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MPI analysis, work on DC tracking

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This commit is contained in:
Silas Oettinghaus
2026-07-09 12:29:41 +02:00
parent 3cafb06c4f
commit 753bc582b9
6 changed files with 253 additions and 44 deletions
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194
Classes/04_DSP/Equalizer/FFE.m
View File

@@ -33,6 +33,7 @@ classdef FFE < handle
epochs_dd
dd_len_fraction
mu_dc
adaptive_dc_enabled
e_dc
P % covariance matrix of rls
@@ -48,6 +49,8 @@ classdef FFE < handle
mu_optimization
mu_optimization_iter = 0;
mu_optimization_len
plot_mu_optimization = 0;
mu_optimization_fignum = 3010;
end
methods
@@ -67,12 +70,15 @@ classdef FFE < handle
options.epochs_dd = 5;
options.dd_len_fraction = 1;
options.mu_dc = 0;
options.adaptive_dc_enabled = false;
options.decide = false;
options.save_debug = 0;
options.optmize_mus = 0;
options.mu_optimization_len = 2^15;
options.plot_mu_optimization = 0;
options.mu_optimization_fignum = 3010;
end
@@ -175,12 +181,40 @@ classdef FFE < handle
epochs = 1;
end
P_err = 0;
alpha = 0.98;
err_prev = 0;
gamma_dc = 1e-6;
mu_min = 1e-6;
mu_max = 3e-1;
debug_enabled = obj.save_debug;
if debug_enabled
n_symbols_debug = ceil(N / obj.sps);
obj.debug_struct.error = NaN(1,n_symbols_debug);
obj.debug_struct.error_first_epoch = NaN(1,n_symbols_debug);
obj.debug_struct.main_cursor = NaN(1,n_symbols_debug);
obj.debug_struct.mu_nlms = NaN(1,n_symbols_debug);
obj.debug_struct.update_gradient = NaN(1,n_symbols_debug);
obj.debug_struct.mu_dc_eff = NaN(1,n_symbols_debug);
obj.debug_struct.e_dc_eff = NaN(1,n_symbols_debug);
if training
obj.debug_struct.error_tr = NaN(1,n_symbols_debug);
obj.debug_struct.update_tr = NaN(1,n_symbols_debug);
else
obj.debug_struct.error_dd = NaN(1,n_symbols_debug);
obj.debug_struct.update = NaN(1,n_symbols_debug);
end
end
for epoch = 1 : epochs
symbol = 0;
% obj.e_dc = 0;
for sample = 1 : obj.sps : N
symbol = symbol+1;
mu_dc_eff = 0;
U = x(obj.order+sample-1:-1:sample);
@@ -240,31 +274,42 @@ classdef FFE < handle
end
if obj.mu_dc ~= 0
obj.e_dc = obj.e_dc + obj.mu_dc * err(symbol);
if obj.adaptive_dc_enabled
delta_mu = gamma_dc * err(symbol) * err_prev * (U.'*U);
obj.mu_dc = min(max(obj.mu_dc + delta_mu,mu_min),mu_max);
err_prev = err(symbol);
P_err = alpha*P_err + (1-alpha)*err(symbol)^2;
mu_dc_eff = obj.mu_dc / (P_err + eps);
else
mu_dc_eff = obj.mu_dc;
end
obj.e_dc = obj.e_dc + mu_dc_eff * err(symbol);
end
end
if obj.save_debug
obj.debug_struct.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
if training
obj.debug_struct.error_tr(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
obj.debug_struct.update_tr(epoch,symbol) = update.'*update ./ rms(obj.e);
else
% if symbol == length(d)
% M = numel(unique(d));
% bits_ref = PAMmapper(M,0).demap(d);
% bits_eq = PAMmapper(M,0).demap(d_hat);
% [~,~,obj.debug_struct.ber(epoch)] = calc_ber(bits_eq,bits_ref);
% end
end
if debug_enabled && epoch == 1
obj.debug_struct.error_first_epoch(1,symbol) = err(symbol) * err(symbol)';
end
% obj.debug_struct.main_cursor(epoch,symbol) = abs(obj.e(maincursor_pos));
% obj.debug_struct.mu_nlms(epoch,symbol) = weight;
% obj.debug_struct.update_gradient(epoch,symbol) = grad.'*grad;
% obj.debug_struct.update(epoch,symbol) = update.'*update ./ rms(obj.e);
if debug_enabled && epoch == epochs
error_power = err(symbol) * err(symbol)';
update_power = update.'*update ./ (rms(obj.e) + eps);
obj.debug_struct.error(1,symbol) = error_power;
obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
obj.debug_struct.mu_nlms(1,symbol) = weight;
obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
obj.debug_struct.mu_dc_eff(1,symbol) = mu_dc_eff;
obj.debug_struct.e_dc_eff(1,symbol) = obj.e_dc;
