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Changes from mwork PC.

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PDP 2025

MPI analysis

new focus on database and SQL
This commit is contained in:
Silas Oettinghaus
2025-03-21 08:11:40 +01:00
parent 402e491506
commit 74066d0669
36 changed files with 2234 additions and 620 deletions
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2
.gitignore vendored
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@@ -21,3 +21,5 @@ sccprj/
# Matlab code generation folders
codegen/
.mat

19
Classes/00_signals/Signal.m
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@@ -656,7 +656,7 @@ classdef Signal
end
%%
function [obj,S,isFlipped] = tsynch(obj,options)
function [obj,S,isFlipped,sequenceFound] = tsynch(obj,options)
% time sync and cut
arguments
obj Signal
@@ -664,6 +664,14 @@ classdef Signal
options.fs_ref = 0;
options.debug_plots = 0;
end
S = {};
isFlipped=0;
sequenceFound = 0;
%normalize the signal
a = obj.normalize("mode","oneone").signal;
@@ -681,7 +689,11 @@ classdef Signal
%estimate start pos of signal
maxpeaknum = floor(length(a)/length(b));
[pks,pkpos] = findpeaks(abs(co./max(co)),'MinPeakDistance',length(b)/2,'MinPeakHeight',0.2,'NPeaks',maxpeaknum,'SortStr','descend');
[pks,pkpos,w,p] = findpeaks(abs(co./max(co)),'MinPeakDistance',length(b)/2,'MinPeakHeight',0.2,'NPeaks',maxpeaknum,'SortStr','descend');
if mean(w) > 10 || mean(p) > 10
return
end
if options.debug_plots
figure()
@@ -692,9 +704,6 @@ classdef Signal
shifts = shifts(shifts>=0);
S = {};
isFlipped=0;
if numel(shifts) > 0
%Cut occurences of ref signal from signal (only positive shifts)

215
Classes/01_transmit/PAMmapper.m
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@@ -24,7 +24,7 @@ classdef PAMmapper
obj.M = M;
obj.unipolar = unipolar;
obj.thresholds = obj.get_demodulation_thresholds();
obj.levels = obj.get_levels();
@@ -36,10 +36,10 @@ classdef PAMmapper
end
function out = map(obj,signal_in)
if isa(signal_in,'Signal')
signal_in.signal = obj.map_(signal_in.signal);
signal_in.signal = obj.map_(signal_in.signal);
% signal_in = signal_in.normalize("mode","rms");
lbdesc = ['Map bat stream to PAM ',num2str(obj.M),' symbols'];
signal_in = signal_in.logbookentry(lbdesc,obj);
@@ -47,19 +47,19 @@ classdef PAMmapper
else
out = signal_in;
end
end
function signal_out = demap(obj,signal_in)
issignalclass = 0;
if isa(signal_in,'Signal')
signalclass = signal_in;
signal_in = signal_in.signal;
issignalclass = 1;
signalclass = signal_in;
signal_in = signal_in.signal;
issignalclass = 1;
end
signal_out = obj.demap_(signal_in);
signal_out = obj.demap_(signal_in);
if issignalclass
lbdesc = ['Demap PAM ',num2str(obj.M),' symbols to bit stream'];
@@ -68,7 +68,7 @@ classdef PAMmapper
signal_in.signal = signal_out;
signal_out = signal_in;
end
end
function pam_sig = map_(obj,bitpattern)
@@ -89,7 +89,7 @@ classdef PAMmapper
% 4-ASK:
if ~obj.eth_style
pam_sig=2*bitpattern(:,1)+(bitpattern(:,1)==bitpattern(:,2));
if obj.unipolar==0
pam_sig=2*pam_sig-3;
end
@@ -100,15 +100,65 @@ classdef PAMmapper
pam_sig = pam_sig/sqrt(5);
case 6
m = 1;
if size(bitpattern,2)>size(bitpattern,1)
bitpattern = bitpattern'; %vector aufrecht stellen
end
% LUT based mapping
for k = 1:5:fix(length(bitpattern)/5)*5
pam_sig(m:m+1,1) = obj.thresholds(bin2dec(int2str(bitpattern(k:k+4)'))+1,:);
m = m+2;
if ~obj.eth_style
m = 1;
if size(bitpattern,2)>size(bitpattern,1)
bitpattern = bitpattern'; %vector aufrecht stellen
end
% LUT based mapping
for k = 1:5:fix(length(bitpattern)/5)*5
pam_sig(m:m+1,1) = obj.thresholds(bin2dec(int2str(bitpattern(k:k+4)'))+1,:);
m = m+2;
end
else
bitsPerSymbol = reshape(bitpattern,5,[]).'; % reorder 5 bits per symbol
normFactor = 1;
%====================32 QAM===================%
%=============================================%
% Coding %
% 01000 01001 |11001 11000 %
% | %
% 01010 01110 01100 |11100 11110 11010 %
% | %
% 01011 01111 01101 |11101 11111 11011 %
% --------------------|------------------- %
% 00011 00111 00101 |10101 10111 10011 %
% | %
% 00010 00110 00100 |10100 10110 10010 %
% | %
% 00000 00001 |10001 10000 %
%=============================================%
% modulate three LSB first in first Quadrant
% first bit inverts real part if 0
% second bit inverts imaginary part if 0
LSB_symbols = normFactor*(...
+(1+1i) *(bitsPerSymbol(:,3)==1 & bitsPerSymbol(:,4)==0 & bitsPerSymbol(:,5)==1)...
+(3+1i) *(bitsPerSymbol(:,3)==1 & bitsPerSymbol(:,4)==1 & bitsPerSymbol(:,5)==1)...
+(5+1i) *(bitsPerSymbol(:,3)==0 & bitsPerSymbol(:,4)==1 & bitsPerSymbol(:,5)==1)...
+(1+3i) *(bitsPerSymbol(:,3)==1 & bitsPerSymbol(:,4)==0 & bitsPerSymbol(:,5)==0)...
+(3+3i) *(bitsPerSymbol(:,3)==1 & bitsPerSymbol(:,4)==1 & bitsPerSymbol(:,5)==0)...
+(5+3i) *(bitsPerSymbol(:,3)==0 & bitsPerSymbol(:,4)==1 & bitsPerSymbol(:,5)==0)...
+(1+5i) *(bitsPerSymbol(:,3)==0 & bitsPerSymbol(:,4)==0 & bitsPerSymbol(:,5)==1)...
+(3+5i) *(bitsPerSymbol(:,3)==0 & bitsPerSymbol(:,4)==0 & bitsPerSymbol(:,5)==0));
Re = real(LSB_symbols);
Im = imag(LSB_symbols);
% if first bit== 0 => invert real part
% if second bit== 0 => invert imag part
modData2 = Re.*(bitsPerSymbol(:,1)*2-1) + 1i*Im.*(bitsPerSymbol(:,2)*2-1);
Re = real(modData2(1:end/2));
Im = imag(modData2(1:end/2));
pam_sig = zeros(length(Re)*2,1);
pam_sig(1:2:length(Re)*2) = Re;
pam_sig(2:2:length(Im)*2) = Im;
end
pam_sig = pam_sig/sqrt(10);
@@ -120,9 +170,9 @@ classdef PAMmapper
x1 = bitpattern(:,1);
x2 = (bitpattern(:,1)==bitpattern(:,3));
x3 = x2~=bitpattern(:,2);
pam_sig = 4*x1 + 2*x2 + x3;
if obj.unipolar==0
pam_sig=2*pam_sig-7;
end
@@ -133,7 +183,7 @@ classdef PAMmapper
end
pam_sig = pam_sig/sqrt(21);
case 16
% 16-ASK:
x1 = bitpattern(:,1);
@@ -167,7 +217,7 @@ classdef PAMmapper
% case 16
% thres = [-10 -8 -6 -4 -2 0 2 4 6 8 10];
% end
switch obj.M
@@ -190,12 +240,12 @@ classdef PAMmapper
thres = thres .* 1/sqrt(5);
case 6 %PAM 6
case 6 %PAM 6
thres = [-3 5;-1 5;-3 -5;-1 -5;-5 3;-5 1;-5 -3;-5 -1;-1 3;-1 1;-1 -3;-1 -1;-3 3;-3 1;-3 -3;-3 -1;3 5;1 5;3 -5;1 -5;5 3;5 1;5 -3;5 -1;1 3;1 1;1 -3;1 -1;3 3;3 1;3 -3;3 -1];
% thres = [-4 -2 0 2 4];
% thres = thres ./ sqrt(10);
case 8
% 8-ASK
if obj.unipolar==0
@@ -203,7 +253,7 @@ classdef PAMmapper
elseif obj.unipolar==1
thres=0.5:6.5;
end
thres=thres./sqrt(21);
case 16
@@ -234,23 +284,26 @@ classdef PAMmapper
end
function [data_out] = demap_(obj,data_in)
data_in= data_in';
if obj.M ~= 6
% create output
if ~isempty(obj.thresholds)
a = squeeze(repmat(real(data_in),[1 1 length(obj.thresholds)])); %Eingangssignal in 3 spalten
b = squeeze(repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in) 1])); %Threshold in 3 Spalten
comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
comp_real=repmat(real(data_in),[1 1 length(obj.thresholds)]) > repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in) 1]);
else
comp_real=[];
end
s1=size(comp_real,1);
s2=size(comp_real,2);
end
switch obj.M
@@ -273,34 +326,70 @@ classdef PAMmapper
case 6
data_in = data_in/(sqrt(mean(abs(data_in).^2)));
data_in = data_in*sqrt(10);
if ~obj.eth_style
data_in = data_in/(sqrt(mean(abs(data_in).^2)));
data_in = data_in*sqrt(10);
if size(data_in,2) > 1
data_in = data_in.';
end
if length(data_in)/2 ~= round(length(data_in)/2)
data_in = [data_in;0];
end
m = 1;
for n = 1:2:length(data_in)
dist = sqrt((data_in(n)-obj.thresholds(:,1)).^2+(data_in(n+1)-obj.thresholds(:,2)).^2);
[~,dd_idx] = min(dist);
% dec_out(n:n+1) = LUT(dd_idx,:);
data_out(m:m+4) = bitget(dd_idx-1,5:-1:1);
m = m+5;
end
else
data_in= data_in';
rxSym = data_in(1:2:end) + 1i*data_in(2:2:end); % 16×1
% Decode the sign bits (bits 1 & 2).
rxBit1 = double(real(rxSym) > 0);
rxBit2 = double(imag(rxSym) > 0);
% Undo the quadrant inversion and normalization.
rxSym_corr = abs(real(rxSym)) + 1i*abs(imag(rxSym));
normFactor = 1/sqrt(10);
rxSym_unscaled = rxSym_corr / normFactor;
cand = [1+1i, 3+1i, 5+1i, 1+3i, 3+3i, 5+3i, 1+5i, 3+5i];
candBits = [1 0 1;
1 1 1;
0 1 1;
1 0 0;
1 1 0;
0 1 0;
0 0 1;
0 0 0];
d = abs(rxSym_unscaled - cand).^2; % 32×8 distances
[~, idx] = min(d, [], 2);
rxLSB = candBits(idx,:); % 32×3
decodedSymbols = [rxBit1, rxBit2, rxLSB];
data_out = reshape(decodedSymbols.', [], 1).';
if size(data_in,2) > 1
data_in = data_in.';
end
if length(data_in)/2 ~= round(length(data_in)/2)
data_in = [data_in;0];
end
m = 1;
for n = 1:2:length(data_in)
dist = sqrt((data_in(n)-obj.thresholds(:,1)).^2+(data_in(n+1)-obj.thresholds(:,2)).^2);
[~,dd_idx] = min(dist);
% dec_out(n:n+1) = LUT(dd_idx,:);
data_out(m:m+4) = bitget(dd_idx-1,5:-1:1);
m = m+5;
end
case 8
% 8-ASK
if ~obj.eth_style
data_out=[comp_real(:,:,4);
comp_real(:,:,1)-comp_real(:,:,3)+comp_real(:,:,5)-comp_real(:,:,7);
1-comp_real(:,:,2)+comp_real(:,:,6)];
data_out=[comp_real(:,:,4);
comp_real(:,:,1)-comp_real(:,:,3)+comp_real(:,:,5)-comp_real(:,:,7);
1-comp_real(:,:,2)+comp_real(:,:,6)];
else
data_out = [(data_in>=0); (abs(data_in)>(4/sqrt(21))); (abs(data_in)>=(2/sqrt(21)))&(abs(data_in)<=(6/sqrt(21)))];
end
@@ -324,21 +413,21 @@ classdef PAMmapper
data_in Signal
options.symbol_levels = []
end
%A) normally return the preproduct of the decision
a = squeeze(repmat(real(data_in.signal),[1 1 length(obj.thresholds)])); %Eingangssignal in 3 spalten
b = squeeze(repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in.signal) 1])); %Threshold in 3 Spalten
comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
data_out = data_in;
data_out.signal = sum(comp_real,2);
%Option: return the actual level values/ just map onto given
%symbol levels
if ~isempty(options.symbol_levels)
data_out.signal = options.symbol_levels(data_out.signal+1);
end
end
function [out] = separate_pamlevels(obj,data_in)
@@ -347,7 +436,7 @@ classdef PAMmapper
a = squeeze(repmat(real(data_in.signal),[1 1 length(obj.thresholds)])); %Eingangssignal in 3 spalten
b = squeeze(repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in.signal) 1])); %Threshold in 3 Spalten
comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
comp_real_sum = sum(comp_real,2);
comp_real_sum = sum(comp_real,2);
out = NaN(length(data_in),length(obj.thresholds)+1);
@@ -358,7 +447,7 @@ classdef PAMmapper
end
function [Signal_out] = quantize(obj,Signal_in)
constellation = obj.get_levels();
constellation = constellation ./ rms(constellation);
@@ -366,9 +455,9 @@ classdef PAMmapper
if isa(Signal_in,'Signal')
issignalclass = 1;
Sig_class = Signal_in;
Signal_in = Signal_in.signal;
Signal_in = Signal_in.signal;
end
[~,high_dim_sig] = max(size(Signal_in));
[~,high_dim_const] = max(size(constellation));
@@ -381,7 +470,7 @@ classdef PAMmapper
Signal_out = constellation(symbol_idx);
Signal_out = reshape(Signal_out,size(Signal_in));
if issignalclass
if issignalclass
Sig_class.signal = Signal_out;
Signal_out = Sig_class;
end
@@ -390,6 +479,10 @@ classdef PAMmapper
end
function bitmap = showBitMapping(obj)
bitmap = obj.demap([obj.levels ./ obj.scaling]');
end
end
end

43
Classes/01_transmit/Pulseformer.m
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@@ -6,8 +6,10 @@ classdef Pulseformer
end
properties(Access=public)
fdac
fs
fsym
matched_sps
output_sps
pulse
pulselength
alpha
@@ -20,8 +22,10 @@ classdef Pulseformer
% Detailed explanation goes here
arguments
options.fdac double
options.fs double
options.fsym double
options.matched_sps double
options.output_sps double
options.pulse pulseform = pulseform.rc
options.pulselength double {mustBeInteger} = 32
options.alpha double = 0.05
@@ -50,7 +54,11 @@ classdef Pulseformer
signalclass_in = signalclass_in.logbookentry(lbdesc);
% write fs to signal
signalclass_in.fs = obj.fdac;%.* (obj.fdac./obj.fsym);
if obj.matched
signalclass_in.fs = obj.fsym .* obj.output_sps;%.* (obj.fdac./obj.fsym);
else
signalclass_in.fs = obj.fs;%.* (obj.fdac./obj.fsym);
end
% write to output
signalclass_out = signalclass_in;
@@ -77,15 +85,19 @@ classdef Pulseformer
data_out
end
if ~rem(obj.fdac,obj.fsym)
if ~isempty(obj.output_sps)
obj.fsym = obj.fsym.*obj.output_sps;
end
if ~rem(obj.fs,obj.fsym)
%ist ein Vielfaches
sps = obj.fdac / obj.fsym;
sps = obj.fs / obj.fsym;
p = sps;
q = 1;
else
%ist kein Vielfaches
p = obj.fsym / gcd(obj.fdac, obj.fsym); %upsampling p->->->
q = obj.fdac/ gcd(obj.fdac, obj.fsym); %downsampling <-q
p = obj.fsym / gcd(obj.fs, obj.fsym); %upsampling p->->->
q = obj.fs/ gcd(obj.fs, obj.fsym); %downsampling <-q
sps= q; %sps während dem pulse shaping
end
@@ -105,6 +117,7 @@ classdef Pulseformer
end
manual_cyclic_convolution = 0;
upsample_filter = 0;
upfirdn_convolution = 1;
if manual_cyclic_convolution
@@ -122,16 +135,28 @@ classdef Pulseformer
data_out=ifft( fft(symbolov.') .* repmat( H,size(data_in,1),1 ).' ).';
data_out = circshift(data_out,[0 -(obj.pulselength*sps)]);
if rem(obj.fdac,obj.fsym)
if rem(obj.fs,obj.fsym)
data_out = data_out(1:q:end);
end
end
if upsample_filter
data_out_ = upsample(data_in,p);
mfOutput = filter(h, 1, data_out_); % Matched filter output
figure()
hold on
stem(mfOutput(1:1000),'Marker','o','MarkerSize',1,'LineStyle','-','LineWidth',1);
stem(data_out_(1:1000),'Marker','o','MarkerSize',1,'LineStyle','-','LineWidth',1);
end
if upfirdn_convolution
%Apply Filter using Matlab build in fctn.
data_out_ = upfirdn(data_in,h,p,q);
%cut signal, which is longer due to fir filter

