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DB and 400G and minor changes

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This commit is contained in:
Silas Oettinghaus
2024-10-07 08:22:28 +02:00
parent 179a7f9682
commit da3be973e2
20 changed files with 578 additions and 142 deletions
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19
Classes/04_DSP/Coding/Duobinary.m
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@@ -47,10 +47,14 @@ classdef Duobinary
% Pre coding
bk = zeros(numel(data),1);
for k = 1:numel(data)-1
bk(k+1) =mod(data(k)-bk(k),M);
end
% for k = 1:numel(data)-1
% bk(k+1) =mod(data(k)-bk(k),M);
% end
for k = 2:numel(data)
bk(k) =mod(data(k)-bk(k-1),M);
end
%make bipolar
bk = bk .* 2;
bk = bk + b;
@@ -107,9 +111,16 @@ classdef Duobinary
assert(isequal((0:M-1)',unique(data)),'Check Duobinary Precoding'); %seems the signal is not unipolar
% duobinary coding (1+D)
% coeff = [1,1];
%
% data = conv(data,coeff,"same");
coeff = [1,1];
data = conv(data,coeff,"same");
data = filter(coeff, 1, data);
%data = data ./ rms(data);
% data = ifft(fft(real(data)).*fft(fliplr(coeff'),length(data))) + 1i*ifft(fft(imag(data)).*fft(fliplr(coeff'),length(data)));
%make bipolar
data = (data-round(mean(data),1));

10
Classes/04_DSP/Equalizer/FFE.m
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@@ -51,7 +51,7 @@ classdef FFE < handle
end
function [X] = process(obj, X, D)
function [X,Noi] = process(obj, X, D)
% actual processing of the signal (steps 1. - 3.)
% 1 normalize RMS
@@ -65,10 +65,10 @@ classdef FFE < handle
obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
% Decision Directed Mode
N = X.length;
n = X.length;
training = 0;
showviz = 0;
[signal,decision]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,N,training,showviz);
[signal,decision]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
% Output Signal
if obj.decide
@@ -76,9 +76,13 @@ classdef FFE < handle
else
X.signal = signal;
end
X.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
lbdesc = [num2str(obj.order),' tap FFE'];
X = X.logbookentry(lbdesc); % append to logbook
Noi = X;
Noi = X - D;
end

68
Classes/04_DSP/Sequence Detection/MLSE.m
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@@ -54,10 +54,12 @@ classdef MLSE
obj.DIR(1:DIR_nonzero(1)-1) = [];
end
if length(obj.DIR) == 1
if isscalar(obj.DIR)
obj.DIR = [0 obj.DIR];
end
obj.DIR = flip(obj.DIR);
% RMS normalization of input data
data_in = data_in ./ rms(data_in);
@@ -95,10 +97,65 @@ classdef MLSE
% i.e. match the rms values of data_in to noise_free_received
if isreal(data_in)
if obj.M == round(obj.M)
data_in = data_in * rms(noise_free_received,'all','omitnan');
data_in = data_in * rms(noise_free_received(noise_free_received ~= inf),'all','omitnan');
end
end
%
% %% Optimized
% % Preallocate and initialize variables
% data_out = NaN(size(data_in)); % output vector
% sum_path_metrics_res = zeros(length(states), length(data_in));
% path_idx = zeros(length(states), length(data_in));
%
% % Precompute repmat size
% num_states = length(states);
% num_signals = numel(noise_free_received);
%
% % First trellis path (initialize)
% sum_path_metrics = zeros(num_states, num_states);
% path_metrics = (abs(data_in(1) - noise_free_received)).^2; % Use broadcasting instead of repmat
% sum_path_metrics = sum_path_metrics + path_metrics; % Compute initial path metrics
%
% [sum_path_metrics_res(:,1), path_idx(:,1)] = min(sum_path_metrics, [], 2); % Best path for first step
%
% % Preallocate path_metrics and sum_path_metrics to avoid reallocating in each loop
% path_metrics = zeros(num_states, num_signals);
%
% % Loop over remaining trellis paths
% for n = 2:length(data_in)
% % Avoid reallocation of sum_path_metrics, reuse the same matrix and update
% previous_sum_path_metrics = sum_path_metrics_res(:,n-1).'; % Transpose once for broadcasting
% sum_path_metrics = repmat(previous_sum_path_metrics, num_states, 1); % Avoid dynamic resizing
%
% % Calculate path metrics using broadcasting
% path_metrics = (abs(data_in(n) - noise_free_received)).^2; % Avoid repmat
%
% % Update sum path metrics
% sum_path_metrics = sum_path_metrics + path_metrics;
%
% % Find the best path for each state and store results
% [sum_path_metrics_res(:,n), path_idx(:,n)] = min(sum_path_metrics, [], 2);
% end
%
% %% Traceback
% ideal_path = NaN(1, length(data_in)+1); % Preallocate ideal path
% [~, ideal_path(length(data_in)+1)] = min(sum_path_metrics_res(:,length(data_in))); % Start from final state
%
% % Trace back through trellis
% for h = length(data_in):-1:1
% ideal_path(h) = path_idx(ideal_path(h+1),h); % Follow the best path back
% end
%
% % Extract the output indices
% idx_out = ideal_path(2:end);
% OLD
% initilaize the output vector
data_out = NaN(size(data_in));
@@ -132,12 +189,17 @@ classdef MLSE
ideal_path(h) = path_idx(ideal_path(h+1),h);
end
idx_out = ideal_path(1:length(data_in));
idx_out = ideal_path(2:length(data_in)+1);
if obj.duobinary_output
%use duobinary encoder, output is already scaled inside this
%one
data_out = Duobinary().encode(first_sym(idx_out));
else
%
data_out(1:length(data_in)) = first_sym(idx_out);