorithmName;
|
||||
|
||||
x = requiredSirRows.block_update(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(blockUpdates);
|
||||
xticklabels(string(blockUpdates));
|
||||
xlim([x(1) x(end)]);
|
||||
grid on;
|
||||
box on;
|
||||
xlabel("Parallelization / block update");
|
||||
ylabel(sprintf("Required SIR at BER = %.1e (dB)", fecBerThreshold));
|
||||
title(sprintf("PAM %.0f, path %.0f m", pamLevel, pathLengthToPlot));
|
||||
legend("Location","southeast", "Interpreter", "none");
|
||||
if exist("beautifyBERplot", "file")
|
||||
beautifyBERplot("logscale", false, "setcolors", false, "setmarkers", false);
|
||||
end
|
||||
end
|
||||
|
||||
%% Local helpers
|
||||
|
||||
function values = numericColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
end
|
||||
if isstring(values) || ischar(values)
|
||||
values = str2double(values);
|
||||
end
|
||||
values = double(values);
|
||||
end
|
||||
|
||||
function values = stringColumn(values)
|
||||
if iscell(values)
|
||||
values = string(values);
|
||||
elseif ischar(values)
|
||||
values = string(values);
|
||||
end
|
||||
values = strip(string(values));
|
||||
end
|
||||
|
||||
function rows = downsampleRows(rows, keepFraction)
|
||||
if keepFraction >= 1 || height(rows) <= 1
|
||||
return
|
||||
end
|
||||
|
||||
keepEvery = max(1, round(1 / keepFraction));
|
||||
keepIdx = 1:keepEvery:height(rows);
|
||||
rows = rows(keepIdx, :);
|
||||
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"
|
||||
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 = algorithmMarker(algorithmName, algorithmMarkers)
|
||||
algorithmOrder = ["plain_ffe", "a2_tracked_levels", "a2_residual", ...
|
||||
"a1_moving_average", "dc_tracking"];
|
||||
markerIdx = find(algorithmOrder == string(algorithmName), 1);
|
||||
if isempty(markerIdx)
|
||||
markerIdx = 1;
|
||||
end
|
||||
marker = algorithmMarkers{mod(markerIdx - 1, numel(algorithmMarkers)) + 1};
|
||||
end
|
||||
|
||||
function [xFit, yFit, requiredSir, fitOrder] = fitBerAtFec( ...
|
||||
sirValues, meanBer, polyfitOrderMax, fecBerThreshold)
|
||||
|
||||
xFit = NaN;
|
||||
yFit = NaN;
|
||||
requiredSir = NaN;
|
||||
fitOrder = NaN;
|
||||
|
||||
valid = isfinite(sirValues) & isfinite(meanBer) & meanBer > 0;
|
||||
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(min(sirValues), max(sirValues), 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
|
||||
end
|
||||
Binary file not shown.
@@ -1,389 +0,0 @@
|
||||
%% Analyze
|
||||
% === DSP settings ===
|
||||
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.debug_plots = false;
|
||||
|
||||
dsp_options.database_type = "mysql";
|
||||
|
||||
dsp_options.dataBase = "labor";
|
||||
dsp_options.storage_path = "W:\labdata\ECOC Silas\ecoc_2025";
|
||||
|
||||
dsp_options.server = "192.168.178.192";
|
||||
dsp_options.port = 3306;
|
||||
dsp_options.user = "silas";
|
||||
dsp_options.password = "silas";
|
||||
|
||||
db = DBHandler("dataBase", [dsp_options.dataBase],...
|
||||
"type", dsp_options.database_type,...
|
||||
"server", dsp_options.server,...
