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Many changes towards simulation of JLT and once again the evaluation of the Highspeed data from Lab experiments 2024

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Silas Oettinghaus
2025-07-09 10:50:53 +02:00
parent 9ce23c4a10
commit 2cff29f239
35 changed files with 1874 additions and 549 deletions
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127
Classes/04_DSP/Equalizer/FFE_DCremoval_adaptive_mu.m
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@@ -9,6 +9,7 @@ classdef FFE_DCremoval_adaptive_mu < handle
sps % usually 2
order
e
e_tr
error
len_tr
@@ -21,6 +22,8 @@ classdef FFE_DCremoval_adaptive_mu < handle
mu_dc
dc_buffer_len
adaptive_mu_mode
ffe_buffer_len
smoothing_buffer_length
@@ -50,6 +53,8 @@ classdef FFE_DCremoval_adaptive_mu < handle
options.ffe_buffer_len = 1;
options.adaptive_mu_mode = 1;
options.smoothing_buffer_length = 0;
options.smoothing_buffer_update = 0;
options.decide = false;
@@ -90,6 +95,7 @@ classdef FFE_DCremoval_adaptive_mu < handle
% Training Mode
training = 1;
obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training);
obj.e_tr = obj.e;
% Decision Directed Mode
N = X.length;
@@ -124,6 +130,10 @@ classdef FFE_DCremoval_adaptive_mu < handle
training % boolean flag: true->training mode, false->DD mode
end
if isempty(obj.e)
obj.e = zeros(obj.order,1);
end
% Zero-padding for filter memory
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
@@ -140,7 +150,7 @@ classdef FFE_DCremoval_adaptive_mu < handle
err_prev = 0; % previous error sample for VSS correlation
gamma_dc = 1e-6; % meta step-size for DC VSS
mu_min = 1e-6; % lower bound for mu_dc
mu_max = 1e-1; % upper bound for mu_dc
mu_max = 3e-1; % upper bound for mu_dc
% DC removal buffer
L = obj.dc_buffer_len; % buffer length
@@ -152,7 +162,6 @@ classdef FFE_DCremoval_adaptive_mu < handle
if ~training
% each column holds one past gradient of length obj.order
grad_buf = NaN(obj.order, L_grad);
end
smth_buffer = zeros(1, obj.smoothing_buffer_length);
@@ -187,7 +196,11 @@ classdef FFE_DCremoval_adaptive_mu < handle
%-- 3) error
e_val = y(s) - d_hat(s);
err(s,idx) = e_val;
if epoch == epochs
err(s,idx) = e_val;
true_err(s,idx) = y(s) - d(s);
end
%-- 4) tap-weight update: training immediate, DD buffered
if training
@@ -199,7 +212,7 @@ classdef FFE_DCremoval_adaptive_mu < handle
obj.e = obj.e - e_val * U / normU;
end
else
if 1
if 0
% buffer gradient
if mu_lms ~= 0
grad = e_val * U;
@@ -226,22 +239,40 @@ classdef FFE_DCremoval_adaptive_mu < handle
%-- 5) DC adaptation
if obj.mu_dc ~= 0
% VSS for mu_dc
delta_mu = gamma_dc * e_val * err_prev * (U.'*U);
obj.mu_dc = min(max(obj.mu_dc + delta_mu, mu_min), mu_max);
err_prev = e_val;
if obj.adaptive_mu_mode
% VSS for mu_dc
delta_mu = gamma_dc * e_val * err_prev * (U.'*U);
obj.mu_dc = min(max(obj.mu_dc + delta_mu, mu_min), mu_max);
err_prev = e_val;
% DC buffer update & periodic estimate
P_err = alpha*P_err + (1-alpha)*e_val^2;
mu_dc_norm = obj.mu_dc / (P_err + eps);
else
% DC buffer update & periodic estimate
% P_err = alpha*P_err + (1-alpha)*e_val^2;
% mu_dc_norm = obj.mu_dc / (P_err + eps);
mu_dc_norm = obj.mu_dc;
end
% DC buffer update & periodic estimate
P_err = alpha*P_err + (1-alpha)*e_val^2;
mu_dc_norm = obj.mu_dc / (P_err + eps);
e_dc_buf = circshift(e_dc_buf, 1);
e_dc_buf(1) = e_dc_est - mu_dc_norm * e_val;
if mod(s, L) == 0
e_dc_est = median(e_dc_buf, 'omitnan');
end
P_err_save(s) = P_err;
% Pcorr_save(s) = e_val * err_prev;
Ucorr_save(s) = (U.'*U);
mu_dc_save(s) = mu_dc_norm;
e_dc_save(s) = e_dc_est;
end
% store instantaneous squared error
@@ -250,23 +281,71 @@ classdef FFE_DCremoval_adaptive_mu < handle
end
% Optional plotting in DD mode (uncomment if needed)
if ~training
figure(342);clf
if 0%~training
constellation = unique(d);
lvlcol = cbrewer2('Paired', numel(constellation)*2);
lvlcol = lvlcol(2:2:end, :);
true_err(true_err==0) = NaN;
true_errmoverr = movsum(true_err, 4096, 'omitnan');
true_errmoverr = true_errmoverr./rms(true_errmoverr);
moverr = movsum(err, [100,100], 'omitnan');
moverr = moverr./rms(moverr);
figure(500); clf
hold on
scatter(1:numSymbols, err + obj.constellation', '.', 'SizeData', 1);
yline(obj.constellation);
% 1st subplot: true_errmoverr
% subplot(2,2,1); hold on
% for k = 1:4
% scatter(1:numSymbols, true_errmoverr(:,k), 1, lvlcol(k,:), '.');
% end
% scatter(1:numSymbols, Ucorr_save./rms(Ucorr_save), 1, lvlcol(1,:), '.','DisplayName','Ucorr_save');
% scatter(1:numSymbols, Pcorr_save./rms(Pcorr_save), 1, lvlcol(1,:), '.','DisplayName','P_corr');
% scatter(1:numSymbols, P_err_save, 1, lvlcol(1,:), '.','DisplayName','P_err');
% scatter(1:numSymbols, mu_dc_save, 1, lvlcol(2,:), '.','DisplayName','adapted value of $\mu_{DC}$');
% scatter(1:numSymbols, sum(moverr,2,'omitnan'), 1, lvlcol(1,:), '.','DisplayName','Mov Error $\hat{d}$ - x over all levels');
scatter(1:numSymbols, sum(e_dc_save,2,'omitnan'), 1, lvlcol(2,:), '.','DisplayName','Est. Error that is subtracted');
title('Moving Sum Error');
hold off
legend
% 2nd subplot: moverr
subplot(2,2,2); hold on
for k = 1:4
scatter(1:numSymbols, moverr(:,k), 1, lvlcol(k,:), '.');
end
title('Moving Sum Error');
hold off
legend
% 3rd subplot: err
subplot(2,2,3); hold on
for k = 1:4
scatter(1:numSymbols, err(:,k), 1, lvlcol(k,:), '.');
end
title('Error');
hold off
legend
% 4th subplot: err + obj.constellation'
subplot(2,2,4); hold on
for k = 1:4
scatter(1:numSymbols, err(:,k) + obj.constellation(k), 1, lvlcol(k,:), '.');
end
yline(obj.constellation, '--k');
title('Error + Constellation');
hold off
legend
sgtitle('Error Analysis Subplots');
end
end
function mu = update_mu()
end
end
end