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Scattered stuff from Silas during Dissertation

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This commit is contained in:
Silas Oettinghaus
2026-06-03 09:05:33 +02:00
parent a91da3b97c
commit 5c2e27687d
37 changed files with 1726 additions and 1338 deletions
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97
Classes/04_DSP/Equalizer/VNLE.m
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@@ -10,6 +10,7 @@ classdef VNLE < handle
sps % usually 2
order
e
e_dc
error
len_tr
@@ -18,10 +19,17 @@ classdef VNLE < handle
mu_dd
epochs_dd
mu_dc
constellation
decide
save_debug = 0;
debug_struct
optmize_mus = 0;
mu_optimization
mu_optimization_iter = 0;
x_norm
ce
@@ -42,8 +50,11 @@ classdef VNLE < handle
options.mu_dd = 1e-5;
options.epochs_dd = 5;
options.mu_dc = 0;
options.decide = false;
options.save_debug = 0;
options.optmize_mus = 0;
end
@@ -54,6 +65,7 @@ classdef VNLE < handle
obj.error = 0;
obj.e_dc = 0;
end
@@ -69,6 +81,13 @@ classdef VNLE < handle
[obj.ie2,obj.ie3] = obj.calcIndiceVectors(obj.order);
obj.e = zeros( sum(obj.ce) ,1);
obj.e_dc = 0;
if obj.optmize_mus
obj.optimizeMus(X.signal,D.signal);
obj.e = zeros(sum(obj.ce),1);
obj.e_dc = 0;
end
% Training Mode
training = 1;
@@ -148,7 +167,7 @@ classdef VNLE < handle
x_in = x(obj.order(1)+sample-1:-1:sample);
x_in = obj.calcVNLENonlinVecs(x_in,obj.ie2,obj.ie3,obj.order,obj.x_norm);
y(symbol,1) = obj.e.' * x_in; % Calculating output of LMS __ * |
y(symbol,1) = obj.e_dc + obj.e.' * x_in; % Calculating output of LMS __ * |
if training
err = y(symbol) - d(symbol); % Instantaneous error
@@ -164,6 +183,9 @@ classdef VNLE < handle
normalizationfactor = (x_in.' * x_in);
obj.e = obj.e - err * x_in / normalizationfactor; % Weight update rule of NLMS
end
if obj.mu_dc ~= 0
obj.e_dc = obj.e_dc - obj.mu_dc * err;
end
if mod(sample,100) == 1 && showviz
a2.XData = 1:2*numel(y);
@@ -177,12 +199,85 @@ classdef VNLE < handle
end
obj.error(epoch,symbol) = err * err'; % Instantaneous square error
if obj.save_debug
obj.debug_struct.error(epoch,symbol) = err * err';
if training
obj.debug_struct.error_tr(epoch,symbol) = err * err';
end
end
end
end
end
function optimizeMus(obj,x,d)
mu_range = [1e-5, 1e-2];
mu_dc_range = [1e-5, 1e-1];
vars = [optimizableVariable("mu_tr",mu_range,"Transform","log"), ...
optimizableVariable("mu_dd",mu_range,"Transform","log")];
optimize_mu_dc = obj.mu_dc ~= 0;
if optimize_mu_dc
vars = [vars, optimizableVariable("mu_dc",mu_dc_range,"Transform","log")];
end
obj.mu_optimization_iter = 0;
obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x,d),vars, ...
"MaxObjectiveEvaluations",10, ...
"AcquisitionFunctionName","expected-improvement-plus", ...
"IsObjectiveDeterministic",false, ...
"Verbose",0, ...
"PlotFcn",[]);
obj.mu_tr = obj.mu_optimization.XAtMinObjective.mu_tr;
obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
if optimize_mu_dc
obj.mu_dc = obj.mu_optimization.XAtMinObjective.mu_dc;
end
objective_db = 10*log10(obj.mu_optimization.MinObjective);
if optimize_mu_dc
fprintf("\nVNLE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, mu_dc=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB\n", ...
obj.mu_tr,obj.mu_dd,obj.mu_dc,obj.mu_optimization.MinObjective,objective_db);
else
fprintf("\nVNLE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB\n", ...
obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective,objective_db);
end
end
function objective = muObjective(obj,params,x,d)
old_debug = obj.save_debug;
old_mu_dc = obj.mu_dc;
obj.save_debug = 1;
optimize_mu_dc = ismember("mu_dc",string(params.Properties.VariableNames));
if optimize_mu_dc
obj.mu_dc = params.mu_dc;
end
obj.e = zeros(sum(obj.ce),1);
obj.e_dc = 0;
obj.debug_struct = struct();
obj.equalize(x,d,params.mu_tr,obj.epochs_tr,obj.len_tr,1,0);
obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),0,0);
objective = mean(obj.debug_struct.error(end,:),"omitnan");
if ~isfinite(objective)
objective = inf;
end
objective_db = 10*log10(objective);
obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
if optimize_mu_dc
fprintf("\rVNLE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, mu_dc=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB", ...
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,params.mu_dc,objective,objective_db);
else
fprintf("\rVNLE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB", ...
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,objective,objective_db);
end
obj.save_debug = old_debug;
obj.mu_dc = old_mu_dc;
end
%% Functions needed During Adaption
function x_in_vnle_format = calcVNLENonlinVecs(~,x_in_block,I_2,I_3,N_,norm_)
% These are the second and third order input signal products of the VNLE EQ