|
||||
"user", dsp_options.user, "password", dsp_options.password);
|
||||
|
||||
%%
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs','run_id','GREATER_EQUAL',3153);
|
||||
fp.where('Runs', 'symbolrate', 'EQUALS', 112e9); % 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', 'db_mode', 'EQUALS', '"no_db"');
|
||||
% fp.where('Runs', 'is_mpi', 'EQUALS', 1);
|
||||
fp.where('Runs', 'pam_level', 'EQUALS', 4);
|
||||
% fp.where('Runs', 'v_bias', 'EQUALS', 2.65);
|
||||
|
||||
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||
|
||||
% [~, uniqueSirRows] = unique(dataTable.sir, "stable");
|
||||
% dataTable = dataTable(uniqueSirRows, :);
|
||||
|
||||
[~, sortIdx] = sort(dataTable.sir, 'descend');
|
||||
dataTable = dataTable(sortIdx, :);
|
||||
% dataTable = dataTable(1, :);
|
||||
run_ids = dataTable.run_id;
|
||||
%%
|
||||
|
||||
wh_ = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\MPI_revisit\parallelization_analysis\wh_block_update_pam4_combined.mat");
|
||||
wh = wh_.wh;
|
||||
|
||||
% wh_ = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Diss\MPI_revisit\parallelization_analysis\wh_block_update_pam4_short_blocks.mat");
|
||||
% wh2 = wh_.wh;
|
||||
%
|
||||
% wh = mergeDataStorages(wh1,wh2);
|
||||
|
||||
%%
|
||||
|
||||
|
||||
storageNames = fieldnames(wh.sto);
|
||||
storageNames = storageNames([1,2,3,5]);
|
||||
ber_by_storage = struct();
|
||||
ber_mat_by_storage = struct();
|
||||
|
||||
sir_values = dataTable.sir(:).';
|
||||
block_updates = wh.parameter.block_update.values;
|
||||
storage_parameter_names = cellstr(wh.fn);
|
||||
block_axis = find(strcmp(storage_parameter_names,"block_update"),1);
|
||||
|
||||
if exist('linspecer','file')
|
||||
curve_colors = linspecer(max(numel(storageNames),1));
|
||||
else
|
||||
curve_colors = lines(max(numel(storageNames),1));
|
||||
end
|
||||
|
||||
useBoundedLines = true;
|
||||
usePolyfit = true;
|
||||
polyfitOrderMax = 4;
|
||||
fec_ber_threshold = 3.8e-3;%3.8e-3;
|
||||
fit_coeff_by_storage = struct();
|
||||
required_sir_fec_by_storage = struct();
|
||||
for storage_idx = 1:numel(storageNames)
|
||||
storageName = storageNames{storage_idx};
|
||||
fit_coeff_by_storage.(storageName) = cell(1,numel(block_updates));
|
||||
required_sir_fec_by_storage.(storageName) = nan(1,numel(block_updates));
|
||||
end
|
||||
|
||||
for block_idx = 1:numel(block_updates)
|
||||
block_update = block_updates(block_idx);
|
||||
|
||||
figure(3000 + block_idx); clf; hold on
|
||||
for storage_idx = 1:numel(storageNames)
|
||||
storageName = storageNames{storage_idx};
|
||||
curve_color = curve_colors(storage_idx,:);
|
||||
result = wh.sto.(storageName);
|
||||
|
||||
if ~isempty(block_axis) && size(result,block_axis) == numel(block_updates)
|
||||
result_subs = repmat({':'},1,ndims(result));
|
||||
result_subs{block_axis} = block_idx;
|
||||
result_for_block = result(result_subs{:});
|
||||
result_for_block = result_for_block(:).';
|
||||
else
|
||||
result_for_block = result(:).';
|
||||
end
|
||||
|
||||
% Each stored entry is a package cell containing one or more occurrences.
|
||||
ber_all = cell(1,numel(result_for_block));
|
||||
for result_idx = 1:numel(result_for_block)
|
||||
packageCell = result_for_block{result_idx};
|
||||
if isempty(packageCell)
|
||||
ber_all{result_idx} = NaN;
|
||||
continue
|
||||
end
|
||||
|
||||
ber_values = nan(1,numel(packageCell));
|
||||
for package_idx = 1:numel(packageCell)
|
||||
if isstruct(packageCell{package_idx}) && isfield(packageCell{package_idx},"metrics")
|
||||
ber_values(package_idx) = packageCell{package_idx}.metrics.BER;
|
||||
end
|
||||
end
|
||||
ber_all{result_idx} = ber_values;
|
||||
end
|
||||
|
||||
maxPackages = max(cellfun(@numel,ber_all));
|
||||
ber_mat = nan(maxPackages,numel(ber_all));
|
||||
for k = 1:numel(ber_all)
|
||||
ber_mat(1:numel(ber_all{k}),k) = ber_all{k};
|
||||
end
|
||||
|
||||
% Match the BER-over-SIR plot cleanup: remove high BER points and outliers per SIR.