14
Classes/04_DSP/Equalizer/FFE.m
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@@ -101,6 +101,17 @@ classdef FFE < handle
end
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
if training
mask = ones(obj.order,1);
else
mask = zeros(obj.order,1);
mask(900:end) = 1;
mask(ceil(length(obj.e)/2)) = 1;
end
mask = ones(obj.order,1);
for epoch = 1 : epochs
symbol = 0;
@@ -110,7 +121,7 @@ classdef FFE < handle
U = x(obj.order+sample-1:-1:sample);
y(symbol,1) = obj.e.' * U; % Calculating output of LMS __ * |
y(symbol,1) = (obj.e.*mask).' * U; % Calculating output of LMS __ * |
if training
d_hat(symbol,1) = d(symbol);
@@ -129,7 +140,6 @@ classdef FFE < handle
normalizationfactor = (U.' * U);
obj.e = obj.e - err(symbol) * U / normalizationfactor; % Weight update rule of NLMS
end
obj.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error

4
Classes/04_DSP/Equalizer/Postfilter.m
View File

@@ -43,12 +43,12 @@ classdef Postfilter < handle
if ~isnan(options.useBurg) && options.useBurg
disp('using burg alg')
% disp('using burg alg')
obj.coefficients = arburg(noiseclass_in.signal,obj.ncoeff);
elseif ~isempty(options.coefficients)
disp('using given taps')
% disp('using given taps')
obj.coefficients = options.coefficients;
obj.useBurg = 0;

52
Classes/04_DSP/Sequence Detection/MLSE.m
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@@ -70,7 +70,7 @@ classdef MLSE < handle
%%%% Separate the equalized signal into the respective levels based on the actually transmitted level
constellation = unique(data_ref);
decisionLevels = (constellation(1:end-1) + constellation(2:end)) / 2;
tx_bits = PAMmapper(numel(constellation),0).demap(data_ref);
tx_bits = PAMmapper(numel(constellation),0,"eth_style",1).demap(data_ref);
% impulse respnse i.e. [0.5, 1.0000]
@@ -188,10 +188,50 @@ classdef MLSE < handle
end
% Compute soft output PAM4 stream from the metric_sym
soft_output = zeros(length(data_in),1); % Expected symbol value per stage
symbol_prob = zeros(length(data_in), length(states)); % Store full probability distribution (optional)
llp = llp';
for n = 1:length(data_in)
metrics = llp(n, :); % A posteriori metric for each PAM4 candidate
% For numerical stability, subtract the maximum metric before exponentiating
maxMetric = min(metrics);
expMetrics = exp(metrics - maxMetric);
probs = expMetrics / sum(expMetrics); % Normalize to get probabilities
symbol_prob(n, :) = probs; % (Optional) store distribution for analysis
% Compute the soft output as the expected value of the PAM4 symbols
soft_output(n) = sum(probs .* constellation');
end
% Number of symbols and bits per symbol
num_symbols = constellation;
num_bits = 2; % 2 bits per symbol
bit_mapping = PAMmapper(4,0,"eth_style",1).showBitMapping; % Each row corresponds to the symbol above
% Initialize LLR storage
llr = zeros(num_bits, length(data_in));
% Compute bit-wise LLRs
for bit_idx = 1:num_bits
% Find indices where bit is 0 and where it is 1
idx_bit_0 = find(bit_mapping(:,bit_idx) == 0);
idx_bit_1 = find(bit_mapping(:,bit_idx) == 1);
% Sum over log-probabilities (Max-Log approximation: using min instead of sum)
llr(:,bit_idx) = min(llp(:,idx_bit_1), [], 1) - min(llp(:,idx_bit_0), [], 1);
end
% Convert LLR values to a hard-decision bit stream
bit_stream = llr < 0;
[~,~,ber_llr,~] = calc_ber(bit_stream',tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
fprintf('LLR BER : %.2e \n',ber_llr);
% directly decide based on lowest LLP index
[~,llp_based_state_seq]=min(llp);
LLP_EST(1:length(data_in)) = constellation(llp_based_state_seq);
rx_bits = PAMmapper(numel(constellation),0).demap(LLP_EST');
rx_bits = PAMmapper(numel(constellation),0,"eth_style",1).demap(LLP_EST');
[~,~,ber_llp,~] = calc_ber(rx_bits,tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
fprintf('LLP BER : %.2e \n',ber_llp);
% [~,~,ber_llp,~] = calc_ber(circshift(rx_bits,1),tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
@@ -200,7 +240,7 @@ classdef MLSE < handle
% fprintf('LLP BER -1: %.2e \n',ber_llp);
%%%% DECIDE based on Viterbi traceback
rx_bits = PAMmapper(numel(constellation),0).demap(VITERBI_ESTIMATION_SYMBOLS');
rx_bits = PAMmapper(numel(constellation),0,"eth_style",1).demap(VITERBI_ESTIMATION_SYMBOLS');
[~,~,ber_viterbi,~] = calc_ber(rx_bits,tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
fprintf('Viterbi BER: %.2e \n',ber_viterbi);
% [~,~,ber_viterbi,~] = calc_ber(circshift(rx_bits,1),tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
@@ -209,14 +249,14 @@ classdef MLSE < handle
% directly decide based on the FW path metrics
[~,fw_direct_state_seq]=min(pm_survivor_fw);
FW_EST(1:length(data_in)) = constellation(fw_direct_state_seq);
rx_bits = PAMmapper(numel(constellation),0).demap(FW_EST');
rx_bits = PAMmapper(numel(constellation),0,"eth_style",1).demap(FW_EST');
[~,~,ber_fw,~] = calc_ber(rx_bits,tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
fprintf('FW BER: %.2e \n',ber_fw);
% directly decide based on the BW path metrics
[~,bw_direct_state_seq]=min(pm_survivor_bw);
BW_EST(1:length(data_in)) = constellation(bw_direct_state_seq(2:end));
rx_bits = PAMmapper(numel(constellation),0).demap(BW_EST');
rx_bits = PAMmapper(numel(constellation),0,"eth_style",1).demap(BW_EST');
[~,~,ber_bw,~] = calc_ber(rx_bits,tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
fprintf('BW BER: %.2e \n',ber_bw);
% [~,~,ber_viterbi,~] = calc_ber(circshift(rx_bits,1),tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
@@ -224,6 +264,8 @@ classdef MLSE < handle
% [~,~,ber_viterbi,~] = calc_ber(circshift(rx_bits,-1),tx_bits,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
% fprintf('BW BER: %.2e \n',ber_viterbi);
PAMmapper(4,0,"eth_style",1).showBitMapping
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tx_symbolpos = zeros(numel(constellation),length(data_ref));

23
Classes/04_DSP/TransmissionPerformance.m
View File

@@ -70,7 +70,7 @@ classdef TransmissionPerformance
CODE_RATE_HDFEC = [1/(1+0.067)]; %Beyond 300 Gbps Short-Reach Links Using TFLN MZMs With 500 mVpp and Linear Equalization
BERTHRESHOLDS_HDFEC = 3.8e-3;
CODE_RATE_O_FEC = [1/(1+0.15)]; %Stefano im Meeting
CODE_RATE_O_FEC = [1/(1+0.153)]; %Stefano im Meeting
BERTHRESHOLDS_O_FEC = 2e-2;
@@ -162,6 +162,11 @@ classdef TransmissionPerformance
netrates.KP4_hamming.NetRate = NaN(1, numMeasurements);
netrates.KP4_hamming.CodeRate = NaN(1, numMeasurements);
netrates.KP4_hamming.Threshold = NaN(1, numMeasurements);
netrates.O_FEC.GrossRate = NaN(1, numMeasurements);
netrates.O_FEC.NetRate = NaN(1, numMeasurements);
netrates.O_FEC.CodeRate = NaN(1, numMeasurements);
netrates.O_FEC.Threshold = NaN(1, numMeasurements);
end
% Process each measurement individually.
@@ -215,6 +220,22 @@ classdef TransmissionPerformance
netrates.KP4_hamming.Threshold(i) = obj.BERTHRESHOLDS_KP4_AND_INNER(idxBER);
end
idxBER = [];
for j = length(obj.BERTHRESHOLDS_O_FEC):-1:1
if ber(i) <= obj.BERTHRESHOLDS_O_FEC(j)
idxBER = j;
break;
end
end
if ~isempty(idxBER)
codeRate = obj.CODE_RATE_O_FEC(idxBER);
netrates.O_FEC.NetRate(i) = grossRate(i) * codeRate;
netrates.O_FEC.GrossRate(i) = grossRate(i) ;
netrates.O_FEC.CodeRate(i) = codeRate;
netrates.O_FEC.Threshold(i) = obj.BERTHRESHOLDS_O_FEC(idxBER);
end
end

