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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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128
Classes/04_DSP/Equalizer/FFE.m
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@@ -3,24 +3,34 @@ classdef FFE < handle
% 1) Training mode (stable performance when you use NLMS)
% 2) Decision directed mode
% Eq = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
%LMS: mu in order of 0.001 for acceptable convergence speed
%NLMS: mu in order of 0.01 for acceptable convergence speed
%RLS: mu is lambda -> 0.99 -> 1 (has a strong dependency on this! use a loop to find out best values)
% FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption",adaption_method(adaption),"dd_mode",use_dd_mode);
properties
sps % usually 2
order
e
e_tr
error
debug_struct
len_tr
mu_tr
epochs_tr
mu_dd
adaption_technique % nlms, lms, rls
dd_mode % 1 or 0 to set DD-mode on or off
mu_dd %weight update in dd mode
epochs_dd
constellation
P % covariance matrix of rls
decide
constellation % symbol constellation
decide %wether to return the (hard) decisions or the result after FFE (soft)
end
methods
@@ -34,6 +44,8 @@ classdef FFE < handle
options.mu_tr = 0;
options.epochs_tr = 5;
options.adaption_technique adaption_method = adaption_method.lms;
options.dd_mode = 1;
options.mu_dd = 1e-5;
options.epochs_dd = 5;
@@ -59,10 +71,15 @@ classdef FFE < handle
obj.constellation = unique(D.signal);
delta = 0.05;
obj.P = (1/delta) * eye(obj.order);
% Training Mode
training = 1;
showviz = 0;
obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
obj.e_tr = obj.e;
% Decision Directed Mode
n = X.length;
@@ -71,7 +88,7 @@ classdef FFE < handle
[signal,decision]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
% Output Signal
if obj.decide
if obj.decide
X.signal = decision;
else
X.signal = signal;
@@ -83,24 +100,25 @@ classdef FFE < handle
Noi = X;
Noi = X - D;
end
function [y,d_hat] = equalize(obj,x,d,mio,epochs,N,training,showviz)
function [y,d_hat] = equalize(obj,x,d,mu,epochs,N,training,showviz)
arguments
obj
x
d
mio
epochs
N
training
obj
x
d
mu
epochs
N
training
showviz
end
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
lambda = mu;
if training
mask = ones(obj.order,1);
@@ -111,8 +129,17 @@ classdef FFE < handle
end
mask = ones(obj.order,1);
maincursor_pos=ceil(length(obj.e)/2);
always_ideal_decision = 0;
save_debug = 1;
grad =0;
weight = 0;
update = 0;
if mu == 0 || (~obj.dd_mode && ~training)
epochs = 1;
end
for epoch = 1 : epochs
symbol = 0;
for sample = 1 : obj.sps : N
@@ -124,30 +151,75 @@ classdef FFE < handle
y(symbol,1) = (obj.e.*mask).' * U; % Calculating output of LMS __ * |
if training
d_hat(symbol,1) = d(symbol);
d_hat(symbol,1) = d(symbol);
else
[~,symbol_idx] = min(abs(y(symbol) - obj.constellation)); % decision for closest constellation point
d_hat(symbol,1) = obj.constellation(symbol_idx);
if ~always_ideal_decision
[~,symbol_idx] = min(abs(y(symbol) - obj.constellation)); % decision for closest constellation point
d_hat(symbol,1) = obj.constellation(symbol_idx);
else
d_hat(symbol,1) = d(symbol);
end
end
err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
% err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
err(symbol) = d_hat(symbol) - y(symbol); % Instantaneous error
true_err(symbol) = y(symbol) - d(symbol); % Instantaneous error
if mio ~= 0
obj.e = obj.e - (mio * err(symbol) * U) ; % Weight update rule of LMS
else
normalizationfactor = (U.' * U);
obj.e = obj.e - err(symbol) * U / normalizationfactor; % Weight update rule of NLMS
if training || obj.dd_mode
switch obj.adaption_technique
case adaption_method.lms
% mu used as update weight (suggestion: 0.001)
weight = mu;
grad = err(symbol) * U;
update = grad * weight;
obj.e = obj.e + update;
case adaption_method.nlms
% mu used as update weight (suggestion: 0.01-0.05; bit higher during tr)
normU = ((U.'*U)) + eps;
weight = mu / normU;
grad = err(symbol) * U;
update = grad * weight;
obj.e = obj.e + update;
case adaption_method.rls
% RLS‐Gain:
denom = lambda + U.' * obj.P * U;
k = (obj.P * U) / denom;
% Gewichtsupdate:
update = k * err(symbol);
obj.e = obj.e + update;
% P-Matrix‐Update:
obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
end
end
obj.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
if 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);
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);
end
end
end
end
end
end
end