|
||||
for col = 1:size(ber_mat,2)
|
||||
colData = ber_mat(:,col);
|
||||
colData(~isfinite(colData) | colData >= 0.1) = NaN;
|
||||
validColData = colData(isfinite(colData));
|
||||
if isempty(validColData)
|
||||
ber_mat(:,col) = NaN;
|
||||
continue
|
||||
end
|
||||
outliers = isoutlier(validColData);
|
||||
validColData(outliers) = NaN;
|
||||
colData(isfinite(colData)) = validColData;
|
||||
ber_mat(:,col) = colData;
|
||||
end
|
||||
|
||||
ber = mean(ber_mat,1,"omitnan");
|
||||
ber_min = nan(1,numel(ber));
|
||||
ber_max = nan(1,numel(ber));
|
||||
for k = 1:numel(ber)
|
||||
ber_here = ber_mat(:,k);
|
||||
ber_here = ber_here(isfinite(ber_here));
|
||||
if isempty(ber_here)
|
||||
continue
|
||||
end
|
||||
ber_min(k) = min(ber_here);
|
||||
ber_max(k) = max(ber_here);
|
||||
end
|
||||
|
||||
x = sir_values;
|
||||
if numel(x) ~= numel(ber)
|
||||
x = 1:numel(ber);
|
||||
end
|
||||
|
||||
ber_by_storage.(storageName){block_idx} = ber;
|
||||
ber_mat_by_storage.(storageName){block_idx} = ber_mat;
|
||||
|
||||
for k = 1:numel(x)
|
||||
scatter(repmat(x(k),maxPackages,1),ber_mat(:,k), ...
|
||||
30, ...
|
||||
'Marker','.', ...
|
||||
'MarkerEdgeColor',curve_color, ...
|
||||
'MarkerFaceColor',curve_color, ...
|
||||
'HandleVisibility','off');
|
||||
end
|
||||
|
||||
valid = isfinite(x) & isfinite(ber);
|
||||
if ~any(valid)
|
||||
continue
|
||||
end
|
||||
|
||||
% if useBoundedLines && exist("boundedline","file")
|
||||
% y_lower = max(ber - ber_min,0);
|
||||
% y_upper = max(ber_max - ber,0);
|
||||
% y_bounds = [y_lower(:), y_upper(:)];
|
||||
%
|
||||
% [hl, hp] = boundedline(x(valid).',ber(valid).',y_bounds(valid,:), ...
|
||||
% 'alpha', 'transparency', 0.08, ...
|
||||
% 'cmap', curve_color, ...
|
||||
% 'nan', 'fill', ...
|
||||
% 'orientation', 'vert');
|
||||
% set(hl, ...
|
||||
% 'LineStyle','none', ...
|
||||
% 'Marker','none', ...
|
||||
% 'HandleVisibility','off');
|
||||
% set(hp, ...
|
||||
% 'HandleVisibility','off', ...
|
||||
% 'LineStyle','none');
|
||||
% end
|
||||
|
||||
scatter(x(valid),ber(valid), ...
|
||||
20, ...
|
||||
'Marker','o', ...
|
||||
'MarkerEdgeColor',curve_color, ...
|
||||
'MarkerFaceColor',curve_color, ...
|
||||
'LineWidth',1, ...
|
||||
'DisplayName',storageName);
|
||||
|
||||
fit_mask = valid & ber > 0;
|
||||
if usePolyfit && nnz(fit_mask) >= 2
|
||||
fit_order = min(polyfitOrderMax,nnz(fit_mask)-1);
|
||||
fit_coeff = polyfit(x(fit_mask),log10(ber(fit_mask)),fit_order);
|
||||
x_fit = linspace(min(x(fit_mask)),max(x(fit_mask)),300);
|
||||
y_fit = 10.^polyval(fit_coeff,x_fit);
|
||||
fit_coeff_by_storage.(storageName){block_idx} = fit_coeff;
|
||||
|
||||
sir_req = NaN;
|
||||
threshold_mask = isfinite(y_fit) & y_fit <= fec_ber_threshold;
|
||||
if any(threshold_mask)
|
||||
first_threshold_idx = find(threshold_mask,1,"first");
|
||||
if first_threshold_idx == 1
|
||||
sir_req = x_fit(first_threshold_idx);
|
||||
else
|
||||
x_pair = x_fit(first_threshold_idx-1:first_threshold_idx);
|
||||
y_pair = log10(y_fit(first_threshold_idx-1:first_threshold_idx));
|
||||
if all(isfinite(y_pair)) && diff(y_pair) ~= 0
|
||||
sir_req = interp1(y_pair,x_pair,log10(fec_ber_threshold), ...
|
||||
"linear","extrap");
|
||||
else
|
||||
sir_req = x_fit(first_threshold_idx);
|
||||
end
|
||||
end
|
||||
end
|
||||
required_sir_fec_by_storage.(storageName)(block_idx) = sir_req;
|
||||
|
||||
plot(x_fit,y_fit, ...
|
||||
'LineWidth',1.1, ...
|
||||
'LineStyle','--', ...
|
||||
'Color',curve_color, ...