616
Classes/DataBaseHandler/DBHandler.m
View File

@@ -45,15 +45,15 @@ classdef DBHandler < handle
else
error('DB seems to be corrupt')
end
end
function obj = refresh(obj)
% Get table names and the first rows of each table to understand the structure
obj.getTableNames();
obj.getTables();
obj.getDistinctValues();
% Get table names and the first rows of each table to understand the structure
obj.getTableNames();
obj.getTables();
obj.getDistinctValues();
end
function obj = getTableNames(obj)
@@ -187,10 +187,11 @@ classdef DBHandler < handle
if isstruct(newRow)
fields = fieldnames(newRow);
emptyFields = structfun(@isempty,newRow);
if sum(emptyFields)>0
newRow.(fields{emptyFields==1}) = NaN;
disp(['In Table: ',tableName,': ',fields{emptyFields==1},' was empty, is now NaN ',newRow.(fields{emptyFields==1})])
if sum(emptyFields) > 0
emptyFieldNames = fields(emptyFields); % use () to get a cell array
for idx = 1:numel(emptyFieldNames)
newRow.(emptyFieldNames{idx}) = NaN;
end
end
newRow = struct2table(newRow);
end
@@ -274,87 +275,168 @@ classdef DBHandler < handle
end
end
function addBEREntry(obj, berValue, occurrence, runID, ffe, dfe, mlse, pf, eqType, ffe_order, dfe_order, len_tr, mu_ffe, mu_dfe, mu_dc, comment)
% addBEREntry Adds a BER entry linked to an existing or new Equalizer entry.
% Usage:
% addBEREntry(runID, eq, ffe, dfe, mlse, pf, eqType, ffe_order, dfe_order, len_tr, mu_ffe, mu_dfe, mu_dc, berValue, comment)
if isempty(pf)
postfilter_taps = [];
function resultID = addProcessingResult(obj, run_id, resultData, eqParamsData)
% addProcessingResult Adds a processing result and links it to an EqualizerParameters entry.
%
% Inputs:
% run_id: A run_id from the main table to connect the BER with.
%
% resultData: A struct with fields corresponding to the ProcessingResults table.
%
% eqParamsData: A struct with fields corresponding to the EqualizerParameters table,
% except 'eq_id' and 'config_hash'. These fields are used to compute
% a hash and check for an existing configuration.
%
% Output:
% resultID: The result_id of the newly inserted ProcessingResults entry.
% 1. Compute hash for equalizer parameters
jsonStr = jsonencode(eqParamsData);
md = java.security.MessageDigest.getInstance('MD5');
md.update(uint8(jsonStr));
hashBytes = typecast(md.digest, 'uint8');
hashStr = lower(dec2hex(hashBytes)');
hashStr = lower(strtrim(hashStr(:)')); % Convert to a lowercase string
% Add hash to equalizer parameters
eqParamsData.config_hash = hashStr;
% 2. Check if an equalizer configuration with the same hash exists
queryStr = sprintf('SELECT eq_id FROM EqualizerParameters WHERE config_hash = ''%s''', eqParamsData.config_hash);
existingEntry = obj.fetch(queryStr);
if ~isempty(existingEntry)
% Use existing eq_id
eq_id = existingEntry{1,1};
else
postfilter_taps = pf.burg_coeff;
% Insert the new equalizer configuration and get its eq_id
eq_id = obj.appendToTable('EqualizerParameters', eqParamsData);
end
% Create equalizer data struct for searching and adding if necessary
equalizerData = struct( ...
'ffe', jsonencode(ffe), ...
'dfe', jsonencode(dfe), ...
'mlse', jsonencode(mlse), ...
'pf', jsonencode(pf), ...
'eq_type', string(eqType), ...
'ffe_order', jsonencode(ffe_order), ...
'dfe_order', jsonencode(dfe_order), ...
'postfilter_taps',jsonencode(postfilter_taps),...
'len_tr', len_tr, ...
'mu_ffe', jsonencode(mu_ffe), ...
'mu_dfe', mu_dfe, ...
'mu_dc', mu_dc, ...
'comment', comment ...
);
% 3. Add the equalizer configuration reference and run_id to resultData
resultData.eqParam_id = eq_id;
resultData.run_id = run_id;
% Check if exact Equalizer and BER entries already exist in the DB ...
selectedFields = {'Runs.run_id','BERs.ber_id','Equalizer.eq_id','BERs.ber',['BERs.occurrence' ...
'']};
filterParams = obj.tables;
filterParams.Equalizer = equalizerData;
[dataTable,sql_query] = obj.queryDB(filterParams, selectedFields);
% 4. Compute hash for the processing result
tempResultData = rmfield(resultData, 'date_of_processing');
resultJsonStr = jsonencode(tempResultData);
md2 = java.security.MessageDigest.getInstance('MD5'); % Create a new MD5 instance
md2.update(uint8(resultJsonStr));
resultHashBytes = typecast(md2.digest, 'uint8');
resultHashStr = lower(dec2hex(resultHashBytes)');
resultHashStr = lower(strtrim(resultHashStr(:)')); % Convert to a lowercase string
% get or insert Equalizer
if ~isempty(dataTable)
% Equalizer entry already exists, use the existing eq_id
cur_eq_id = dataTable.eq_id;
else
% Insert the new Equalizer entry
cur_eq_id = obj.appendToTable('Equalizer', equalizerData);
% Add the result hash to resultData
resultData.result_hash = resultHashStr;
% 5. Check if an identical processing result already exists
queryStr2 = sprintf('SELECT result_id FROM Results WHERE result_hash = ''%s''', resultData.result_hash);
existingResult = obj.fetch(queryStr2);
if ~isempty(existingResult)
% If the result exists, return its result_id without inserting a new row
resultID = existingResult{1,1};
warning(['Result already exists: ResultID: ',num2str(resultID),'| EQ ID: ',num2str(eq_id),' Run ID: ' num2str(run_id)])
return;
end
% skip if already here or insert BER entry
if ~isempty(dataTable)
% 6. Insert the processing result
resultID = obj.appendToTable('Results', resultData);
end
% A BER entry with the same eq_id, run_id, and occurrence already exists
existingBERValue = dataTable.ber;
function recalcHashes(obj)
% recalcHashes Recalculate hashes for all rows in the Results and EqualizerParameters tables.
%
% For EqualizerParameters, the hash is computed from all fields except
% 'eq_id' and 'config_hash'.
%
% For Results, the hash is computed from all fields except 'result_id',
% 'result_hash', and 'date_of_processing'.
% Compare the existing BER value with the new BER value
if existingBERValue == berValue
fprintf('The BER entry %.2e || -- eq_id: %d -- run_id: %d -- occurrence: %d already exists. \n',berValue, cur_eq_id, runID, occurrence);
else
fprintf('Already found BER for EQ: %.2e ~= %.2e || -- eq_id: %d -- run_id: %d -- occurrence: %d already exists.\n', berValue, existingBERValue, cur_eq_id, runID, occurrence);
% Recalculate hashes for EqualizerParameters
eqParamsRows = obj.fetch('SELECT * FROM EqualizerParameters');
for i = 1:height(eqParamsRows)
rowStruct = table2struct(eqParamsRows(i,:)); % Convert the table row to a struct
% Remove fields not part of the hash computation
if isfield(rowStruct, 'eq_id')
rowStruct = rmfield(rowStruct, 'eq_id');
end
if isfield(rowStruct, 'config_hash')
rowStruct = rmfield(rowStruct, 'config_hash');
end
else
% No such BER entry exists, insert the new BER entry
berData = struct( ...
'run_id', runID, ...
'eq_id', cur_eq_id, ...
'ber', berValue, ...
'occurrence', occurrence ...
);
obj.appendToTable('BERs', berData);
% Compute MD5 hash from the JSON representation
jsonStr = jsonencode(rowStruct);
md = java.security.MessageDigest.getInstance('MD5');
md.update(uint8(jsonStr));
hashBytes = typecast(md.digest, 'uint8');
hashStr = lower(dec2hex(hashBytes)');
hashStr = lower(strtrim(hashStr(:)'));
% Update the config_hash field using the eq_id from the table row
eq_id = eqParamsRows.eq_id(i);
updateQuery = sprintf('UPDATE EqualizerParameters SET config_hash = ''%s'' WHERE eq_id = %d', hashStr, eq_id);
obj.executeSQL(updateQuery);
end
% Fetch all rows from the Results table using queryDB with no filters
Results = obj.tables.Results;
if isstruct(Results)
newFields = {};
fNames = fieldnames(Results);
for i = 1:numel(fNames)
newFields{end+1} = ['Results.' fNames{i}];
end
Results = newFields;
end
[resultsRows, ~] = obj.queryDB(obj.tables, Results);
for i = 1:height(resultsRows)
rowStruct = table2struct(resultsRows(i, :)); % Convert the row to a struct
% Remove fields not used in the hash calculation
if isfield(rowStruct, 'result_id')
rowStruct = rmfield(rowStruct, 'result_id');
end
if isfield(rowStruct, 'result_hash')
rowStruct = rmfield(rowStruct, 'result_hash');
end
if isfield(rowStruct, 'date_of_processing')
rowStruct = rmfield(rowStruct, 'date_of_processing');
end
% Compute MD5 hash from the JSON representation
jsonStr = jsonencode(rowStruct);
md = java.security.MessageDigest.getInstance('MD5');
md.update(uint8(jsonStr));
hashBytes = typecast(md.digest, 'uint8');
hashStr = lower(dec2hex(hashBytes)');
hashStr = lower(strtrim(hashStr(:)'));
% Access the result_id from the table row and update the hash
result_id = resultsRows.result_id(i);
updateQuery = sprintf('UPDATE Results SET result_hash = ''%s'' WHERE result_id = %d', ...
hashStr, result_id);
obj.executeSQL(updateQuery);
end
end
function executeSQL(obj, query)
% This method executes an SQL statement using MATLAB's execute function.
execute(obj.conn, query);
end
function answer = fetch(obj,query)
answer = fetch(obj.conn,query);
end
@@ -405,15 +487,46 @@ classdef DBHandler < handle
function query = constructSQLQuery(obj, filterParams, selectedFields)
% constructSQLQuery Constructs the SQL query based on filter parameters and selected fields.
%
% If selectedFields is provided as a struct, it is converted to a cell array.
% The conversion takes the field names and creates entries like:
% {'selectedFields.fieldName'} for each field.
% Construct the SELECT clause dynamically based on user selection
% Input check for selectedFields: if it's a struct, convert it to a cell array.
if isstruct(selectedFields)
newFields = {};
tableNames = fieldnames(selectedFields);
for t = 1:numel(tableNames)
tableStruct = selectedFields.(tableNames{t});
fieldNames = fieldnames(tableStruct);
for f = 1:numel(fieldNames)
if isequal(tableStruct.(fieldNames{f}), 1)
newFields{end+1} = sprintf('%s.%s', tableNames{t}, fieldNames{f});
end
end
end
selectedFields = newFields;
end
% Construct the SELECT clause dynamically based on user selection.
% (Assuming that when provided as a cell array, each entry is of the form
% 'TableName.fieldName' or, in our conversion case, 'selectedFields.fieldName'.)
selectClause = 'SELECT DISTINCT ';
for i = 1:numel(selectedFields)
fieldParts = strsplit(selectedFields{i}, '.');
tableName = fieldParts{1};
fieldName = fieldParts{2};
% If the field comes from the struct conversion, its first part is 'selectedFields'
% and the actual field name is in the second part.
if strcmp(fieldParts{1}, 'selectedFields')
tableName = fieldParts{1}; % not used for type checking below
fieldName = fieldParts{2};
else
tableName = fieldParts{1};
fieldName = fieldParts{2};
end
if isnumeric(obj.tables.(tableName).(fieldName))
% Decide on COALESCE depending on the field type.
% If the table is known in obj.tables and the field is numeric, use 'NaN'.
if isfield(obj.tables, tableName) && isfield(obj.tables.(tableName), fieldName) && isnumeric(obj.tables.(tableName).(fieldName))
selectClause = [selectClause, 'COALESCE(', selectedFields{i}, ', ''NaN'') AS ', fieldName];
else
selectClause = [selectClause, 'COALESCE(', selectedFields{i}, ', '''') AS ', fieldName];
@@ -426,235 +539,302 @@ classdef DBHandler < handle
end
end
% Construct the FROM and WHERE clause
baseQuery = [selectClause, 'FROM Runs ' ...
'LEFT JOIN Configurations ON Runs.run_id = Configurations.run_id ' ...
'LEFT JOIN Measurements ON Runs.run_id = Measurements.run_id ' ...
'WHERE '];
% 'LEFT JOIN BERs ON Runs.run_id = BERs.run_id ' ...
% 'LEFT JOIN Equalizer ON BERs.eq_id = Equalizer.eq_id ' ...
% --- Adaptive FROM Clause ---
% Use "Runs" as the main table and add LEFT JOINs for every other table in obj.tables
% (except "sqlite_sequence") that has a run_id field.
mainTable = 'Runs';
fromClause = ['FROM ', mainTable, ' '];
tableNamesAll = fieldnames(obj.tables);
for t = 1:numel(tableNamesAll)
tableName = tableNamesAll{t};
if strcmpi(tableName, mainTable) || strcmpi(tableName, 'sqlite_sequence')
continue;
end
% Loop through each table in filterParams
if isfield(obj.tables.(tableName), 'run_id')
% most tables are directly linked to runs table
fromClause = [fromClause, 'LEFT JOIN ', tableName, ' ON ', mainTable, '.run_id = ', tableName, '.run_id '];
elseif isfield(obj.tables.(tableName), 'eq_id')
% equalizer is only linked to results table
fromClause = [fromClause, 'LEFT JOIN ', tableName, ' ON ', 'Results', '.eqParam_id = ', tableName, '.eq_id '];
end
end
% --- WHERE Clause Construction ---
baseQuery = [selectClause, ' ', fromClause, 'WHERE '];
filterClauses = [];
tableNames_ = fieldnames(filterParams);
for t = 1:numel(tableNames_)
tableName = tableNames_{t};
tableParams = filterParams.(tableName);
% Loop through each parameter in the table
fieldNames = fieldnames(tableParams);
for i = 1:numel(fieldNames)
fieldName = fieldNames{i};
value = tableParams.(fieldName);
% Construct the full column name in the format "tableName.fieldName"
fullName = sprintf('%s.%s', tableName, fieldName);
% Handle different types of values for SQL query construction
% Handle various types of values for SQL query construction
if isempty(value)
% Skip this parameter if it is empty (include all values)
continue;
elseif isnumeric(value) && isnan(value)
% If value is NaN, use IS NULL in SQL
filterClause = sprintf('%s IS NULL', fullName);
elseif isnumeric(value) && ~isEnumeration(value)
filterClause = sprintf('%s = %f', fullName, value);
elseif islogical(value) || (isnumeric(value) && ismember(value, [0, 1])) && ~isEnumeration(value)
filterClause = sprintf('%s = %d', fullName, value);
elseif ischar(value) || isstring(value)
filterClause = sprintf('%s = ''%s''', fullName, char(value)); %nicht nach string suchen sondern nach chararray -> 'bla' statt "bla"
filterClause = sprintf('%s = ''%s''', fullName, char(value));
elseif isEnumeration(value)
filterClause = sprintf('%s = ''%s''', fullName, value);
else
error('Unsupported data type for field "%s".', fullName);
end
% Add the constructed filter clause to the list
filterClauses = [filterClauses, filterClause, ' AND '];
end
end
% Remove trailing ' AND ' from the filter clauses if any filters were added
% Remove trailing ' AND ' if any filters were added.
if ~isempty(filterClauses)
filterClauses = filterClauses(1:end-5);
end
% Construct the final SQL query
if isempty(filterClauses)
query = [selectClause, 'FROM Runs ' ...
'LEFT JOIN Configurations ON Runs.run_id = Configurations.run_id ' ...
'LEFT JOIN Measurements ON Runs.run_id = Measurements.run_id ' ...
'LEFT JOIN BERs ON Runs.run_id = BERs.run_id'];
query = [selectClause, ' ', fromClause, 'WHERE ', filterClauses];
else
query = [baseQuery, filterClauses];
query = [selectClause, ' ', fromClause];
end
end
function selectedFields = promptSelectFields(obj)
% promptSelectFields Prompts the user to select fields from multiple tables to include in the SELECT statement using settingsdlg.
% promptSelectFields Prompts the user to select fields from multiple tables
% using a custom checkbox GUI with scrolling.
%
% The function builds a list of all fields (formatted as 'TableName.fieldName')
% and displays each as a checkbox inside an inner container panel. The container's
% height is set to accommodate all checkboxes, so the scrollable panel shows scrollbars.
% When the user clicks the "Select" button, the selected fields are returned.
% If none are selected, all fields are returned.
% Get all possible fields from all tables (excluding sqlite_sequence)
% Get all possible tables (excluding sqlite_sequence)
tableNames = fieldnames(obj.tables);
tableNames = setdiff(tableNames, {'sqlite_sequence'}); % Remove sqlite_sequence
% Prepare the inputs for settingsdlg
promptSettings = {};
allFieldsFullName = {};
convertedFieldNames = {};
tableNames = setdiff(tableNames, {'sqlite_sequence'});
% Build a single cell array of all field names with table prefix.
allFields = {};
for i = 1:numel(tableNames)
tableFields = fieldnames(obj.tables.(tableNames{i}));
for j = 1:numel(tableFields)
fieldName = tableFields{j};
fullName = sprintf('%s.%s', tableNames{i}, fieldName);
convertedName = strrep(fullName, '.', '_'); % Replace '.' with '_'
allFieldsFullName{end + 1} = fullName; % Add full name to the list
convertedFieldNames{end + 1} = convertedName; % Store the converted name
% Add the field name and checkbox setting to the prompt
promptSettings{end + 1} = {sprintf('Include %s', fullName), convertedName};
promptSettings{end + 1} = false; % Default: not selected
fields = fieldnames(obj.tables.(tableNames{i}));
for j = 1:numel(fields)
allFields{end+1} = sprintf('%s.%s', tableNames{i}, fields{j});
end
end
numFields = numel(allFields);
% Create the settings dialog
[settings, button] = settingsdlg(...
'title', 'Select Fields for the SQL Query', ...
'description', 'Check the boxes for the fields you want to include in the SELECT statement.', ...
promptSettings{:} ...
);
% Create the main figure.
fig = uifigure('Name', 'Select Fields', 'Position', [100, 100, 400, 600]);
% If the user cancels, default to selecting all fields
if strcmp(button, 'cancel')
selectedFields = allFieldsFullName;
return;
% Create a scrollable panel.
scrollPanel = uipanel(fig, 'Position', [10, 60, 380, 530], 'Scrollable', 'on');
% Define checkbox dimensions.
checkboxHeight = 30;
spacing = 5;
totalHeight = numFields * (checkboxHeight + spacing);
% Create an inner container panel with height larger than the scrollPanel's height.
container = uipanel(scrollPanel, 'Position', [0, 0, scrollPanel.Position(3), totalHeight]);
% Create checkboxes using absolute positioning in the container.
checkboxes = gobjects(numFields, 1);
for i = 1:numFields
% Calculate the vertical position.
% The origin (0,0) is at the bottom left of the container.
yPos = totalHeight - i*(checkboxHeight + spacing) + spacing;
checkboxes(i) = uicheckbox(container, ...
'Text', allFields{i}, ...
'Value', false, ...
'Position', [10, yPos, container.Position(3)-20, checkboxHeight]);
end
% Parse user input into selectedFields
% Create a "Select" button in the main figure.
btn = uibutton(fig, 'Text', 'Select', ...
'Position', [150, 10, 100, 30], ...
'ButtonPushedFcn', @(btn, event) uiresume(fig));
% Wait for the user to click the button.
uiwait(fig);
% Retrieve the selected fields.
selectedFields = {};
for i = 1:numel(allFieldsFullName)
convertedName = convertedFieldNames{i};
if isfield(settings, convertedName) && settings.(convertedName) % Add to selectedFields if the checkbox was selected
selectedFields{end + 1} = allFieldsFullName{i}; %#ok<AGROW>
for i = 1:numFields
if checkboxes(i).Value
selectedFields{end+1} = checkboxes(i).Text;
end
end
% If no fields are selected, default to selecting all fields
% If no fields are selected, default to all fields.
if isempty(selectedFields)
selectedFields = allFieldsFullName;
selectedFields = allFields;
end
% Close the figure.
delete(fig);
end
function filterParams = promptFilterParameters(obj)
% promptFilterParameters Prompts the user to enter filter parameters using the settingsdlg framework.
% promptFilterParameters Prompts the user to enter filter parameters using a
% custom scrollable UI with dropdowns.
%
% For each table (excluding 'sqlite_sequence'), each field that has distinct
% values is displayed as a label and a dropdown. The dropdown items are built
% from the distinct values (with "All" prepended). The output is a struct where,
% for each table, each field is set to the chosen value (or [] if "All" is selected).
% Get all possible parameters from all tables (excluding sqlite_sequence)
tableNames_ = fieldnames(obj.tables);
tableNames_ = setdiff(tableNames_, {'sqlite_sequence'}); % Remove sqlite_sequence
% Get all tables except 'sqlite_sequence'
tableNames = fieldnames(obj.tables);
tableNames = setdiff(tableNames, {'sqlite_sequence'});
% Prepare the inputs for settingsdlg with sections and separators
promptSettings = {};
allFieldsFullName = {};
convertedFieldNames = {};
% Precompute layout constants.
heightPerTableLabel = 30;
heightPerField = 40; % vertical space for a field (label + dropdown)
spacing = 5;
for i = 1:numel(tableNames_)
% Add a separator for each table section
promptSettings{end + 1} = 'separator';
promptSettings{end + 1} = tableNames_{i};
% Get all fields from the current table
tableFields = fieldnames(obj.tables.(tableNames_{i}));
% Prepare each field to be added to the dialog
% Compute total required height.
totalHeight = 0;
for i = 1:numel(tableNames)
totalHeight = totalHeight + heightPerTableLabel;
tableName = tableNames{i};
tableFields = fieldnames(obj.tables.(tableName));
for j = 1:numel(tableFields)
fieldName = tableFields{j};
fullName = sprintf('%s.%s', tableNames_{i}, fieldName);
convertedName = strrep(fullName, '.', '_'); % Replace '.' with '_'
% Skip fields that do not have distinct values stored
if ~isfield(obj.distinctValues.(tableNames_{i}), fieldName)
continue;
% Only include fields that have distinct values stored.
if isfield(obj.distinctValues.(tableName), fieldName)
totalHeight = totalHeight + heightPerField;
end
% Get the distinct values for the field
distinctValues_ = obj.distinctValues.(tableNames_{i}).(fieldName);
% Prepare distinct values for dropdown
if isempty(distinctValues_)
% If there are no distinct values, use only an "All" entry
distinctValues_ = {'All'};
else
% Ensure distinctValues is a cell array of strings
if isnumeric(distinctValues_)
distinctValues_ = arrayfun(@(x) num2str(x), distinctValues_, 'UniformOutput', false);
elseif isstring(distinctValues_)
distinctValues_ = cellstr(distinctValues_);
elseif iscell(distinctValues_) && ~iscellstr(distinctValues_)
distinctValues_ = cellfun(@num2str, distinctValues_, 'UniformOutput', false);
end
% Add an "All" option at the beginning of the distinct values list
distinctValues_ = [{'All'}; distinctValues_];
end
allFieldsFullName{end + 1} = fullName; % Add full name to the list
convertedFieldNames{end + 1} = convertedName; % Store the converted name
% Add the field name and value setting to the prompt
promptSettings{end + 1} = {sprintf('%s', fullName), convertedName};
promptSettings{end + 1} = distinctValues_; % Add distinct values as dropdown options
end
end
% Create the settings dialog
[settings, button] = settingsdlg(...
'title', 'Input Parameters for Filtering', ...
'description', 'Enter the values for each field to filter. Select "All" to include all values.', ...
promptSettings{:} ...
);
% Create the main UI figure.
fig = uifigure('Name', 'Input Parameters for Filtering', 'Position', [100, 100, 500, 600]);
% If the user cancels, return an empty struct
if strcmp(button, 'cancel')
% Set a CloseRequestFcn so that closing the figure calls uiresume.
fig.CloseRequestFcn = @(src, event) uiresume(src);
% Create a scrollable panel inside the figure.
scrollPanel = uipanel(fig, 'Position', [10, 60, 480, 530], 'Scrollable', 'on');
% Create an inner container panel with a height set to totalHeight.
container = uipanel(scrollPanel, 'Position', [0, 0, scrollPanel.Position(3), totalHeight]);
% Prepare cell arrays to store dropdown handles and corresponding table/field names.
dropdownHandles = {};
dropdownTableNames = {};
dropdownFieldNames = {};
% Set the starting Y coordinate (filling from top to bottom).
currentY = totalHeight;
% Maximum number of dropdown items.
maxItems = 100;
for i = 1:numel(tableNames)
% Create a label for the table name.
uilabel(container, ...
'Text', tableNames{i}, ...
'FontWeight', 'bold', ...
'Position', [10, currentY - heightPerTableLabel + spacing, 200, heightPerTableLabel - spacing]);
currentY = currentY - heightPerTableLabel;
tableName = tableNames{i};
tableFields = fieldnames(obj.tables.(tableName));
for j = 1:numel(tableFields)
fieldName = tableFields{j};
if ~isfield(obj.distinctValues.(tableName), fieldName)
continue; % Skip if no distinct values are stored.
end
% Retrieve distinct values for the field.
distinctValues_ = obj.distinctValues.(tableName).(fieldName);
if ~isempty(distinctValues_) && numel(distinctValues_) > maxItems
distinctValues_ = distinctValues_(1:maxItems);
end
if isempty(distinctValues_)
items = {'All'};
else
if isnumeric(distinctValues_)
items = cellfun(@num2str, num2cell(distinctValues_), 'UniformOutput', false);
elseif isstring(distinctValues_)
items = cellstr(distinctValues_);
elseif iscell(distinctValues_) && ~iscellstr(distinctValues_)
items = cellfun(@num2str, distinctValues_, 'UniformOutput', false);
else
items = distinctValues_;
end
items = items(:)'; % Ensure row vector
items = [{'All'}, items];
end
% Create a label for the field.
uilabel(container, ...
'Text', sprintf('%s:', fieldName), ...
'HorizontalAlignment', 'right', ...
'Position', [10, currentY - 25, 150, 25]);
% Create a dropdown for the field.
dd = uidropdown(container, ...
'Items', items, ...
'Value', 'All', ...
'Position', [170, currentY - 25, 200, 25]);
% Store the dropdown handle and its associated table/field.
dropdownHandles{end+1} = dd;
dropdownTableNames{end+1} = tableName;
dropdownFieldNames{end+1} = fieldName;
currentY = currentY - heightPerField;
end
end
% Create a "Submit" button at the bottom of the figure.
btn = uibutton(fig, 'Text', 'Submit', ...
'Position', [200, 10, 100, 30], ...
'ButtonPushedFcn', @(btn, event) uiresume(fig));
% Wait until the user clicks "Submit" or closes the figure.
uiwait(fig);
% If the figure was closed (and thus no longer valid), return an empty struct.
if ~isvalid(fig)
filterParams = struct();
return;
end
% Parse user input into filterParams structure
% Build the filterParams struct from the dropdown selections.
filterParams = struct();
for i = 1:numel(allFieldsFullName)
value = settings.(convertedFieldNames{i});
% Split full name to get table and field names
fieldParts = strsplit(allFieldsFullName{i}, '.');
tableName = fieldParts{1};
fieldName = fieldParts{2};
% If the table does not exist in the filterParams struct, create it
for k = 1:numel(dropdownHandles)
tableName = dropdownTableNames{k};
fieldName = dropdownFieldNames{k};
value = dropdownHandles{k}.Value;
if ~isfield(filterParams, tableName)
filterParams.(tableName) = struct();
end
% Assign values to the respective fields under each table
% If "All" is selected, assign empty; otherwise, try converting to numeric.
if strcmp(value, 'All')
filterParams.(tableName).(fieldName) = []; % Set to empty to include all values
elseif isnumeric(value) && isnan(value)
filterParams.(tableName).(fieldName) = NaN; % Use NaN to handle as NULL
filterParams.(tableName).(fieldName) = [];
else
filterParams.(tableName).(fieldName) = value; % Use the entered value
numValue = str2double(value);
if ~isnan(numValue)
filterParams.(tableName).(fieldName) = numValue;
else
filterParams.(tableName).(fieldName) = value;
end
end
end
% Close the figure.
delete(fig);
end
end
end

