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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
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@@ -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