if training
obj.debug_struct.error_tr(1,symbol) = error_power;
obj.debug_struct.update_tr(1,symbol) = update_power;
else
obj.debug_struct.error_dd(1,symbol) = error_power;
obj.debug_struct.update(1,symbol) = update_power;
end
end
end
@@ -272,17 +317,6 @@ classdef FFE < handle
end
function N_dd = ddLength(obj,N)
if isempty(obj.dd_len_fraction) || obj.dd_len_fraction <= 0 || obj.dd_len_fraction >= 1
N_dd = N;
return
end
N_dd = floor(N * obj.dd_len_fraction);
N_dd = max(obj.sps,N_dd);
N_dd = min(N,N_dd);
end
function optimizeMus(obj,x,d)
[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d);
@@ -333,6 +367,10 @@ classdef FFE < handle
fprintf("\nFFE mu opt done: mu_tr=%9.3e, BER=%9.3e\n", ...
obj.mu_tr,obj.mu_optimization.MinObjective);
end
if obj.plot_mu_optimization
obj.plotMuOptimization();
end
end
function [x_opt,d_opt,N_opt] = optimizationSignals(obj,x,d)
@@ -402,5 +440,99 @@ classdef FFE < handle
obj.save_debug = old_debug;
obj.mu_dc = old_mu_dc;
end
function plotMuOptimization(obj)
if isempty(obj.mu_optimization)
return
end
X = obj.mu_optimization.XTrace;
objective = obj.mu_optimization.ObjectiveTrace;
objective = objective(:);
valid = isfinite(objective);
if isempty(X) || ~any(valid)
return
end
var_names = X.Properties.VariableNames;
n_vars = numel(var_names);
eval_idx = (1:numel(objective)).';
objective_plot = obj.positiveObjectiveForLogPlot(objective);
best_plot = obj.positiveObjectiveForLogPlot(cummin(objective));
figure(obj.mu_optimization_fignum);
clf;
t = tiledlayout(2,2,"TileSpacing","compact","Padding","compact");
title(t,"FFE Bayesian mu optimization");
nexttile;
h_candidate = semilogy(eval_idx,objective_plot,"o-","DisplayName","candidate");
obj.addOptimizationDataTips(h_candidate,X,objective,objective_plot,eval_idx,var_names);
hold on;
h_best_trace = semilogy(eval_idx,best_plot,"k-","LineWidth",1.2,"DisplayName","best so far");
h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Best BER",best_plot);
grid on;
xlabel("Evaluation");
ylabel("BER");
legend("Location","best");
if n_vars < 2
return
end
pairs = nchoosek(1:n_vars,2);
n_pair_plots = min(size(pairs,1),3);
[~,best_idx] = min(objective);
for pair_idx = 1:n_pair_plots
nexttile;
x_name = var_names{pairs(pair_idx,1)};
y_name = var_names{pairs(pair_idx,2)};
x_data = X.(x_name);
y_data = X.(y_name);
c_data = log10(objective_plot);
h_scatter = scatter(log10(x_data),log10(y_data),35,c_data,"filled");
obj.addOptimizationDataTips(h_scatter,X,objective,objective_plot,eval_idx,var_names);
hold on;
h_best = plot(log10(x_data(best_idx)),log10(y_data(best_idx)),"kp", ...
"MarkerSize",12, ...
"MarkerFaceColor","y", ...
"DisplayName","best");
obj.addOptimizationDataTips(h_best,X(best_idx,:),objective(best_idx),objective_plot(best_idx),eval_idx(best_idx),var_names);
grid on;
xlabel("log10(" + string(x_name) + ")");
ylabel("log10(" + string(y_name) + ")");
cb = colorbar;
cb.Label.String = "log10(BER)";
title(string(x_name) + " vs " + string(y_name));
end
end
function addOptimizationDataTips(~,plot_handle,X,objective,objective_plot,eval_idx,var_names)
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER",objective);
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER shown",objective_plot);
for var_idx = 1:numel(var_names)
var_name = var_names{var_idx};
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow(var_name,X.(var_name));
end
end
function objective_plot = positiveObjectiveForLogPlot(~,objective)
objective_plot = objective;
positive_values = objective(isfinite(objective) & objective > 0);
if isempty(positive_values)
floor_value = 1e-12;
else
floor_value = min(positive_values) / 10;
end
objective_plot(~isfinite(objective_plot) | objective_plot <= 0) = floor_value;
end
end
end