23
Classes/Moveit_wrapper.m
View File

@@ -39,17 +39,22 @@ classdef Moveit_wrapper < handle
end
function signalclass_out = process(obj,signal_in)
function signal_out = process(obj,signal_in)
arguments
obj
signal_in = []
end
isSignalClass = 0;
if isa(signal_in,"Signal")
signalclass_out = signal_in;
signal_out = signal_in;
signal_in = signal_in.signal;
isSignalClass = 1;
end
input_len = length(signal_in);
signal_in = signal_in.';
% signal_in = signal_in.';
% INIT MOVEIT
global loop;
@@ -59,18 +64,14 @@ classdef Moveit_wrapper < handle
% RUN MOVEIT
global loop;
loop = 1;
signal_out = obj.moveit_init(signal_out);
% CHECK IF SIGNAL CHANGED
if input_len ~= length(signal_out)
warning(['Signal length changed in moveit function: ', obj.moveit_function_name]);
end
signal_out = obj.moveit_init(signal_out);
if isSignalClass
% append to logbook
lbdesc = ['Logbookentry'];
signalclass_out = signalclass_out.logbookentry(lbdesc);
signalclass_out.signal = signal_out;
signal_out = signal_out.logbookentry(lbdesc);
signal_out.signal = signal_out;
end
end

5
Datatypes/equalizer_structure.m
View File

@@ -4,8 +4,9 @@ classdef equalizer_structure < int32
ffe (0)
vnle (1)
vnle_pf_mlse (2)
db_precoded (3)
db_encoded (4)
% db_precoded (3)
% db_encoded (4)
vnle_db_mlse (3)
end
end

141
Functions/EQ_structures/duobinary_target.m
View File

@@ -1,36 +1,42 @@
function [eq_package] = duobinary_target(eq_, mlse_,M, rx_signal, tx_symbols, tx_bits, options)
arguments
eq_
mlse_
M
rx_signal
tx_symbols
tx_bits
eq_
mlse_
M
rx_signal
tx_symbols
tx_bits
options.precode_mode db_mode
options.showAnalysis = 0;
options.eth_style_symbol_mapping = 0;
options.postFFE = [];
end
%Duobinary Targeting
[eq_signal, eq_noise] = eq_.process(rx_signal,Duobinary().encode(tx_symbols));
%Duobinary Targeting
db_ref_sequence = Duobinary().encode(tx_symbols);
db_ref_constellation = unique(db_ref_sequence.signal);
[eq_signal, eq_noise] = eq_.process(rx_signal,db_ref_sequence);
% dir = [1,1];
mlse_sig_sd = mlse_.process(eq_signal);
if ~isempty(options.postFFE)
[eq_signal,eq_noise] = options.postFFE.process(eq_signal,db_ref_sequence);
end
mlse_sig_hd = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).quantize(mlse_sig_sd);
% precoding to mitigate error propagation, most prominently used in
% combination with duobinary signaling to avoid catastrophic error
% behavior (see J.W.M. Bergmans, Digital Baseband Transmission and Recording -> partial response signaling)
% dir = [1,1];
mlse_sig_sd = mlse_.process(eq_signal);
% takes:
% -> eq_signal_hd: hard decision signal after eq
% -> tx_symbols: that where used as reference for eq
mlse_sig_hd = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).quantize(mlse_sig_sd);
switch options.precode_mode
case db_mode.db_emulate
% precoding to mitigate error propagation, most prominently used in
% combination with duobinary signaling to avoid catastrophic error
% behavior (see J.W.M. Bergmans, Digital Baseband Transmission and Recording -> partial response signaling)
% takes:
% -> eq_signal_hd: hard decision signal after eq
% -> tx_symbols: that where used as reference for eq
switch options.precode_mode
case db_mode.db_emulate
mlse_sig_hd = Duobinary().encode(mlse_sig_hd,"M",M);
mlse_sig_hd = Duobinary().decode(mlse_sig_hd,"M",M);
@@ -40,43 +46,102 @@ end
tx_bits = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(tx_symbols_precoded);
case db_mode.db_discard
case db_mode.db_discard
% normal dsp for precoded sequence == discard/omit/ignore precode
tx_bits = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(tx_symbols);
case db_mode.db_encoded
case db_mode.db_encoded
% normal DB encoded data (only for 10KM)
case db_mode.db_precoded
case db_mode.db_precoded
mlse_sig_hd = Duobinary().encode(mlse_sig_hd,"M",M);
mlse_sig_hd = Duobinary().decode(mlse_sig_hd,"M",M);
end
end
% M = numel(unique(tx_symbols.signal));
rx_bits = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(mlse_sig_hd);
% M = numel(unique(tx_symbols.signal));
rx_bits = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(mlse_sig_hd);
[~,numErrors,ber,~] = calc_ber(rx_bits.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
[bits_db,errors_db,ber_db,errorIndice_db] = calc_ber(rx_bits.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
eq_package.ber = ber_db;
eq_package.ber = ber;
resultsDBtgt = struct( ...
'result_id', NaN, ... %
'run_id', NaN, ... % Beispielhafte Run-ID
'eqParam_id', NaN, ... % Beispielhafter Fremdschlüssel zur EqualizerParameters-Tabelle
'date_of_processing', datetime('now'), ... % Aktuelles Datum und Uhrzeit
'numBits', bits_db, ... % Beispiel: 1.000.000 Bits
'numBitErr', errors_db, ... % Beispiel: 120 Bitfehler
'BER', ber_db, ... % BER = 120 / 1.000.000
'SNR', [], ... % Beispielhafte SNR
'SNR_level', jsonencode([]), ... % SNR-Level als JSON-codiertes Array
'GMI', [], ... % Beispielhafter GMI-Wert
'AIR', [], ... % Beispielhafter AIR-Wert
'EVM', [], ... % Beispielhafte EVM
'EVM_level', jsonencode([]), ... % EVM-Level als JSON-codiertes Array
'Alpha', [] ... % Beispielhafter Alpha-Wert
);
if options.showAnalysis
eq_noise = eq_noise - mean(eq_noise.signal);
if ~isempty(options.postFFE)
npostFFE = options.postFFE.order;
else
npostFFE = 0;
end
rx_signal.spectrum("normalizeTo0dB",1,"fignum",250,"displayname","Rx Spectrum");
equalizerConfigDBtgt = struct( ...
'eq_id', NaN, ... % Auto-Inkrement, wird in der DB gesetzt
'equalizer_structure', int32(equalizer_structure.vnle_db_mlse), ... % Beispiel: 1 (z.B. für vnle)
'M', M, ... % Ordnung der PAM-Konstellation
'target_constellation', jsonencode(round(db_ref_constellation,5)), ... % Beispielhafter Target-String
'db_target', 1, ... % 0 oder 1
'diff_precode', int32(options.precode_mode), ... % 0 oder 1
'postFFE', ~isempty(options.postFFE), ... % Beispielwert
'NpostFFE', npostFFE, ... % Beispielwert
'Ne1', eq_.Ne(1), ... % Feedforward Koeffizienten 1. Ordnung
'Ne2', eq_.Ne(2), ... % Feedforward Koeffizienten 2. Ordnung
'Ne3', eq_.Ne(3), ... % Feedforward Koeffizienten 3. Ordnung
'Nb1', eq_.Nb(1), ... % Decision Feedback Koeffizienten 1. Ordnung
'Nb2', eq_.Nb(2), ... % Decision Feedback Koeffizienten 2. Ordnung
'Nb3', eq_.Nb(3), ... % Decision Feedback Koeffizienten 3. Ordnung
'K', eq_.K, ... % Samples pro Symbol
'DCmu', eq_.DCmu, ... % Anpassungsrate für DC-Tap
'ideal_dfe', eq_.ideal_dfe, ... % Flag für ideal DFE (0 oder 1)
'training_length', eq_.training_length, ... % Anzahl Trainingssymbole
'training_loops', eq_.training_loops, ... % Anzahl Trainingsdurchläufe
'TRmu1', eq_.FFEmu, ... % mu für DD-Modus (1. Ordnung)
'TRmu2', eq_.FFEmu, ... % mu für DD-Modus (2. Ordnung)
'TRmu3', eq_.FFEmu, ... % mu für DD-Modus (3. Ordnung)
'TRmuDFE', eq_.DFEmu, ... % mu für DFE-Modus im DD
'dd_loops', 5, ... % Anzahl Durchläufe im DD-Modus
'DDmu1', eq_.DDmu(1), ... % mu für DD-Modus (1. Ordnung)
'DDmu2', eq_.DDmu(2), ... % mu für DD-Modus (2. Ordnung)
'DDmu3', eq_.DDmu(3), ... % mu für DD-Modus (3. Ordnung)
'DDmuDFE', eq_.DDmu(4), ... % mu für DFE-Modus im DD
'MLSE_mode', 'viterbi', ... % Beispiel: MLSE-Modus als String
'MLSE_trellis_states', jsonencode(mlse_.trellis_states), ... % Trellis-States, z.B. als JSON-String oder kommasepariert
'comment', 'function: duobinary_target.m', ... % Zusätzliche Kommentare
'config_hash', NaN ...
);
Duobinary().encode(tx_symbols).spectrum("normalizeTo0dB",1,"fignum",250,"displayname","DB encoded reference");
eq_package.resultsDBtgt = resultsDBtgt;
eq_package.equalizerConfigDBtgt = equalizerConfigDBtgt;
showEQNoisePSD(eq_noise,"fignum",250,"displayname",'Duobinary Target Noise after Equalization');
if options.showAnalysis
eq_noise = eq_noise - mean(eq_noise.signal);
fprintf('DB tgt BER: %.2e \n',ber);
rx_signal.spectrum("normalizeTo0dB",1,"fignum",250,"displayname","Rx Spectrum");
end
Duobinary().encode(tx_symbols).spectrum("normalizeTo0dB",1,"fignum",250,"displayname","DB encoded reference");
showEQNoisePSD(eq_noise,"fignum",250,"displayname",'Duobinary Target Noise after Equalization');
fprintf('DB tgt BER: %.2e \n',ber);
end
end

30
Functions/EQ_structures/vnle.m
View File

@@ -23,6 +23,7 @@ function [eq_package] = vnle(eq_,M,rx_signal,tx_symbols,tx_bits,options)
options.precode_mode db_mode
options.showAnalysis = 0
options.eth_style = 0;
options.postFFE = [];
end
%FFE or VNLE
@@ -32,6 +33,10 @@ function [eq_package] = vnle(eq_,M,rx_signal,tx_symbols,tx_bits,options)
end
[eq_signal_sd,eq_noise] = eq_.process(rx_signal,tx_symbols);
if ~isempty(options.postFFE)
[eq_signal_sd,eq_noise] = options.postFFE.process(eq_signal_sd,tx_symbols);
end
eq_signal_hd = PAMmapper(M,0).quantize(eq_signal_sd);
% precoding to mitigate error propagation, most prominently used in
@@ -89,15 +94,34 @@ function [eq_package] = vnle(eq_,M,rx_signal,tx_symbols,tx_bits,options)
fprintf('SNR: %d dB \n',snr(eq_signal_sd.signal,eq_noise.signal));
if M == 6
logm = 2.5;
else
logm = log2(M);
end
fprintf('NGMI: %.4f \n', inf_rate/logm);
fprintf(['VNLE EVM lvl: ',repmat('%.3f ',1,numel(evm_lvl)),' \n'],evm_lvl);
fprintf('VNLE BER: %.2e \n',ber);
showEQcoefficients(eq_.e,eq_.e2,eq_.e3,"displayname",'Coefficients');
disp("%%%%%%%%%%%%%%%%%%%%%")
showEQNoiseSNR(tx_symbols,eq_noise,"displayname",'SNR after VNLE','fignum',301);
% showEQcoefficients('n1',eq_.e,'n2',eq_.e2,'n3',eq_.e3,"displayname",'Coefficients');
%
% if ~isempty(options.postFFE)
% showEQcoefficients('n1',options.postFFE.e,"displayname",'Coefficients');
% end
%
% showEQNoisePSD(eq_noise);
%
% showEQfilter(eq_.e,eq_signal_sd.fs.*2)
% noiselessness(tx_symbols,eq_noise,"displayname",'SNR after VNLE','fignum',301);
% showLevelHistogram(eq_signal_sd,tx_symbols,"fignum",302);
showLevelHistogram(eq_signal_sd,tx_symbols,"fignum",302);
end

