silas/imdd_silas
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

updates of framework

Browse Source 
- focus on AWG output power and lowpass characteristics
This commit is contained in:
Silas Oettinghaus
2024-04-26 14:08:21 +02:00
parent 0600abfcbf
commit 7c1d9850d6
25 changed files with 864 additions and 279 deletions
Download Patch File Download Diff File Expand all files Collapse all files

60
Classes/04_DSP/EQ_silas.m
View File

@@ -12,6 +12,7 @@ classdef EQ_silas < handle
d_constellation %constellation points of the reference
y_out %equalizer output signal
d_out %decision output
% FFE coefficients always named with "e"
Ne
@@ -107,7 +108,7 @@ classdef EQ_silas < handle
end
function [signalclass_out] = process(obj,signalclass_in, reference_signalclass_in)
function [signalclass_out,symbols_out] = process(obj,signalclass_in, reference_signalclass_in)
% actual processing of the signal (steps 1. - 3.)
% 1 normalize RMS
@@ -117,6 +118,7 @@ classdef EQ_silas < handle
obj.process_(signalclass_in.signal', reference_signalclass_in.signal');
signalclass_in.signal = obj.y_out';
%change sampling frequency of outgoing signal
signalclass_in.fs = reference_signalclass_in.fs;
@@ -125,6 +127,9 @@ classdef EQ_silas < handle
lbdesc = ['EQ von Silas ist gelaufen '];
signalclass_in = signalclass_in.logbookentry(lbdesc);
symbols_out = signalclass_in;
symbols_out.signal = obj.d_out;
% write to output
signalclass_out = signalclass_in;
@@ -213,7 +218,7 @@ classdef EQ_silas < handle
%start the dd mode with coefficients from training
coeff = [obj.e;obj.b];
obj.e_dc = ones(obj.eq_updatelatency,1);%.*obj.e_dc;
obj.e_dc = ones(obj.eq_updatelatency,1).*obj.e_dc;
dc_block = ones(obj.eq_parallelization_blocklength,1);
for ddloop = 1:obj.ddloops
@@ -236,8 +241,9 @@ classdef EQ_silas < handle
d_feedback = zeros(obj.Cb(1),1);
d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
d_hat = zeros(obj.x_length,1);
m_reg = 0;
lvl_err = NaN(obj.x_length,numel(obj.d_constellation));
lvl_err_mov = NaN(100,numel(obj.d_constellation));
m_reg = 0;
if obj.eq_avg_blocklength > 0
averaging_window = zeros(obj.eq_avg_blocklength,1);
@@ -264,19 +270,30 @@ classdef EQ_silas < handle
x_d = [x_vnle;-d_vnle];
%Apply filter
%y(m) = (m_reg(end)*dc_cnt + obj.e_dc(end)) + x_d.'* coeff;
if obj.mu_dc_dd > 0
y(m) = obj.e_dc(end) + x_d.'* coeff;
else
y(m) = x_d.'* coeff;
end
%Decision
%Decision 1
[~,symbol_idx] = min(abs(y(m) - obj.d_constellation)); % decision for closest constellation point
d_hat(k) = obj.d_constellation(symbol_idx);
%Error between FFE & DFE filtered signal and Decision
obj.error(k) = y(m) - d_hat(k);
%
lvl_err(k,symbol_idx) = obj.error(k);
lvl_err_mov(:,symbol_idx) = circshift(lvl_err_mov(:,symbol_idx),1);
lvl_err_mov(1,symbol_idx) = obj.error(k);
%Decision 2
y(m) = y(m)-mean(lvl_err_mov(:,symbol_idx),'omitnan');
[~,symbol_idx] = min(abs(y(m) - obj.d_constellation)); % decision for closest constellation point
d_hat(k) = obj.d_constellation(symbol_idx);
%Error between FFE & DFE filtered signal and Decision
obj.error(k) = y(m) - d_hat(k);
%Update FFE and DFE coefficients
coeff = coeff - (mu_mat * (obj.error(k) * conj(x_d)));
@@ -324,7 +341,34 @@ classdef EQ_silas < handle
end
end
%%
%
% b = movmean(lvl_err,[500 500],1,"omitnan");
%
% figure(11)
% for i = 1:4
% hold on
% stem(lvl_err(:,i))
% end
%%
obj.y_out = (circshift( y.' ,-(obj.delay))).';
obj.d_out = d_hat(1:2:end);
% err = obj.error(1:2:end);
% res = NaN(8,length(err));
% for lvl = 1:8
% a = find(obj.d_out==obj.d_constellation(lvl));
% res(lvl,a) = err(a);
% end
% mean(res,2,"omitnan");
%
% figure(12)
% scatter(1:length(obj.y_out),obj.y_out,1,'.')
% hold on
% scatter(1:length(obj.d_out),obj.d_out,1,'.')
end