|
||||
'HandleVisibility','off');
|
||||
end
|
||||
end
|
||||
|
||||
title(sprintf("BER over SIR, block update = %g",block_update));
|
||||
xlabel("SIR (dB)");
|
||||
ylabel("BER");
|
||||
|
||||
yline(2.2e-4, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
yline(3.8e-3, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
yline(2e-2, 'LineWidth', 1, 'LineStyle', '--', 'HandleVisibility', 'off');
|
||||
ylim([9e-5, 0.1]);
|
||||
|
||||
set(gca, "YScale", "log");
|
||||
xlim([15,45]);
|
||||
grid on;
|
||||
box on;
|
||||
legend("Location", "best", "Interpreter", "none");
|
||||
end
|
||||
|
||||
%% Required SIR to reach FEC over parallelization
|
||||
|
||||
figure(5000); clf; hold on
|
||||
for storage_idx = 1:numel(storageNames)
|
||||
storageName = storageNames{storage_idx};
|
||||
curve_color = curve_colors(storage_idx,:);
|
||||
required_sir = required_sir_fec_by_storage.(storageName);
|
||||
valid = isfinite(block_updates) & isfinite(required_sir);
|
||||
|
||||
if ~any(valid)
|
||||
continue
|
||||
end
|
||||
|
||||
plot(block_updates(valid),required_sir(valid), ...
|
||||
'LineWidth',1.4, ...
|
||||
'LineStyle','-', ...
|
||||
'Marker','o', ...
|
||||
'MarkerSize',5, ...
|
||||
'Color',curve_color, ...
|
||||
'DisplayName',storageName);
|
||||
end
|
||||
|
||||
set(gca,'XScale','log');
|
||||
xticks(block_updates);
|
||||
xticklabels(string(block_updates));
|
||||
grid on
|
||||
box on
|
||||
xlabel("Parallelization / block update");
|
||||
ylabel(sprintf("Required SIR at BER = %.1e (dB)",fec_ber_threshold));
|
||||
title("Required SIR to reach FEC over parallelization");
|
||||
legend("Location","best","Interpreter","none");
|
||||
|
||||
|
||||
%%
|
||||
|
||||
|
||||
function whMerged = mergeDataStorages(varargin)
|
||||
% mergeDataStorages merges compatible DataStorage objects by physical indices.
|
||||
% Existing duplicate entries are concatenated when both values are package cells.
|
||||
|
||||
whList = varargin;
|
||||
templateWh = whList{1};
|
||||
paramNames = cellstr(templateWh.fn);
|
||||
|
||||
mergedParams = struct();
|
||||
for param_idx = 1:numel(paramNames)
|
||||
paramName = paramNames{param_idx};
|
||||
values = templateWh.parameter.(paramName).values(:).';
|
||||
|
||||
for wh_idx = 2:numel(whList)
|
||||
curWh = whList{wh_idx};
|
||||
if ~isequal(sort(cellstr(curWh.fn)),sort(paramNames))
|
||||
error("mergeDataStorages:ParameterMismatch", ...
|
||||
"All warehouses must use the same parameter names.");
|
||||
end
|
||||
if ~isfield(curWh.parameter,paramName)
|
||||
error("mergeDataStorages:ParameterMismatch", ...
|
||||
"Warehouse %d does not contain parameter '%s'.",wh_idx,paramName);
|
||||
end
|
||||
|
||||
newValues = curWh.parameter.(paramName).values(:).';
|
||||
for value_idx = 1:numel(newValues)
|
||||
if ~ismember(newValues(value_idx),values)
|
||||
values(end+1) = newValues(value_idx); %#ok<AGROW>
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
if strcmp(paramName,"block_update") && isnumeric(values)
|
||||
values = sort(values);
|
||||
end
|
||||
|
||||
mergedParams.(paramName) = values;
|
||||
end
|
||||
|
||||
whMerged = DataStorage(mergedParams);
|
||||
|
||||
for wh_idx = 1:numel(whList)
|
||||
curWh = whList{wh_idx};
|
||||
curStorageNames = fieldnames(curWh.sto);
|
||||
|
||||
for storage_idx = 1:numel(curStorageNames)
|
||||
storageName = curStorageNames{storage_idx};
|
||||
if ~isfield(whMerged.sto,storageName)
|
||||
whMerged.addStorage(storageName);
|
||||
end
|
||||
|
||||
for lin_idx = 1:numel(curWh.sto.(storageName))
|
||||
storedValue = curWh.sto.(storageName){lin_idx};
|
||||
if isempty(storedValue)
|
||||
continue
|
||||
end
|
||||
|
||||
[physValues,physNames] = curWh.getPhysIndicesByLinIndex(lin_idx);
|
||||
physNames = cellfun(@char,physNames,'UniformOutput',false);
|
||||
targetSubscripts = cell(1,numel(whMerged.fn));
|
||||
|
||||
for param_idx = 1:numel(whMerged.fn)
|
||||
paramName = char(whMerged.fn(param_idx));
|
||||
sourceParamIdx = find(strcmp(physNames,paramName),1);
|
||||
if isempty(sourceParamIdx)
|
||||
error("mergeDataStorages:ParameterMismatch", ...