144
Functions/EQ_structures/vnle_postfilter_mlse.m
View File

@@ -11,11 +11,17 @@ arguments
options.precode_mode db_mode
options.showAnalysis = 0;
options.eth_style_symbol_mapping = 0;
options.postFFE = [];
options.database = [];
end
%FFE or VNLE
[eq_signal_sd,eq_noise] = eq_.process(rx_signal,tx_symbols);
if ~isempty(options.postFFE)
[eq_signal_sd,eq_noise] = options.postFFE.process(eq_signal_sd,tx_symbols);
end
eq_signal_hd = PAMmapper(M,0).quantize(eq_signal_sd);
mlse_sig_sd = pf_.process(eq_signal_sd,eq_noise);
@@ -70,28 +76,152 @@ end
% METRICS OF VNLE %
rx_bits_vnle = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(eq_signal_hd);
[~,~,ber_vnle,~] = calc_ber(rx_bits_vnle.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
[bits_vnle,errors_vnle,ber_vnle,errorIndice_vnle] = calc_ber(rx_bits_vnle.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
[snr_vnle,snr_vnle_lvl] = calc_snr(tx_symbols.signal,eq_noise.signal);
% correct TUM implementation of AIR
[inf_rate_vnle] = calc_air(eq_signal_sd,tx_symbols,"skip_front",10000,"skip_end",10000);
[gmi_vnle] = calc_air(eq_signal_sd,tx_symbols,"skip_front",10000,"skip_end",10000);
gmi_vnle = min(max(abs(gmi_vnle),0.1),0); %set to zero if no convergence of gmi below 0.1 to avoid negative or any other "dumb" value
air_vnle = tx_symbols.fs .* floor(log2(8)*10)/10 .* gmi_vnle;
[evm_vnle_total,evm_vnle_lvl] = calc_evm(eq_signal_sd,tx_symbols);
% METRICS OF MLSE (HD-VITERBI)
rx_bits_mlse = PAMmapper(M,0,"eth_style",options.eth_style_symbol_mapping).demap(mlse_sig_hd);
[~,~,ber_mlse,~] = calc_ber(rx_bits_mlse.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
[bits_mlse,errors_mlse,ber_mlse,errorIndice_mlse]= calc_ber(rx_bits_mlse.signal,tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
pf_.ncoeff = 1;
pf_.process(eq_signal_sd,eq_noise);
alpha = pf_.coefficients(2);
eq_package.ber_mlse = ber_mlse;
eq_package.ber_vnle = ber_vnle;
eq_package.evm_vnle_total = evm_vnle_total;
eq_package.evm_vnle_lvl = evm_vnle_lvl;
eq_package.air = inf_rate_vnle;
eq_package.gmi = gmi_vnle;
eq_package.eq = eq_;
eq_package.pf = pf_;
eq_package.mlse = mlse_;
resultsVNLE = struct( ...
'result_id', NaN, ... %
'run_id', NaN, ... % Beispielhafte Run-ID
'eqParam_id', NaN, ... % Beispielhafter Fremdschlüssel zur EqualizerParameters-Tabelle
'date_of_processing', datetime('now'), ... % Aktuelles Datum und Uhrzeit
'numBits', bits_vnle, ... % Beispiel: 1.000.000 Bits
'numBitErr', errors_vnle, ... % Beispiel: 120 Bitfehler
'BER', ber_vnle, ... % BER = 120 / 1.000.000
'SNR', snr_vnle, ... % Beispielhafte SNR
'SNR_level', jsonencode(snr_vnle_lvl), ... % SNR-Level als JSON-codiertes Array
'GMI', gmi_vnle, ... % Beispielhafter GMI-Wert
'AIR', air_vnle, ... % Beispielhafter AIR-Wert
'EVM', evm_vnle_total, ... % Beispielhafte EVM
'EVM_level', jsonencode(evm_vnle_lvl), ... % EVM-Level als JSON-codiertes Array
'Alpha', [] ... % Beispielhafter Alpha-Wert
);
if ~isempty(options.postFFE)
npostFFE = options.postFFE.order;
else
npostFFE = 0;
end
equalizerConfigVNLE = struct( ...
'eq_id', NaN, ... % Auto-Inkrement, wird in der DB gesetzt
'equalizer_structure', int32(equalizer_structure.vnle), ... % Beispiel: 1 (z.B. für vnle)
'M', M, ... % Ordnung der PAM-Konstellation
'target_constellation', jsonencode(round(unique(tx_symbols.signal),5)), ... % Beispielhafter Target-String
'db_target', 0, ... % 0 oder 1
'diff_precode', int32(options.precode_mode), ... % 0 oder 1
'postFFE', ~isempty(options.postFFE), ... % Beispielwert
'NpostFFE', npostFFE, ... % Beispielwert
'Ne1', eq_.Ne(1), ... % Feedforward Koeffizienten 1. Ordnung
'Ne2', eq_.Ne(2), ... % Feedforward Koeffizienten 2. Ordnung
'Ne3', eq_.Ne(3), ... % Feedforward Koeffizienten 3. Ordnung
'Nb1', eq_.Nb(1), ... % Decision Feedback Koeffizienten 1. Ordnung
'Nb2', eq_.Nb(2), ... % Decision Feedback Koeffizienten 2. Ordnung
'Nb3', eq_.Nb(3), ... % Decision Feedback Koeffizienten 3. Ordnung
'K', eq_.K, ... % Samples pro Symbol
'DCmu', eq_.DCmu, ... % Anpassungsrate für DC-Tap
'ideal_dfe', eq_.ideal_dfe, ... % Flag für ideal DFE (0 oder 1)
'training_length', eq_.training_length, ... % Anzahl Trainingssymbole
'training_loops', eq_.training_loops, ... % Anzahl Trainingsdurchläufe
'TRmu1', eq_.FFEmu, ... % mu für DD-Modus (1. Ordnung)
'TRmu2', eq_.FFEmu, ... % mu für DD-Modus (2. Ordnung)
'TRmu3', eq_.FFEmu, ... % mu für DD-Modus (3. Ordnung)
'TRmuDFE', eq_.DFEmu, ... % mu für DFE-Modus im DD
'dd_loops', 5, ... % Anzahl Durchläufe im DD-Modus
'DDmu1', eq_.DDmu(1), ... % mu für DD-Modus (1. Ordnung)
'DDmu2', eq_.DDmu(2), ... % mu für DD-Modus (2. Ordnung)
'DDmu3', eq_.DDmu(3), ... % mu für DD-Modus (3. Ordnung)
'DDmuDFE', eq_.DDmu(4), ... % mu für DFE-Modus im DD
'comment', 'function: vnle_postfilter_mlse', ... % Zusätzliche Kommentare
'config_hash', NaN ...
);
resultsMLSE = struct( ...
'result_id', NaN, ... %
'run_id', NaN, ... % Beispielhafte Run-ID
'eqParam_id', NaN, ... % Beispielhafter Fremdschlüssel zur EqualizerParameters-Tabelle
'date_of_processing', datetime('now'), ... % Aktuelles Datum und Uhrzeit
'numBits', bits_mlse, ... % Beispiel: 1.000.000 Bits
'numBitErr', errors_mlse, ... % Beispiel: 120 Bitfehler
'BER', ber_mlse, ... % BER = 120 / 1.000.000
'SNR', [], ... % Beispielhafte SNR
'SNR_level', jsonencode([]), ... % SNR-Level als JSON-codiertes Array
'GMI', [], ... % Beispielhafter GMI-Wert
'AIR', [], ... % Beispielhafter AIR-Wert
'EVM', [], ... % Beispielhafte EVM
'EVM_level', jsonencode([]), ... % EVM-Level als JSON-codiertes Array
'Alpha', alpha, ... % Beispielhafter Alpha-Wert
'MLSE_dir', jsonencode([mlse_.DIR])...
);
equalizerConfigMLSE = struct( ...
'eq_id', NaN, ... % Auto-Inkrement, wird in der DB gesetzt
'equalizer_structure', int32(equalizer_structure.vnle_pf_mlse), ... % Beispiel: 1 (z.B. für vnle)
'M', M, ... % Ordnung der PAM-Konstellation
'target_constellation', jsonencode(round(unique(tx_symbols.signal),5)), ... % Beispielhafter Target-String
'db_target', 0, ... % 0 oder 1
'diff_precode', int32(options.precode_mode), ... % 0 oder 1
'postFFE', ~isempty(options.postFFE), ... % Beispielwert
'NpostFFE', npostFFE, ... % Beispielwert
'Ne1', eq_.Ne(1), ... % Feedforward Koeffizienten 1. Ordnung
'Ne2', eq_.Ne(2), ... % Feedforward Koeffizienten 2. Ordnung
'Ne3', eq_.Ne(3), ... % Feedforward Koeffizienten 3. Ordnung
'Nb1', eq_.Nb(1), ... % Decision Feedback Koeffizienten 1. Ordnung
'Nb2', eq_.Nb(2), ... % Decision Feedback Koeffizienten 2. Ordnung
'Nb3', eq_.Nb(3), ... % Decision Feedback Koeffizienten 3. Ordnung
'K', eq_.K, ... % Samples pro Symbol
'DCmu', eq_.DCmu, ... % Anpassungsrate für DC-Tap
'ideal_dfe', eq_.ideal_dfe, ... % Flag für ideal DFE (0 oder 1)
'training_length', eq_.training_length, ... % Anzahl Trainingssymbole
'training_loops', eq_.training_loops, ... % Anzahl Trainingsdurchläufe
'TRmu1', eq_.FFEmu, ... % mu für DD-Modus (1. Ordnung)
'TRmu2', eq_.FFEmu, ... % mu für DD-Modus (2. Ordnung)
'TRmu3', eq_.FFEmu, ... % mu für DD-Modus (3. Ordnung)
'TRmuDFE', eq_.DFEmu, ... % mu für DFE-Modus im DD
'dd_loops', 5, ... % Anzahl Durchläufe im DD-Modus
'DDmu1', eq_.DDmu(1), ... % mu für DD-Modus (1. Ordnung)
'DDmu2', eq_.DDmu(2), ... % mu für DD-Modus (2. Ordnung)
'DDmu3', eq_.DDmu(3), ... % mu für DD-Modus (3. Ordnung)
'DDmuDFE', eq_.DDmu(4), ... % mu für DFE-Modus im DD
'MLSE_mode', 'viterbi', ... % Beispiel: MLSE-Modus als String
'MLSE_trellis_states', jsonencode(mlse_.trellis_states), ... % Trellis-States, z.B. als JSON-String oder kommasepariert
'comment', 'function: vnle_postfilter_mlse', ... % Zusätzliche Kommentare
'config_hash', NaN ...
);
eq_package.resultsVNLE = resultsVNLE;
eq_package.resultsMLSE = resultsMLSE;
eq_package.equalizerConfigVNLE = equalizerConfigVNLE;
eq_package.equalizerConfigMLSE = equalizerConfigMLSE;
% eq_package.vnle_out = eq_signal_sd;
if options.showAnalysis
% fprintf(['VNLE EVM lvl: ',repmat('%.3f ',1,numel(evm_lvl)),' \n'],evm_lvl);
@@ -110,13 +240,13 @@ end
showLevelHistogram(eq_signal_sd,tx_symbols)
% showLevelHistogram(mlse_sig_sd,tx_symbols)
showEQcoefficients(eq_.e,eq_.e2,eq_.e3,"displayname",'Coefficients');
showEQcoefficients('n1',eq_.e,'n2',eq_.e2,'n3',eq_.e3,"displayname",'Coefficients');
showEQNoiseSNR(tx_symbols,eq_noise,"displayname",'vnle snr','fignum',101);
%%% EQ SNR Spectrum %230
%snr
snr_vnle = snr(tx_symbols.signal,eq_noise.signal);
% showErrorBurstCount(eq_signal_sd,tx_symbols)

77
Functions/EQ_visuals/showEQcoefficients.m
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@@ -1,58 +1,67 @@
function showEQcoefficients(n1, n2, n3, options)
% Show filter coefficients as stem plot
% n1, n2, and n3 in different subplots
% Scale all y-axis to -1 and 1
function showEQcoefficients(options)
% Show filter coefficients as stem plots.
% Only the provided coefficient arrays (n1, n2, n3) are shown,
% each in its own subplot. The y-axis is scaled to [-1, 1].
arguments
n1
n2
n3
options.fignum (1,1) double = NaN % Default to NaN if not provided
options.displayname (1,:) char = '' % Default to an empty string if not provided
options.n1 = [];
options.n2 = [];
options.n3 = [];
options.fignum (1,1) double = NaN; % Default: create new figure if NaN
options.displayname (1,:) char = ''; % Default: empty string
options.color = [0.2157, 0.4941, 0.7216];
options.clf = 0; % Clear figure before plotting new
options.clf = 0; % Clear figure before plotting if set to 1
end
% Determine the figure number to use or create a new figure
% Determine the figure number to use or create a new one.
if isnan(options.fignum)
fig = figure; % Create a new figure and get its handle
fig = figure;
else
fig = figure(options.fignum); % Use the specified figure number
fig = figure(options.fignum);
end
if options.clf
clf(fig); % Clear the figure if requested
clf(fig);
end
hold on
ax = gca;
N = numel(ax.Children);
% Set up a colormap for consistent coloring
% Set up a colormap for consistent coloring.
cmap = linspecer(8);
options.color = cmap(mod(N, size(cmap, 1)) + 1, :);
% Create subplots for n1, n2, n3
for i = 1:3
subplot(3, 1, i);
switch i
case 1
stem(n1, 'Color', options.color, 'LineWidth', 1,'Marker','.','MarkerSize',10);
title(sprintf('1st order Filter Coefficients: %d',numel(n1)));
case 2
stem(n2, 'Color', options.color, 'LineWidth', 1,'Marker','.','MarkerSize',10);
title(sprintf('2nd order Filter Coefficients: %d',numel(n2)));
case 3
stem(n3, 'Color', options.color, 'LineWidth', 1,'Marker','.','MarkerSize',10);
title(sprintf('3rd order Filter Coefficients: %d',numel(n3)));
end
ylim([-1, 1]); % Scale y-axis to -1 and 1
% Build cell arrays for coefficients and their corresponding titles.
coeffs = {};
titles = {};
if ~isempty(options.n1)
coeffs{end+1} = options.n1;
titles{end+1} = sprintf('1st order Filter Coefficients: %d', numel(options.n1));
end
if ~isempty(options.n2)
coeffs{end+1} = options.n2;
titles{end+1} = sprintf('2nd order Filter Coefficients: %d', numel(options.n2));
end
if ~isempty(options.n3)
coeffs{end+1} = options.n3;
titles{end+1} = sprintf('3rd order Filter Coefficients: %d', numel(options.n3));
end
numSubplots = numel(coeffs);
for i = 1:numSubplots
subplot(1, numSubplots, i);
stem(coeffs{i}, 'Color', options.color, 'LineWidth', 1, ...
'Marker', '.', 'MarkerSize', 10);
title(titles{i});
ylim([-1, 1]); % Set y-axis limits to [-1, 1]
grid on;
grid minor
grid minor;
xlabel('Coefficient Index');
ylabel('Amplitude');
end
% Ensure the layout is tight for better visibility
sgtitle('Filter Coefficients'); % Overall title
end
sgtitle('Filter Coefficients'); % Overall title for the figure
end

36
Functions/EQ_visuals/showEQfilter.m Normal file
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@@ -0,0 +1,36 @@
function showEQfilter(coefficients,fs)
% Assuming that obj.e contains the final FFE filter coefficients.
% Set the number of frequency points and sampling frequency.
nfft = 1024; % Number of frequency points
% Compute the frequency response of the FFE filter.
[H, f] = freqz(coefficients, 1, nfft, fs);
% Keep only the first half of the frequency response (up to the Nyquist frequency).
half_nfft = floor(nfft/2) + 1;
f = f(1:half_nfft);
H = H(1:half_nfft);
% Plot the magnitude and phase responses.
figure;
% Magnitude response (in dB)
subplot(2,1,1);
hold on
plot(f.*1e-9, 20*log10(abs(1./H)));
title('(Inverted) Magnitude Response of FFE Filter');
xlabel('Frequency (Hz)');
ylabel('Magnitude (dB)');
grid on;
% Phase response
subplot(2,1,2);
plot(f.*1e-9, unwrap(angle(H)));
title('Phase Response of FFE Filter');
xlabel('Frequency (Hz)');
ylabel('Phase');
grid on;
end

2
Functions/EQ_visuals/showLevelHistogram.m
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@@ -39,7 +39,9 @@ end
intermediate = received_sd(lvl,:);
cnt(lvl) = round(numel(intermediate(~isnan(intermediate)))./length(eq_signal),3).*100;
hold on
warning off
histogram(received_sd(lvl,:),1000,"EdgeAlpha",0,'DisplayName',['Lvl ',num2str(lvl),' | ',num2str(cnt(lvl)),' %'],'FaceColor',lvlcol(lvl,:),'Normalization','pdf');
warning on
end
legend
grid on

40
Functions/Metrics/calc_air_plain.m Normal file
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@@ -0,0 +1,40 @@
function [airs] = calc_air_plain(noisy_signal,reference_signal,options)
% Calculation of AIR acc. to J. Kozesnik, „Numerically Computing Achievable Rates of Memoryless Channels“, Francisco Javier Garcıa-Gomez, doi: 10.1007/978-94-009-9857-5.
% Implementation is not accessible, I mailed TUM to get the code...
arguments(Input)
noisy_signal;
reference_signal;
options.skip_front = 0;
options.skip_end = 0;
options.returnErrorLocation = 0;
end
options.skip_end = abs(options.skip_end);
options.skip_front = abs(options.skip_front);
assert((options.skip_end+options.skip_front)<length(noisy_signal),"You can not skip more bits than overall length of data! Set skip_front or skip_end to lower value or check data_in");
% TRIM
[noisy_signal,reference_signal]=trimseq(noisy_signal,reference_signal,options.skip_front,options.skip_end);
% CALC EVM
%%% new implementation of AIR
constellation = unique(reference_signal);
reference_idx = arrayfun(@(x) find(constellation == x, 1), reference_signal);
air = air_garcia_implementation(constellation',noisy_signal',reference_idx');
function [data_,reference_]=trimseq(data,reference,skipstart,skip_end)
data_ = data(skipstart+1:end-skip_end,:);
delta_bits = length(reference) - length(data);
skip_end = delta_bits + skip_end;
reference_ = reference(skipstart+1:end-skip_end,:);
end
end

36
Functions/Metrics/calc_snr.m Normal file
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@@ -0,0 +1,36 @@
function [snr_all, snr_per_level] = calc_snr(tx_signal, eq_noise)
% CALC_SNR Calculates overall SNR and level-wise SNR for a PAM-M constellation.
%
% [snr_all, snr_per_level] = calc_snr(tx_signal, eq_noise)
%
% Inputs:
% tx_signal - Vector of transmitted signal values.
% eq_noise - Vector of corresponding noise samples.
%
% Outputs:
% snr_all - Overall SNR computed using all signal values.
% snr_per_level - A vector where each element is the SNR computed
% for a unique amplitude level in tx_signal.
%
% The function first computes the overall SNR using the full signal vectors.
% Then it uses the unique levels in tx_signal to calculate the SNR for
% the symbols corresponding to each level separately.
% Calculate overall SNR using the complete signals
snr_all = snr(tx_signal, eq_noise);
% Get the unique amplitude levels in the transmitted signal
levels = unique(tx_signal);
% Preallocate an array to store the SNR for each unique level
snr_per_level = zeros(size(levels));
% Loop over each unique level to compute the SNR for that level
for i = 1:length(levels)
% Find indices where tx_signal equals the current level
idx = (tx_signal == levels(i));
% Compute the SNR for these indices
snr_per_level(i) = snr(tx_signal(idx), eq_noise(idx));
end
end

2
Functions/beautifyBERplot.m
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@@ -25,7 +25,7 @@ function beautifyBERplot()
% Set logarithmic scale for y-axis, but only if it makes sense.
% If this is not always desired, you could condition this on the presence of lines or data.
% set(gca, 'YScale', 'log');
set(gca, 'YScale', 'log');
% Customize grid and box appearance
set(gca, 'Box', 'on', 'LineWidth', 0.8); % Thicker border