2
Functions/EQ_blocks/ffe.m
View File

@@ -1,4 +1,4 @@
function [ffe_results] = ffe(eq_, M, rx_signal, tx_symbols, tx_bits, options)
function [ffe_results,eq_signal_sd] = ffe(eq_, M, rx_signal, tx_symbols, tx_bits, options)
% FFE Processes signals through FFE equalizer
%
% Inputs:

95
Functions/EQ_recipes/mpi_recipe_dev.m
View File

@@ -20,22 +20,23 @@ function output = mpi_recipe_dev(Scpe_sig_raw, Symbols, Tx_bits, options)
"mode", "auto", ...
"debug_plots", options.debug_plots);
mu_dc = 0;%1e-5;
eq_settings = { ...
"epochs_tr", 5, ...
"epochs_dd", 5, ...
"len_tr", 4096*2, ...
"mu_dd",0.02, ...
"mu_tr",0.2, ...
"len_tr", 4096, ...
"mu_tr",0.04, ...
"mu_dd",0.012, ...
"mu_dc", 0.012, ...
"adaptive_dc_enabled", 0, ...
"order", 25, ...
"sps", 2, ...
"decide", 0, ...
"optmize_mus", 1, ...
"optmize_mus", 0, ...
"dd_mode", 1, ...
"adaption_technique", "nlms", ...
"mu_dc", mu_dc};
"plot_mu_optimization", options.debug_plots,...
"save_debug",true};
eq_ffe = FFE(eq_settings{:});
@@ -51,7 +52,7 @@ function output = mpi_recipe_dev(Scpe_sig_raw, Symbols, Tx_bits, options)
%% NORMAL FFE
if 1
ffe_results = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
[ffe_results,equalized_signal] = ffe(eq_ffe, options.M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", options.duob_mode, ...
'showAnalysis', options.debug_plots, ...
"postFFE", [], ...
@@ -60,8 +61,84 @@ function output = mpi_recipe_dev(Scpe_sig_raw, Symbols, Tx_bits, options)
ffe_results.metrics.print("description",sprintf('Normal FFE; SIR %d dB',options.dataTable.sir));
output.ffe_package = ffe_results;
[~, avg_for_lvl] = showLevelScatter(equalized_signal, Symbols, ...
"fsym", options.fsym, ...
"fignum", 401, ...
"normalize", true);
output.ffe_debug = struct();
dbg = eq_ffe.debug_struct;
smooth_len = 501;
output.ffe_debug.smoothing_window = smooth_len;
error_first_s = movmean(dbg.error_first_epoch(:),smooth_len,"omitnan");
error_last_s = movmean(dbg.error(:),smooth_len,"omitnan");
mu_dc_eff_s = movmean(dbg.mu_dc_eff(:),smooth_len,"omitnan");
e_dc_eff_s = movmean(dbg.e_dc_eff(:),smooth_len,"omitnan");
figure(460); clf;
tiledlayout(2,1,"TileSpacing","compact","Padding","compact");
nexttile;
semilogy(error_first_s + eps,"DisplayName","first epoch");
hold on;
semilogy(error_last_s + eps,"DisplayName","last epoch");
grid on;
ylabel("error");
title("FFE debug error");
legend("Location","best");
nexttile;hold on
% plot(eq_sig_mov,"DisplayName","err dc eff");
plot(mu_dc_eff_s,"DisplayName","mu DC - either fixed or adaptive");
e_dc_scale = max(abs(e_dc_eff_s),[],"omitnan") + eps;