|
||||
"Storage entry is missing parameter '%s'.",paramName);
|
||||
end
|
||||
targetSubscripts{param_idx} = whMerged.getIndexByPhys(paramName,physValues{sourceParamIdx});
|
||||
end
|
||||
|
||||
if isscalar(targetSubscripts)
|
||||
targetLinIdx = targetSubscripts{1};
|
||||
else
|
||||
targetLinIdx = sub2ind(whMerged.getStorageSize(),targetSubscripts{:});
|
||||
end
|
||||
|
||||
existingValue = whMerged.sto.(storageName){targetLinIdx};
|
||||
whMerged.sto.(storageName){targetLinIdx} = mergeStoredValue(existingValue,storedValue);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
function out = mergeStoredValue(existingValue,newValue)
|
||||
if isempty(existingValue)
|
||||
out = newValue;
|
||||
elseif iscell(existingValue) && iscell(newValue)
|
||||
out = [existingValue(:); newValue(:)].';
|
||||
else
|
||||
out = newValue;
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
55
projects/Diss/MPI_revisit/plot_mpi_run_id_timesignal.m
Normal file
55
projects/Diss/MPI_revisit/plot_mpi_run_id_timesignal.m
Normal file
@@ -0,0 +1,55 @@
|
||||
%% Quick plot of one MPI run_id time signal
|
||||
|
||||
clear; clc;
|
||||
|
||||
run_id = 3933;
|
||||
savePath = "W:\labdata\ECOC Silas\ecoc_2025\";
|
||||
|
||||
db = DBHandler("type", "mysql", "dataBase", "labor");
|
||||
|
||||
fp = QueryFilter();
|
||||
fp.where('Runs', 'run_id', 'EQUALS', run_id);
|
||||
|
||||
[dataTable, sql_query] = db.queryDB(fp, db.getTableFieldNames('Runs'));
|
||||
|
||||
if isempty(dataTable)
|
||||
error("No run found for run_id %d.", run_id);
|
||||
end
|
||||
|
||||
dataTable = dataTable(1, :);
|
||||
fsym = dataTable.symbolrate;
|
||||
M = double(dataTable.pam_level);
|
||||
|
||||
Symbols = load(char(savePath + string(dataTable.tx_symbols_path)));
|
||||
Symbols = Symbols.Symbols;
|
||||
|
||||
Scpe_sig_raw = load(char(savePath + string(dataTable.rx_raw_path)));
|
||||
Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
|
||||
Scpe_sig_raw = Scpe_sig_raw.normalize("mode", "rms");
|
||||
|
||||
figure(run_id); clf;
|
||||
showLevelScatter(Scpe_sig_raw, Symbols, ...
|
||||
"fsym", fsym, ...
|
||||
"syncFs", 2*fsym, ...
|
||||
"fignum", run_id, ...
|
||||
"normalize", true, ...
|
||||
"debug_plots", false, ...
|
||||
"showPlot", true, ...
|
||||
"showStdAnnotations", true, ...
|
||||
"yLimits", [-2.5 2.5], ...
|
||||
"xLimits", [0 25], ...
|
||||
"scatterAlpha", 0.25, ...
|
||||
"scatterSize", 1, ...
|
||||
"avgLineMaxPoints", 500, ...
|
||||
"avgLineSmoothWindow", 5);
|
||||
|
||||
title(sprintf("run\\_id %d, PAM %.0f, %.0f m, SIR %.2f dB", ...
|
||||
run_id, M, dataTable.interference_path_length, dataTable.sir));
|
||||
|
||||
fprintf("run_id: %d\n", run_id);
|
||||
fprintf("PAM: %.0f\n", M);
|
||||
fprintf("symbolrate: %.2f GBd\n", fsym*1e-9);
|
||||
fprintf("path length: %.0f m\n", dataTable.interference_path_length);
|
||||
fprintf("SIR: %.2f dB\n", dataTable.sir);
|
||||
fprintf("v_bias: %.3g V\n", dataTable.v_bias);
|
||||
fprintf("P_rx: %.2f dBm\n", dataTable.power_pd_in);
|
||||
BIN
workerError.mat
BIN
workerError.mat
Binary file not shown.
Reference in New Issue
Block a user