92
projects/HighSpeedExperiment_2024/auswertung MPI/analyze_mpi.m Normal file
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@@ -0,0 +1,92 @@
% basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
% db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
if 1
uloops = struct;
uloops.precomp = [0,1];
uloops.db_precode = [0,1];
uloops.bitrate = [224,336,360,390,420,448].*1e9; %[300,330,360,390,420,450,480] [224,336,360,390,420,448] for MPI
% uloops.laser_wavelength = [1293,1297.5,1302,1306.5,1310,1313.4,1318,1322.7,1327.4];
uloops.laser_wavelength = [1310];
uloops.M = [4,6,8];
uloops.link_length = [1]; % 1,2,3,5,6,8,10
wh = DataStorage(uloops);
wh.addStorage("ber");
% wh = submit_simulations(wh,"parallel",0,"simulation_mode",0);
wh = submit_handle(@dsp_mpi,wh,"parallel",1);
end
a = wh_mpi_112gbd.getStoValue('ber',uloops.precomp, uloops.db_precode, uloops.bitrate(1) , uloops.laser_wavelength, uloops.M, uloops.link_length);
%VNLE standalone
try
ber_vnle = cellfun(@(x) x.vnle_dfe_package{1,1}.ber_vnle, a);
end
%MLSE
try
ber_values_mlse = cellfun(@(s) cellfun(@(pkg) pkg.ber_mlse, s.vnle_pf_package, 'UniformOutput', false), a, 'UniformOutput', false);
ber_values_mlse = cell2mat(ber_values_mlse{1});
end
%DB
try
ber_values_db = cellfun(@(s) cellfun(@(pkg) pkg.ber, s.dbtgt_package, 'UniformOutput', false), a, 'UniformOutput', false);
ber_values_db = cell2mat(ber_values_db{1});
end
xax = [0
3
6
9
12
15
18
21
24
27
30
45];
cols = cbrewer2('Set1',8);
% Compute min, max, and mean for PAM 4 MLSE
min_mlse = min(ber_values_mlse, [], 2);
max_mlse = max(ber_values_mlse, [], 2);
mean_mlse = mean(ber_values_mlse, 2);
err_lower_mlse = mean_mlse - min_mlse;
err_upper_mlse = max_mlse - mean_mlse;
err_mlse = [err_lower_mlse, err_upper_mlse];
% Compute min, max, and mean for PAM 4 DB tgt.
min_db = min(ber_values_db, [], 2);
max_db = max(ber_values_db, [], 2);
mean_db = mean(ber_values_db, 2);
err_lower_db = mean_db - min_db;
err_upper_db = max_db - mean_db;
err_db = [err_lower_db, err_upper_db];
figure(1)
hold on
title('MPI');
% Plot the MLSE curve with bounded error using boundedline
[hl_mlse, hp_mlse] = boundedline(xax, mean_mlse, err_mlse,'Color', cols(1,:));
plot(xax,ber_values_mlse,'DisplayName','PAM 4 MLSE','Color',cols(1,:),'LineStyle','-','HandleVisibility','on','Marker','none','LineWidth',0.2);
% Plot the DB tgt. curve with bounded error using boundedline
[hl_db, hp_db] = boundedline(xax, mean_db, err_db, 'Color', cols(2,:));
plot(xax,ber_values_db,'DisplayName','PAM 4 MLSE','Color',cols(2,:),'LineStyle','-','HandleVisibility','on','Marker','none','LineWidth',0.2);
% Format the plot
xticks(xax);
set(gca, 'YScale', 'log');
ylim([5e-5 0.4]);
xlim([min(xax) max(xax)]);
yline([4.85e-3, 2e-2], 'HandleVisibility', 'off');
legend
% beautifyBERplot()
xlabel('Interference Attenuation');
ylabel('BER');

349
projects/HighSpeedExperiment_2024/auswertung MPI/dsp_mpi.m Normal file
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@@ -0,0 +1,349 @@
function [output] = dsp_mpi(varargin)
simulation_mode = 0;
%%% Change folder
curFolder = pwd;
funcFolder=fileparts(mfilename('fullpath'));
if ~isempty(funcFolder)
cd(funcFolder);
end
%%% Run parameters
% TX
M = 4;
fsym = 180e9;
apply_pulsef = 1;
fdac = 256e9;
fadc = 256e9;
random_key = 1;
interference_attenuation = 0;
is_mpi = 1;
precomp = 0;
db_precode = 0;
db_encode = 0;
rcalpha = 0.05;
kover = 16;
vbias_rel = 0.5;
u_pi = 2.9;
vbias = -vbias_rel*u_pi;
laser_wavelength = 1293;
laser_linewidth = 0;
tx_bw_nyquist = 0.8;
% Channel
link_length = 1;
% RX
rop = -5;
rx_bw_nyquist = 0.8;
vnle_order1 = 50;
vnle_order2 = 5;
vnle_order3 = 5;
vnle_order=[vnle_order1,vnle_order2,vnle_order3];
dfe_order = [0 0 0];
pf_ncoeffs = 1;
alpha = 0;
len_tr = 4096*2;
mu_ffe1 = 0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dc = 0.005;
mu_dc = 0;
mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
mu_dfe = 0.0004;
dfe_ = sum(dfe_order)>0;
doub_mode = db_mode.no_db;
%%% change specific parameter if given in varargin
% Parse optional input arguments
if ~isempty(varargin)
var_s = varargin{1};
if isstruct(var_s)
fields = fieldnames(var_s);
for i = 1:numel(fields)
if isnumeric(fields{i})
eval([fields{i}, ' = ', num2str( var_s.(fields{i}) ), ';']);
fprintf("%s <-- %.2f \n", fields{i}, var_s.(fields{i}));
else
eval([fields{i}, ' = ', 'var_s.(fields{',num2str(i),'})' , ';']);
end
end
else
error('Optional variables should be passed as a struct.');
end
end
if doub_mode ~= db_mode.db_encoded
if precomp == 0 && db_precode == 1
doub_mode = db_mode.db_precoded;
db_precode = 1; % preceded data (in my measurement set, this corresponds to low precomp too!)
discard_precode = 0; %
emulate_precode = 0;
legendentry = 'low precomp; precoded';
disp('low precomp; precoded')
elseif precomp == 1 && db_precode == 1
doub_mode = db_mode.db_emulate;
db_precode = 0; % preceded data (in my measurement set, this corresponds to low precomp too!)
discard_precode = 0; %
emulate_precode = 1;
legendentry = 'high precomp; precoded';
disp('high precomp; precoded')
elseif precomp == 0 && db_precode == 0
doub_mode = db_mode.db_discard;
db_precode = 1; % preceded data (in my measurement set, this corresponds to low precomp too!)
discard_precode = 1; %
emulate_precode = 0;
legendentry = 'no precomp; not precoded';
disp('no precomp; not precoded')
elseif precomp == 1 && db_precode == 0
doub_mode = db_mode.no_db;
db_precode = 0; % preceded data (in my measurement set, this corresponds to low precomp too!)
discard_precode = 0; %
emulate_precode = 0;
legendentry = 'high precomp; not precoded';
disp('high precomp; not precoded')
end
else
end
fsym_ = floor( bitrate*1e-9./log2(M) ).*1e9;
if fsym_ ~= fsym
fsym = fsym_;
% fprintf('Adapted symbolrate to %d GBd, to match provided bitrate of %d GBit/s using PAM %d \n',fsym.*1e-9,bitrate.*1e-9, M);
end
f_nyquist = fsym/2;
basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
database = DBHandler("pathToDB",[basePath,'silas_labor.db']);
useGui = 0;
% db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
filterParams = database.tables;
% filterParams.Runs.run_id = 2958; % no db
% filterParams.Runs.run_id = 2937; % no db
filterParams.Configurations = struct( ...
'bitrate', bitrate, ...
'db_mode', db_precode+db_encode, ...
'fiber_length', link_length, ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', is_mpi, ...
'pam_level', M, ...
'precomp_amp', [], ...
'rop_attenuation', 0, ...
'symbolrate', [], ...
'v_awg', [], ...
'v_bias', [], ...
'wavelength', laser_wavelength ...
);
selectedFields = {'Runs.run_id','Runs.tx_bits_path', 'Runs.tx_symbols_path', 'Runs.rx_sync_path','Runs.rx_raw_path',...
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
'Configurations.interference_attenuation'};
[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
dataTable = dataTable(uniqueIdx,:); % Extract unique configurations for each run_id
fprintf('Found %d entries for requested Configuration. IDs are: %s \n \n',size(dataTable,1),jsonencode(dataTable.run_id(1:min(size(dataTable,1),100))));
output = struct();
vnle_pf_package = {};
vnle_dfe_package = {};
dbtgt_package = {};
disp(num2str(bitrate))
for iatt = 1:numel(dataTable.interference_attenuation)
current_run_id = dataTable.run_id(iatt);
Tx_bits = load([basePath, char(dataTable.tx_bits_path(iatt))]);
Tx_bits = Tx_bits.Bits;
Symbols_mapped = PAMmapper(M,0).map(Tx_bits);
Symbols_mapped.fs = fsym;
Symbols = load([basePath, char(dataTable.tx_symbols_path(iatt))]);
Symbols = Symbols.Symbols;
Scpe_load = load([basePath, char(dataTable.rx_sync_path(iatt))]);
Scpe_cell = Scpe_load.S;
[~,~,found]=Scpe_cell{2}.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",1);
if ~found
Raw_signal = load([basePath, char(dataTable.rx_raw_path(1))]);
Raw_signal = Raw_signal.Scpe_sig_raw;
[~,Scpe_cell,found] =Raw_signal.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
end
if ~found
if length(Symbols_mapped.signal) == sum(Symbols_mapped.signal == Symbols.signal)
warning('Could not synchronize the received signal with the stored symbols!')
else
[~,Scpe_cell,found] =Raw_signal.tsynch("reference",Symbols_mapped,"fs_ref",fsym,"debug_plots",0);
end
if ~found
warning('Could not synchronize the received signal with the stored symbols!')
end
end
%
% Raw_signal = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.55,"fs",Raw_signal.fs,"filterType",filtertypes.gaussian,"active",true).process(Raw_signal);
%
% Scpe_cell{1}.eye(fsym,M,"displayname",'eye','fignum',227);
%
% Raw_signal.spectrum("normalizeTo0dB",0,"fignum",11,"fft_length",2^12);
% Raw_signal.move_it_spectrum("fignum",334);
% Raw_signal.move_it_spectrum("fignum",334);
fsym = Symbols.fs;
if db_precode
Symbols_precoded = Symbols;
end
proc_occ = min(15,length(Scpe_cell));
for occ = 1:proc_occ
Scpe_sig = Scpe_cell{occ};
%%%%%% Sample to 2x fsym %%%%%%
Scpe_sig = Scpe_sig.resample("fs_out",2*fsym);
%%%%%% Sync Rx signal with reference %%%%%%
[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.5,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
%
% Pform = Pulseformer("fsym",Scpe_sig.fs,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha,"matched",0);
%
% Scpe_sig_matched = Pform.process(Scpe_sig);
%
% Scpe_sig.spectrum("normalizeTo0dB",0,"fignum",336,"displayname","scope ");
% Scpe_sig_matched.spectrum("normalizeTo0dB",0,"fignum",336,"displayname","matched");
%%% EQUALIZING
% eq_mlse = FFE_DCremoval("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr,"mu_dd",mu_ffe(1),"mu_tr",0,"order",ffe_order(1),"sps",2,"decide",0,"dc_buffer_len",1,"mu_dc",0.05);
% eq_mlse = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr,"mu_dd",mu_ffe(1),"mu_tr",0,"order",ffe_order(1),"sps",2,"decide",0);
% eq_mlse = FFE_DCremoval("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr,"mu_dd",mu_ffe(1),"mu_tr",0,"order",ffe_order(1),"sps",2,"decide",0,"dc_buffer_len",512,"mu_dc",0.05);
mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
vnle_order=[vnle_order1,vnle_order2,vnle_order3];
% %%%%% VNLE + DFE %%%%
if 0
eq_vnle_dfe = EQ("Ne",vnle_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
eq_2 = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
[result] = vnle(eq_vnle_dfe,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",doub_mode,"showAnalysis",1,"postFFE",[]);
vnle_dfe_package{iatt,occ} = result;
end
%%%%% VNLE + PF + MLSE %%%%
if 1
try
% len_tr = length(Symbols)-1000;
eq_vnle_ = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
% eq_vnle_ = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0,"order",vnle_order,"sps",2,"decide",0);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
eq_2 = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
[result] = vnle_postfilter_mlse(eq_vnle_,pf_,mlse_,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",doub_mode,'showAnalysis',0,"postFFE",[]);
vnle_pf_package{iatt,occ} = result;
database.addProcessingResult(current_run_id,result.resultsMLSE, result.equalizerConfigMLSE);
database.addProcessingResult(current_run_id,result.resultsVNLE, result.equalizerConfigVNLE);
catch
warning(['VNLE+MLSE fail: run id: ', num2str(current_run_id)],' occ:', num2str(occ), ' iatten: ',num2str(iatt))
end
end
%%%%% Duobinary Targeting %%%%
if 1
try
mlse_db = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
eq_db = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
eq_2 = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
[result] = duobinary_target(eq_db, mlse_db, M, Scpe_sig, Symbols, Tx_bits, "precode_mode", doub_mode,'showAnalysis',0,"postFFE",[]);
dbtgt_package{iatt,occ} = result;
database.addProcessingResult(current_run_id,result.resultsDBtgt, result.equalizerConfigDBtgt);
catch
warning(['VNLE DB+MLSE fail: run id: ', num2str(current_run_id)],' occ:', num2str(occ), ' iatten: ',num2str(iatt))
end
end
%%%%%% %db signaling => db encoded %%%%%
if 0
mlse_db_enc = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
eq_db_enc = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
[result] = duobinary_signaling(eq_db_enc, mlse_db_enc,M, Scpe_sig ,Symbols, Tx_bits);
dbenc_package{iatt,occ} = result;
end
% autoArrangeFigures;
disp('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ')
fprintf('\n')
end
if ~isempty(curFolder)
cd(curFolder);
end
end
output.dataTable = dataTable;
output.vnle_dfe_package = vnle_dfe_package;
output.vnle_pf_package = vnle_pf_package;
output.dbtgt_package = dbtgt_package;

91
projects/HighSpeedExperiment_2024/auswertung MPI/plot_from_database.m Normal file
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@@ -0,0 +1,91 @@
basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
database = DBHandler("pathToDB",[basePath,'silas_labor.db']);
filterParams = database.tables;
filterParams.Configurations = struct( ...
'bitrate', 336e9, ...
'db_mode', 0, ...
'fiber_length', 1, ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 1, ...
'pam_level', 4, ...
'rop_attenuation', 0 ...
);
filterParams.EqualizerParameters.diff_precode = int32(db_mode.no_db);
filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
selectedFields = {'Configurations.run_id' 'Runs.rx_raw_path' 'Configurations.bitrate' 'Configurations.symbolrate' 'Configurations.pam_level' 'Configurations.db_mode' 'Configurations.rop_attenuation' 'Configurations.is_mpi' 'Configurations.interference_attenuation' 'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'Measurements.power_pd_in' 'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.SNR' 'Results.GMI' 'Results.Alpha'};
[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
fixedVars = {'run_id','eq_id','bitrate'};
resultTable = groupIt(fixedVars,dataTable);
% Create a new figure
figure(1);
hold on
unique_rates = unique(resultTable.bitrate);
for i = 1:numel(unique_rates)
% Plot BER vs. interference_attenuation
plot(resultTable.power_mpi_signal(resultTable.bitrate==unique_rates(i),:)-resultTable.power_mpi_interference(resultTable.bitrate==unique_rates(i),:), resultTable.BER(resultTable.bitrate==unique_rates(i),:), 'o-', 'LineWidth', 1.5);
end
% Label the axes and add a title
xlabel('Interference Attenuation');
ylabel('BER');
title('BER vs. Interference Attenuation');
% Enable grid for better readability
grid on;
beautifyBERplot;
function resultTable = groupIt(fixedVars,dataTable)
% Group by run_id and eq_id (adjust grouping keys as needed)
[G, groupKeys] = findgroups(dataTable(:, fixedVars));
% Preallocate a cell array for aggregated data.
varNames = dataTable.Properties.VariableNames;
nVars = numel(varNames);
aggData = cell(height(groupKeys), nVars);
groupCount = zeros(height(groupKeys), 1); % To store the size of each group
% Loop over each group.
for i = 1:height(groupKeys)
idx = (G == i); % Logical index for group i
groupCount(i) = sum(idx); % Count number of rows in this group
% For each variable in the table:
for j = 1:nVars
colData = dataTable.(varNames{j});
if isnumeric(colData)
% For numeric data, compute the mean.
aggData{i, j} = mean(colData(idx));
else
% For non-numeric data, take the first entry.
if iscell(colData)
aggData{i, j} = colData{find(idx, 1)};
else
aggData{i, j} = colData(find(idx, 1));
end
end
end
end
% Convert the aggregated cell array into a table.
resultTable = cell2table(aggData, 'VariableNames', varNames);
% Append the group count as a new column.
resultTable.nRows = groupCount;
end

0
projects/IMDD_base_system/analyze_burg.m → projects/HighSpeedExperiment_2024/auswertung deliv 06 feb 2025/analyze_burg.m
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0
projects/IMDD_base_system/analyze_equalizer_taps.m → projects/HighSpeedExperiment_2024/auswertung deliv 06 feb 2025/analyze_equalizer_taps.m
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projects/IMDD_base_system/analyze_lambda.m → projects/HighSpeedExperiment_2024/auswertung deliv 06 feb 2025/analyze_lambda.m
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0
projects/IMDD_base_system/analyze_postfilter_taps.m → projects/HighSpeedExperiment_2024/auswertung deliv 06 feb 2025/analyze_postfilter_taps.m
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projects/HighSpeedExperiment_2024/auswertung/App/auswertungs_app.mlapp
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17
projects/IMDD_base_system/imdd_it.m
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@@ -1,25 +1,22 @@
% basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
% db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
if 0
if 1
uloops = struct;
uloops.precomp = [1];
uloops.db_precode = [0];
uloops.bitrate = [300,330,360,390,420,450,480].*1e9; %[300,330,360,390,420,450,480]
uloops.bitrate = [224].*1e9; %[300,330,360,390,420,450,480] [224,336,360,390,420,448] for MPI
% uloops.laser_wavelength = [1293,1297.5,1302,1306.5,1310,1313.4,1318,1322.7,1327.4];
uloops.precomp = [0];
uloops.db_precode = [0];
uloops.bitrate = [330].*1e9; %[300,330,360,390,420,450,480]
uloops.laser_wavelength = [1310];
uloops.M = [4,6,8];
uloops.M = [4];
uloops.link_length = [2]; % 1,2,3,5,6,8,10
uloops.link_length = [1]; % 1,2,3,5,6,8,10
uloops.interference_attenuation = [0,3,6,9,12,15,18,21,24,27,30,45];
wh = DataStorage(uloops);
wh.addStorage("ber");
wh = submit_simulations(wh,"parallel",0,"simulation_mode",0);
wh = submit_simulations(wh,"parallel",0,"simulation_mode",1);
% wh = submit_simulations(wh,"parallel",0,"simulation_mode",0);
wh = submit_handle(@imdd_model,wh,"parallel",1);
end
wh_ana = wh_master;