plot(-1.*e_dc_eff_s./e_dc_scale,"DisplayName","Inverted DC-tracking; Value that is subtracted during EQ");
avg_lvl_dc=mean(avg_for_lvl,1,"omitnan");
plot(avg_lvl_dc./0.01,"DisplayName","Smoothed EQ output signal; calc'd by showLevelScatter");
grid on;
xlabel("Symbol");
ylabel("mu dc eff");
title("Effective adaptive DC step");
legend
inv_dc_track = -e_dc_eff_s(:);
avg_lvl_dc = avg_lvl_dc(:);
xcorr_len = min(numel(inv_dc_track),numel(avg_lvl_dc));
inv_dc_xcorr = inv_dc_track(1:xcorr_len);
avg_lvl_xcorr = avg_lvl_dc(1:xcorr_len);
inv_dc_xcorr = fillmissing(inv_dc_xcorr,"linear","EndValues","nearest");
avg_lvl_xcorr = fillmissing(avg_lvl_xcorr,"linear","EndValues","nearest");
inv_dc_xcorr = inv_dc_xcorr - mean(inv_dc_xcorr,"omitnan");
avg_lvl_xcorr = avg_lvl_xcorr - mean(avg_lvl_xcorr,"omitnan");
[dc_level_xcorr,dc_level_lags] = xcorr(inv_dc_xcorr,avg_lvl_xcorr,"coeff");
[dc_level_corr,dc_level_idx] = max(dc_level_xcorr);
dc_level_delay_symbols = dc_level_lags(dc_level_idx);
output.ffe_debug.dc_level_delay_symbols = dc_level_delay_symbols;
output.ffe_debug.dc_level_delay_corr = dc_level_corr;
output.ffe_debug.dc_level_lags = dc_level_lags;
output.ffe_debug.dc_level_xcorr = dc_level_xcorr;
fprintf("FFE debug: xcorr(inv DC tracking, avg level) delay=%d symbols, corr=%6.3f\n", ...
dc_level_delay_symbols,dc_level_corr);
figure(461); clf;
plot(dc_level_lags,dc_level_xcorr,"DisplayName","xcorr");
hold on;
plot(dc_level_delay_symbols,dc_level_corr,"ro","DisplayName","max");
grid on;
xlabel("Lag in symbols");
ylabel("Correlation coefficient");
title(sprintf("Delay estimate: %d symbols (corr %.3f)",dc_level_delay_symbols,dc_level_corr));
legend("Location","best");
end
%% MPI Reduction
if 1

2
Functions/EQ_visuals/showLevelScatter.m
View File

@@ -1,4 +1,4 @@
function [symbols_for_lvl, avg_for_lvl, info] = showLevelScatter(rxInput, refSymbols, options)
function [symbols_for_lvl, avg_for_lvl, xAxisUs, info] = showLevelScatter(rxInput, refSymbols, options)
%SHOWLEVELSCATTER Plot received samples separated by reference PAM level.
% Supports plain numeric vectors, Signal objects, synchronized scope cell
% arrays, and raw unsynchronized Signal input. Raw Signal input is

4
projects/Diss/MPI_revisit/investigate_mpi_algorithms.m
View File

@@ -3,9 +3,9 @@ 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 = 2;
dsp_options.max_occurences = 10;
dsp_options.debug_plots = true;
dsp_options.debug_plots = false;
dsp_options.database_type = "mysql";

BIN
workerError.mat
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