25
projects/IMDD_base_system/imdd_model.m
View File

@@ -1,4 +1,6 @@
function [output] = imdd_model(simulation_mode,varargin)
function [output] = imdd_model(varargin)
simulation_mode = 0;
%%% Change folder
curFolder = pwd;
@@ -17,6 +19,9 @@ fdac = 256e9;
fadc = 256e9;
random_key = 1;
interference_attenuation = 0;
is_mpi = 1;
precomp = 0;
db_precode = 0;
@@ -216,7 +221,7 @@ else
'fiber_length', link_length, ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 0, ...
'is_mpi', is_mpi, ...
'pam_level', M, ...
'precomp_amp', [], ...
'rop_attenuation', 0, ...
@@ -227,7 +232,8 @@ else
);
selectedFields = {'Runs.run_id','Runs.tx_bits_path', 'Runs.tx_symbols_path', 'Runs.rx_sync_path','Runs.rx_raw_path',...
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias'};
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
'Configurations.interference_attenuation'};
[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
@@ -245,6 +251,7 @@ else
% Raw_signal = load([basePath, char(dataTable.rx_raw_path(1))]);
% Raw_signal.Scpe_sig_raw.plot("displayname",'0db atten','fignum',10101)
% Raw_signal = Raw_signal.Scpe_sig_raw;
%
% Raw_signal = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.55,"fs",Raw_signal.fs,"filterType",filtertypes.gaussian,"active",true).process(Raw_signal);
@@ -270,7 +277,7 @@ dbtgt_package = {};
proc_occ = min(1,length(Scpe_cell));
for occ = 1:proc_occ
for occ = 1%:proc_occ
Scpe_sig = Scpe_cell{occ};
@@ -303,14 +310,16 @@ for occ = 1:proc_occ
% %%%%% VNLE + DFE %%%%
if 0
eq_vnle_dfe = EQ("Ne",vnle_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
[result] = vnle(eq_vnle_dfe,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",doub_mode,"showAnalysis",0);
eq_2 = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
[result] = vnle(eq_vnle_dfe,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",doub_mode,"showAnalysis",1,"postFFE",[]);
vnle_dfe_package{occ} = result;
end
%%%%% VNLE + PF + MLSE %%%%
if 0
if 1
% len_tr = length(Symbols)-1000;
eq_vnle_ = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
@@ -325,7 +334,7 @@ for occ = 1:proc_occ
%%%%% Duobinary Targeting %%%%
if 0
if 1
mlse_db = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
eq_db = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);

85
projects/Messung_Zürich/change_filenames.m Normal file
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@@ -0,0 +1,85 @@
%% 1) Read all files in the TR folder
pathToTimingRecov = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\TR_ZIP";
trFiles = dir(fullfile(pathToTimingRecov, 'TR_SILAS_*.mat'));
%% Directory for measurement files (used to extract metadata)
pathToMeasurement = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\20250221";
%% Initialize lists for different PAM types
listPAM2 = {};
listPAM4 = {};
listPAM6 = {};
listPAM8 = {};
%% Loop over each TR file
for k = 1:length(trFiles)
% Get current TR file name
trFileName = trFiles(k).name;
% 2) Extract file code from TR file name.
% For a filename like "TR_SILAS_20250221T001800.mat" the code is "20250221T001800".
filecode = extractBetween(trFileName, 'TR_SILAS_', '.mat');
% Find corresponding measurement file by code (custom function)
measurementFilename = findFileByCode(pathToMeasurement, filecode{1});
% 3) Extract parameters from the measurement filename using regex.
% Expected measurement filename format (example):
% "Pmod_-10p000dBm_P_PD_-20p000dBm_..._32GBd_4PAM__1234T5678"
tokens = regexp(measurementFilename, ...
'Pmod_([-0-9p]+)dBm_P_PD_([-0-9p]+)dBm_.*?_(\d+)GBd_(\d+)PAM__\d+T\d+', ...
'tokens');
if isempty(tokens)
error('Filename format not recognized for measurement file: %s', measurementFilename);
end
tokens = tokens{1};
% Convert token strings to numbers
config.P_laser = str2double(strrep(tokens{1}, 'p', '.'));
config.P_pd = str2double(strrep(tokens{2}, 'p', '.'));
config.fsym = str2double(tokens{3}) * 1e9; % Convert from GBd to Hz
config.M = str2double(tokens{4});
% Display loaded metadata
fprintf('Loaded measurement file: %s\n', measurementFilename);
fprintf('P_laser: %.3f dBm\n', config.P_laser);
fprintf('P_pd: %.3f dBm\n', config.P_pd);
fprintf('fsym: %.1f GBd\n', config.fsym * 1e-9);
fprintf('M: %d\n', config.M);
% 4) Rename the TR file to include the metadata.
% New filename format: TR_SILAS_<code>_Pmod_<P_laser>dBm_P_PD_<P_pd>dBm_<fsym in GBd>GBd_<M>PAM.mat
newTRname = sprintf('TR_SILAS_%s_Pmod_%.3fdBm_P_PD_%.3fdBm_%dGBd_%dPAM', ...
filecode{1}, config.P_laser, config.P_pd, config.fsym/1e9, config.M);
newTRname = strrep(newTRname,'.','p');
newTRname = [newTRname, '.mat'];
movefile(fullfile(pathToTimingRecov, trFileName), fullfile(pathToTimingRecov, newTRname));
% Append the file code to the corresponding PAM list based on config.M
switch config.M
case 2
listPAM2{end+1} = filecode{1};
case 4
listPAM4{end+1} = filecode{1};
case 6
listPAM6{end+1} = filecode{1};
case 8
listPAM8{end+1} = filecode{1};
otherwise
warning('Unexpected PAM value %d in file %s', config.M, measurementFilename);
end
end
%% Display the lists of file codes for each PAM type
disp('List of file codes for PAM2:');
disp(listPAM2);
disp('List of file codes for PAM4:');
disp(listPAM4);
disp('List of file codes for PAM6:');
disp(listPAM6);
disp('List of file codes for PAM8:');
disp(listPAM8);

431
projects/Messung_Zürich/dsp_all_files.m
View File

@@ -1,113 +1,366 @@
% 202502211_km_fiber_Pmod_15dBm_P_PD_9p2494dBm_W03C34-0603E03_160GBd_8PAM__20250221T024256.h5
M = 8;
if M == 4
file_codes = {"20250221T032043",...
"20250221T032312",...
"20250221T032424",...
"20250221T032529",...
"20250221T032632",...
"20250221T032800",...
"20250221T032936",...
"20250221T033035",...
"20250221T033138",...
"20250221T033138",...
"20250221T033425",...
"20250221T033527",...
"20250221T033642",...
"20250221T033743",...
"20250221T033851",...
"20250221T034314",...
"20250221T034529",...
"20250221T034647",...
"20250221T034756",...
"20250221T034915",...
};
baudrate = [96,120,128,136,144,152,160,168,172,176,180,184,188,192,196,200,204,212,220,224];
pam4.result_vnle = result_vnle;
pam4.result_db = result_db;
M = 6;
if M == 2
file_codes = {'20250221T035221' '20250221T035354' '20250221T035824' '20250221T035931' '20250221T040035' '20250221T040132' '20250221T040226' '20250221T040523' '20250221T040646' '20250221T040723' '20250221T040843' '20250221T041011' '20250221T041101' '20250221T041244'};
elseif M == 4
file_codes = {'20250221T030844' '20250221T032043' '20250221T032312' '20250221T032424' '20250221T032529' '20250221T032632' '20250221T032800' '20250221T033035' '20250221T033138' '20250221T033246' '20250221T033425' '20250221T033527' '20250221T033642' '20250221T033743' '20250221T033851' '20250221T034314' '20250221T034529' '20250221T034647' '20250221T034756' '20250221T034915'};
elseif M == 6
file_codes = {'20250221T041445' '20250221T041512' '20250221T041539' '20250221T041607' '20250221T041633' '20250221T041702' '20250221T041729' '20250221T041758' '20250221T041825' '20250221T041854' '20250221T041922' '20250221T041951' '20250221T042019' '20250221T042048' '20250221T042117' '20250221T042147' '20250221T042215'};
elseif M ==8
file_codes = {"20250221T004651",... %96
"20250221T004926",...
"20250221T005844",...
"20250221T010032",...
"20250221T010150",...
"20250221T025505",...%144 ohne balun
"20250221T024629",...%152 ohne balun
"20250221T024315",...%160 ohne balun
"20250221T023534",...%168 ohne balun
"20250221T024929",...%172 ohne balun
"20250221T025305",...%176 ohne balun
};
file_codes = {"20250221T024256"};
baudrate = [96,112,120,128,136,144,152,160,168,172,176];
file_codes = {'20250221T004926' '20250221T023534' '20250221T024256' '20250221T024629' '20250221T024929' '20250221T025305' '20250221T025505' '20250221T025856' '20250221T030122' '20250221T030311' '20250221T030513'};
end
if 1
if 0
uloops = struct;
uloops.filecode = file_codes;
uloops.mu_dc = [0];
uloops.vnle_order1 = [50];
uloops.vnle_order2 = [5];
uloops.vnle_order3 = [5];
wh = DataStorage(uloops);
wh.addStorage("ber");
wh = submit_handle(@dsp_ief_file,wh,"parallel",0);
wh = submit_handle(@dsp_ief_file,wh,"parallel",1);
end
if 0
% Bring figure(5) to focus
fig = figure(2);
% Get handles to all line objects in the figure
lines = findobj(fig, 'Type', 'line');
% Preallocate cell arrays to store the data for each line
xData = cell(numel(lines),1);
yData = cell(numel(lines),1);
% Loop through each line and extract its data
for k = 1:numel(lines)
xData{k} = get(lines(k), 'XData');
yData{k} = get(lines(k), 'YData');
end
ief.M2.baudr_new = xData{4};
ief.M2.ngmi_new = yData{4};
ief.M4.baudr_new = xData{3};
ief.M4.ngmi_new = yData{3};
ief.M6.baudr_new = xData{2};
ief.M6.ngmi_new = yData{2};
ief.M8.baudr_new = xData{1};
ief.M8.ngmi_new = yData{1};
ief.M2.ber_new = yData{4};
ief.M4.ber_new = yData{3};
ief.M6.ber_new = yData{2};
ief.M8.ber_new = yData{1};
pam2_baudr = xData{4};
pam2_ber = yData{4};
pam2_ngmi = yData{4};
pam4_baudr = xData{3};
pam4_ber = yData{3};
pam4_ngmi = yData{3};
pam6_baudr = xData{2};
pam6_ber = yData{2};
pam6_ngmi = yData{2};
pam8_baudr = xData{1};
pam8_ber = yData{1};
pam8_ngmi = yData{1};
pam2_baudr_lowdsp = xData{4};
pam2_ber_lowdsp = yData{4};
pam2_ngmi_lowdsp = yData{4};
pam4_baudr_lowdsp = xData{3};
pam4_ber_lowdsp = yData{3};
pam4_ngmi_lowdsp = yData{3};
pam6_baudr_lowdsp = xData{2};
pam6_ber_lowdsp = yData{2};
pam6_ngmi_lowdsp = yData{2};
pam8_baudr_lowdsp = xData{1};
pam8_ber_lowdsp = yData{1};
pam8_ngmi_lowdsp = yData{1};
end
a = wh.getStoValue('ber',uloops.filecode, uloops.mu_ffe1);
close all
figure(1)
cnt = 1;
for M = [2,4,6,8]
% for i = 1:numel(uloops.vnle_order2)
% for j = 1:numel(uloops.vnle_order3)
wh = eval(sprintf('wh_pam%d',M));
a =wh.getStoValue('ber',wh.parameter.filecode.values, wh.parameter.vnle_order1.values, wh.parameter.vnle_order2.values,wh.parameter.vnle_order3.values);
% a =wh_mit_1001.getStoValue('ber',uloops.filecode, wh_mit_1001.parameter.vnle_order1.values, wh_mit_1001.parameter.vnle_order2.values,wh_mit_1001.parameter.vnle_order3.values);
baudrate = cell2mat(cellfun(@(a) a.config.fsym, a, 'UniformOutput', false));
[baudrate, idx] = sort(baudrate);
% get best results per baudrate
ber_vnle_values = cellfun(@(a) cellfun(@(y) y.ber_vnle, a.vnle_package, 'UniformOutput', false), a, 'UniformOutput', false);
best_vnle = cellfun(@(x) min(cell2mat(x)), ber_vnle_values);
% get best results per baudrate
vnle = 0;
try
ber_vnle_values = cellfun(@(a) cellfun(@(y) y.ber_vnle, a.vnle_package, 'UniformOutput', false), a, 'UniformOutput', false);
ber_vnle_infrate = cellfun(@(a) cellfun(@(y) y.inf_rate_vnle, a.vnle_package, 'UniformOutput', false), a, 'UniformOutput', false);
% ber_vnle_values = cellfun(@(a) cellfun(@(y) y.ber_vnle, a.vnle_pf_package, 'UniformOutput', false), a, 'UniformOutput', false);
% best_vnle = cellfun(@(x) min(cell2mat(x)), ber_vnle_values);
%
% ber_mlse_values = cellfun(@(a) cellfun(@(y) y.ber_mlse, a.vnle_pf_package, 'UniformOutput', false), a, 'UniformOutput', false);
% best_mlse = cellfun(@(x) min(cell2mat(x)), ber_mlse_values);
best_vnle = cellfun(@(x) min(cell2mat(x)), ber_vnle_values);
best_vnle = best_vnle(idx);
best_gmi_vnle = cellfun(@(x) min(cell2mat(x)), ber_vnle_infrate);
best_gmi_vnle = best_gmi_vnle(idx);
%%% PLOT PARAMETER OPTIMIZATION
figure(2)
cols = cbrewer2('Set1',6);
hold on
title(sprintf('%d km | %d nm | PAM %d',1,1313.5,M));
xax = uloops.mu_ffe1;
plot(xax,best_vnle,'DisplayName',sprintf('VNLE 50,3,3'),'Color',cols(o,:),'LineStyle','-','HandleVisibility','on');
% netRateVNLE = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'NGMI',best_gmi_vnle./log2(M), 'BER',best_vnle);
% plot(xax,best_mlse,'DisplayName',sprintf('VNLE + 1 tap post-filter + MLSE'),'Color',cols(2,:),'LineStyle','-','HandleVisibility','on');
vnle = 1;
end
% plot(xax,best_db,'DisplayName',sprintf('DB tgt. + MLSE'),'Color',cols(3,:),'LineStyle','-','HandleVisibility','on');
mlse = 0;
try
ber_vnle_values = cellfun(@(a) cellfun(@(y) y.ber_vnle, a.vnle_pf_package, 'UniformOutput', false), a, 'UniformOutput', false);
best_vnle_2 = cellfun(@(x) min(cell2mat(x)), ber_vnle_values);
best_vnle_2 = best_vnle_2(idx);
set(gca, 'YScale', 'log');
ylim([5e-5 0.3]);
xlim([min(xax), max(xax) ]);
yline([3.8e-3, 2e-2],'HandleVisibility','off');
legend
beautifyBERplot()
xlabel('Adaption speed');
ylabel('BER');
gmi_vnle_values = cellfun(@(a) cellfun(@(y) y.gmi, a.vnle_pf_package, 'UniformOutput', false), a, 'UniformOutput', false);
best_gmi_vnle = cellfun(@(x) min(cell2mat(x)), gmi_vnle_values);
best_gmi_vnle = best_gmi_vnle(idx);
ber_mlse_values = cellfun(@(a) cellfun(@(y) y.ber_mlse, a.vnle_pf_package, 'UniformOutput', false), a, 'UniformOutput', false);
best_mlse = cellfun(@(x) min(cell2mat(x)), ber_mlse_values);
best_mlse = best_mlse(idx);
if M == 8
best_vnle_2(2) = [];
best_mlse(2) = [];
best_gmi_vnle(2) = [];
best_db(2) = [];
baudrate(2) = [];
elseif M == 4
end
mlse = 1;
end
db = 0;
try
ber_db_values = cellfun(@(a) cellfun(@(y) y.ber, a.dbtgt_package, 'UniformOutput', false), a, 'UniformOutput', false);
best_db = cellfun(@(x) min(cell2mat(x)), ber_db_values);
best_db = best_db(idx);
if M == 4
best_db(end-1:end) = [];
baudrate(end-1:end) = [];
end
db = 1;
end
%% BER PLOT
% figure(1)
subplot(1,2,1)
cols = cbrewer2('Set1',6);
hold on
title(sprintf('BER'));
% title(sprintf('%d 1st order',uloops.vnle_order1(i)));
xax = baudrate.*1e-9;
if M == 4 || M == 2
plot(xax,best_db,'DisplayName',sprintf('PAM %d | DB+MLSE',M),'Color',cols(M/2,:),'LineStyle',':','HandleVisibility','on','Marker','o','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
plot(ief.(sprintf('M%d', M)).baudr_new,ief.(sprintf('M%d', M)).ber_new,'DisplayName',sprintf('PAM %d |VNLE',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
elseif M == 6
plot(xax,best_vnle_2,'DisplayName',sprintf('PAM %d |VNLE',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
% plot(ief.(sprintf('M%d', M)).baudr_new,ief.(sprintf('M%d', M)).ber_new,'DisplayName',sprintf('PAM %d |VNLE',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
else
% plot(xax,best_vnle_2,'DisplayName',sprintf('PAM %d |VNLE',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
plot(ief.(sprintf('M%d', M)).baudr_new,ief.(sprintf('M%d', M)).ber_new,'DisplayName',sprintf('PAM %d |VNLE',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
end
if 0
if vnle
plot(xax,best_vnle,'DisplayName',sprintf('VNLE'),'Color',cols(2,:),'LineStyle','-','HandleVisibility','on');
end
if mlse
plot(xax,best_vnle_2,'DisplayName',sprintf('VNLE'),'Color',cols(2,:),'LineStyle','-','HandleVisibility','on');
plot(xax,best_mlse,'DisplayName',sprintf('VNLE + 1 tap post-filter + MLSE'),'Color',cols(3,:),'LineStyle','-','HandleVisibility','on');
end
if db
plot(xax,best_db,'DisplayName',sprintf('DB tgt. + MLSE'),'Color',cols(1,:),'LineStyle','-','HandleVisibility','on');
end
plot(ief.(sprintf('M%d', M)).baudr,ief.(sprintf('M%d', M)).ber,'DisplayName',sprintf('VNLE [100,15,15] (ETH)'),'Color',cols(4,:),'LineStyle','-','HandleVisibility','on');
plot(ief.(sprintf('M%d', M)).baudr_lowdsp,ief.(sprintf('M%d', M)).ber_lowdsp,'DisplayName',sprintf('VNLE [100,15,15] (ETH)'),'Color',cols(4,:),'LineStyle','-','HandleVisibility','on');
end
set(gca, 'YScale', 'log');
ylim([1e-6 0.3]);
xlim([92, 260 ]);
xticks([0:16:280]);
yline([4.85e-3, 2e-2],'HandleVisibility','off');
% legend
% beautifyBERplot()
xlabel('baudrate in GBd');
ylabel('BER');
set(findall(gca, '-property', 'Interpreter'), 'Interpreter', 'latex');
set(gcf, 'Color', 'w');
set(gca, 'Box', 'on', 'LineWidth', 0.8); % Thicker border
grid on;
set(gca, 'FontSize', 10, 'FontName', 'Times New Roman');
%
%% NGMI PLOT
% figure(2)
% cols = cbrewer2('Set1',6);
% hold on
% title(sprintf('%d km ; %.1f nm ; PAM %d',1,1313,M));
% xax = baudrate.*1e-9;
%
% plot(xax,best_gmi_vnle./log2(M),'DisplayName',sprintf('NGMI VNLE'),'Color',cols(2,:),'LineStyle','-','HandleVisibility','on');
%
% plot(ief.(sprintf('M%d', M)).baudr,ief.(sprintf('M%d', M)).ngmi,'DisplayName',sprintf('VNLE [100,15,15] (ETH)'),'Color',cols(4,:),'LineStyle','-','HandleVisibility','on');
% plot(ief.(sprintf('M%d', M)).baudr_lowdsp,ief.(sprintf('M%d', M)).ngmi_lowdsp,'DisplayName',sprintf('VNLE [100,15,15] (ETH)'),'Color',cols(4,:),'LineStyle','-','HandleVisibility','on');
%
% set(gca, 'YScale', 'log');
% ylim([0.7 1]);
% xlim([min(xax), max(xax) ]);
% xticks(xax);
% yline([0.8],'HandleVisibility','off');
% legend
% beautifyBERplot()
% xlabel('Baudrate in GBd');
% ylabel('NGMI');
%% AIR Plot
% subplot(1,3,2)
%
% netRateMLSE = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'BER',best_mlse);
% netRateVNLE = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'NGMI',best_gmi_vnle./log2(M), 'BER',best_vnle_2);
% netRateDB = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'BER',best_db);
%
% netRateIEF = TransmissionPerformance().calculateNetRate(log2(M)*ief.(sprintf('M%d', M)).baudr,'BER',ief.(sprintf('M%d', M)).ber,'NGMI',ief.(sprintf('M%d', M)).ngmi);
% netRateIEF_lowdsp = TransmissionPerformance().calculateNetRate(log2(M)*ief.(sprintf('M%d', M)).baudr_lowdsp,'BER',ief.(sprintf('M%d', M)).ber_lowdsp,'NGMI',ief.(sprintf('M%d', M)).ngmi_lowdsp);
%
% cols = cbrewer2('Set1',8);
% hold on
% title(sprintf('AIR'));
% xax = baudrate.*1e-9;
%
% if M == 2 || M == 4
%
% % plot(xax,netRateMLSE.HD.NetRate.*1e-9,'DisplayName',sprintf('MLSE - HD'),'Color',cols(1,:),'LineStyle',':','HandleVisibility','on');
% % plot(xax,netRateVNLE.SDHD.NetRate.*1e-9,'DisplayName',sprintf('VNLE SD+HD'),'Color',cols(4,:),'LineStyle','-','HandleVisibility','on');
% plot(xax,netRateDB.HD.NetRate.*1e-9,'DisplayName',sprintf('PAM %d | DB+MLSE HD',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on');
% plot(xax,netRateDB.O_FEC.NetRate.*1e-9,'DisplayName',sprintf('PAM %d | DB+MLSE O-FEC',M),'Color',cols(M/2,:),'LineStyle',':','HandleVisibility','on');
% % plot(xax,netRateDB.KP4_hamming.NetRate.*1e-9,'DisplayName',sprintf('PAM %d | DB+MLSE KP4-FEC',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on');
%
% else
%
% % plot(xax,netRateMLSE.HD.NetRate.*1e-9,'DisplayName',sprintf('MLSE - HD'),'Color',cols(1,:),'LineStyle',':','HandleVisibility','on');
% % plot(xax,netRateVNLE.SDHD.NetRate.*1e-9,'DisplayName',sprintf('VNLE SD+HD'),'Color',cols(4,:),'LineStyle','-','HandleVisibility','on');
% % plot(ief.(sprintf('M%d', M)).baudr,netRateIEF.HD.NetRate,'DisplayName',sprintf('PAM %d | HD IEF',M),'Color',cols(M/2,:),'LineStyle',':','HandleVisibility','on');
% plot(ief.(sprintf('M%d', M)).baudr,netRateIEF.SDHD.NetRate,'DisplayName',sprintf('PAM %d | SD+HD IEF',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on');
%
% end
% set(gca, 'YScale', 'log');
% ylim([92 450]);
% xlim([min(xax), max(xax) ]);
% xlim([92, 260 ]);
% xticks([0:16:280]);
% yline([0.8],'HandleVisibility','off');
% legend
% beautifyBERplot()
% xlabel('Baudrate in GBd');
% ylabel('Net Rate in Gbps');
%
%
%% NDR PLOT
%%% PLOT COMPLETE BAUDRATE
figure(20)
cols = cbrewer2('Set1',6);
hold on
title(sprintf('%d km | %d nm | PAM %d',1,1313.5,M));
subplot(1,2,2)
netRateIEF = TransmissionPerformance().calculateNetRate(log2(M)*ief.(sprintf('M%d', M)).baudr,'BER',ief.(sprintf('M%d', M)).ber,'NGMI',ief.(sprintf('M%d', M)).ngmi);
netRateIEF_lowdsp = TransmissionPerformance().calculateNetRate(log2(M)*ief.(sprintf('M%d', M)).baudr_lowdsp,'BER',ief.(sprintf('M%d', M)).ber_lowdsp,'NGMI',ief.(sprintf('M%d', M)).ngmi_lowdsp);
netRateIEF_new = TransmissionPerformance().calculateNetRate(log2(M)*ief.(sprintf('M%d', M)).baudr_new,'BER',ief.(sprintf('M%d', M)).ber_new,'NGMI',ief.(sprintf('M%d', M)).ngmi_new);
plot(baudrate,best_vnle,'DisplayName',sprintf('VNLE'),'Color',cols(1,:),'LineStyle','-','HandleVisibility','on');
plot(baudrate,best_mlse,'DisplayName',sprintf('VNLE + 1 tap post-filter + MLSE'),'Color',cols(2,:),'LineStyle','-','HandleVisibility','on');
cols = cbrewer2('Set1',8);
hold on
title(sprintf('Net Bitrate'));
xax = baudrate.*1e-9;
plot(baudrate,best_db,'DisplayName',sprintf('DB tgt. + MLSE'),'Color',cols(3,:),'LineStyle','-','HandleVisibility','on');
if M == 2 || M == 4
set(gca, 'YScale', 'log');
ylim([5e-5 0.3]);
xlim([min(baudrate), max(baudrate) ]);
yline([3.8e-3, 2e-2],'HandleVisibility','off');
legend
beautifyBERplot()
xlabel('Bit Rate in Gbps');
ylabel('BER');
log2M = log2(M);
netRateDB = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'BER',best_db);
% plot(xax,netRateMLSE.HD.NetRate.*1e-9,'DisplayName',sprintf('MLSE - HD'),'Color',cols(1,:),'LineStyle',':','HandleVisibility','on');
% plot(xax,netRateVNLE.SDHD.NetRate.*1e-9,'DisplayName',sprintf('VNLE SD+HD'),'Color',cols(4,:),'LineStyle','-','HandleVisibility','on');
plot(xax,netRateDB.HD.NetRate.*1e-9,'DisplayName',sprintf('PAM %d | DB+MLSE HD',M),'Color',cols(M/2,:),'LineStyle',':','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
plot(xax,netRateDB.O_FEC.NetRate.*1e-9,'DisplayName',sprintf('PAM %d | DB+MLSE O-FEC',M),'Color',cols(M/2,:),'LineStyle','-.','HandleVisibility','on','Marker','x','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
% plot(xax,netRateDB.KP4_hamming.NetRate.*1e-9,'DisplayName',sprintf('PAM %d | DB+MLSE KP4-FEC',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','*','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
plot(ief.(sprintf('M%d', M)).baudr_new,netRateIEF_new.SDHD.NetRate,'DisplayName',sprintf('PAM %d | SD+HD',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','o','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
plot(ief.(sprintf('M%d', M)).baudr_new,ief.(sprintf('M%d', M)).ngmi_new.*ief.(sprintf('M%d', M)).baudr_new.*log2M ,'DisplayName',sprintf('PAM %d | AIR',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','o','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
elseif M == 6
log2M = 2.5;
netRateMLSE = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'BER',best_mlse);
netRateVNLE = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'NGMI',best_gmi_vnle./log2(M), 'BER',best_vnle_2);
plot(xax,netRateVNLE.HD.NetRate.*1e-9,'DisplayName',sprintf('MLSE - HD'),'Color',cols(M/2,:),'LineStyle',':','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
plot(xax,netRateVNLE.SDHD.NetRate.*1e-9,'DisplayName',sprintf('VNLE SD+HD'),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','o','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
% plot(ief.(sprintf('M%d', M)).baudr_new,netRateIEF_new.HD.NetRate,'DisplayName',sprintf('PAM %d | HD IEF',M),'Color',cols(M/2,:),'LineStyle',':','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
% plot(ief.(sprintf('M%d', M)).baudr_new,netRateIEF_new.SDHD.NetRate,'DisplayName',sprintf('PAM %d | SD+HD IEF',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','o','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
else
log2M = log2(M);
netRateMLSE = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'BER',best_mlse);
netRateVNLE = TransmissionPerformance().calculateNetRate(log2(M)*baudrate,'NGMI',best_gmi_vnle./log2(M), 'BER',best_vnle_2);
% plot(xax,netRateVNLE.HD.NetRate.*1e-9,'DisplayName',sprintf('MLSE - HD'),'Color',cols(M/2,:),'LineStyle',':','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
% plot(xax,netRateVNLE.SDHD.NetRate.*1e-9,'DisplayName',sprintf('VNLE SD+HD'),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','o','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
plot(ief.(sprintf('M%d', M)).baudr_new,netRateIEF_new.HD.NetRate,'DisplayName',sprintf('PAM %d | HD IEF',M),'Color',cols(M/2,:),'LineStyle',':','HandleVisibility','on','Marker','square','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
plot(ief.(sprintf('M%d', M)).baudr_new,netRateIEF_new.SDHD.NetRate,'DisplayName',sprintf('PAM %d | SD+HD IEF',M),'Color',cols(M/2,:),'LineStyle','-','HandleVisibility','on','Marker','o','MarkerFaceColor',[1,1,1],'MarkerEdgeColor',cols(M/2,:),'MarkerSize',4,'LineWidth',1.4);
end
set(gca, 'YScale', 'log');
ylim([150 450]);
xlim([min(xax), max(xax) ]);
xlim([92, 270 ]);
xticks([0:16:280]);
yline([0.8],'HandleVisibility','off');
% legend
xlabel('Baudrate in GBd');
ylabel('Net Rate in Gbps');
set(findall(gca, '-property', 'Interpreter'), 'Interpreter', 'latex');
set(gcf, 'Color', 'w');
set(gca, 'Box', 'on', 'LineWidth', 0.8); % Thicker border
grid on;
set(gca, 'FontSize', 10, 'FontName', 'Times New Roman');
end

157
projects/Messung_Zürich/dsp_ief_file.m
View File

@@ -4,12 +4,14 @@ function [results] = dsp_ief_file(varargin)
mu_ffe1 = 0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dc = 0.00;
mu_dc = 0.0003; %0.0003;
mu_dfe = 0.0004;
vnle_order1 = 100;
mu_dfe_training = 0.0004;
vnle_order1 = 50;
vnle_order2 = 3;
vnle_order3 = 3;
dfe_mu = 0.0005;
tcorrect = 0;
@@ -33,10 +35,11 @@ function [results] = dsp_ief_file(varargin)
end
end
ssdPath = "D:\36_IMDD_Kiel\Data";
% code = "20250221T004802";
% code = "20250221T032043";
filename = findFileByCode(ssdPath, filecode{1});
pathToMeasurement = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\20250221";
filename = findFileByCode(pathToMeasurement, filecode{1});
pathToTimingRecov = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\TR_ZIP";
filename_2 = findFileByCode(pathToTimingRecov, filecode{1});
% Extract parameters from the filename using an updated regex
tokens = regexp(filename, 'Pmod_([-0-9p]+)dBm_P_PD_([-0-9p]+)dBm_.*?_(\d+)GBd_(\d+)PAM__\d+T\d+', 'tokens');
@@ -75,20 +78,45 @@ function [results] = dsp_ief_file(varargin)
dataRx = double(h5read(filepath, '/Waveforms/Channel 2/Channel 2Data')); % rohdaten des CH4
bitsTx = h5read(filepath, '/Settings/dataTx'); %Binär
bitsTx = reshape(bitsTx,log2(config.M),[])';
if config.M ~= 6
bitsTx = reshape(bitsTx,log2(config.M),[])';
else
%bitsTx = reshape(bitsTx,5,[])';
end
%%% Build Tx Signal (Bits, Pam Map, Symbols)
Tx_bits = Informationsignal(bitsTx);
Tx_symbols = PAMmapper(config.M,0,"eth_style",1).map(Tx_bits);
Tx_symbols.fs = config.fsym;
if 0
Rx_bits = PAMmapper(config.M,0,"eth_style",1).demap(Tx_symbols);
[~,~,ber_bw,~] = calc_ber(Rx_bits.signal,Tx_bits.signal(1:length(Rx_bits.signal)),"skip_front",100,"skip_end",150,"returnErrorLocation",1);
end
%%% Build Rx Signal (Rx, normalize,remove mean)
loadAfterTR = 0;
loadAfterTR = 1;
if loadAfterTR
rx_sig = load("testSilas.mat","signal_TR3");
rx_sig=rx_sig.signal_TR3;
Rx_Sig_resamp = Informationsignal(rx_sig,"fs",config.fsym*2);
if 1
rx_sig = load(filename_2);
rx_sig=rx_sig.signal_TR;
if config.M == 6
rx_sig_pam6 = zeros(length(rx_sig)*2,1);
rx_sig_pam6(1:2:end) = real(rx_sig);
rx_sig_pam6(2:2:end) = imag(rx_sig);
rx_sig = rx_sig_pam6;
Rx_Sig_resamp = Informationsignal(rx_sig,"fs",config.fsym);
% Tx_symbols.signal = Tx_symbols.signal(1:end/2);
else
Rx_Sig_resamp = Informationsignal(rx_sig,"fs",config.fsym*2);
end
else
rx_sig = load(string(['testSilas_',char(filecode{1}),'.mat']),"signal_TR3");
rx_sig=rx_sig.signal_TR3;
Rx_Sig_resamp = Informationsignal(rx_sig,"fs",config.fsym*2);
end
else
@@ -98,39 +126,17 @@ function [results] = dsp_ief_file(varargin)
Rx_Sig.signal = Rx_Sig.signal - mean(Rx_Sig.signal);
Rx_Sig = Rx_Sig.normalize("mode","rms");
Rx_Sig.spectrum("fignum",2,"displayname",'Rx Signal','normalizeTo0dB',1);
Rx_Sig = Filter('filtdegree',4,"f_cutoff",Tx_symbols.fs.*0.9,"fs",Rx_Sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Rx_Sig);
Rx_Sig.spectrum("fignum",2,"displayname",'Rx Signal filt','normalizeTo0dB',1);
mf = Pulseformer("alpha",config.ROF,"fsym",config.fsym,"fdac",Rx_Sig.fs,"matched",1,"pulse","rrc","pulselength",32);
Rx_matched = mf.process(Rx_Sig);
Rx_matched.spectrum("fignum",2,"displayname",'Rx Signal m','normalizeTo0dB',1);
%%%%%% Sample to 2x fsym %%%%%%
Rx_Sig_resamp = Rx_Sig.resample("fs_out",2*config.fsym);
Rx_Sig_resamp = Rx_Sig.resample("fs_out",config.fsym);
end
%%%%%% Sync Rx signal with reference (S is a cell array with all occurences) %%%%%%
[Rx_Sig_sync,S,isFlipped] = Rx_Sig_resamp.tsynch("reference",Tx_symbols,"fs_ref",config.fsym,"debug_plots",1);
% Rx_Sig_sync.eye(fsym,M,"fignum",4,"displayname",'eye diagram');
% timing syncroization ??
[~,S,isFlipped] = Rx_Sig_resamp.tsynch("reference",Tx_symbols,"fs_ref",config.fsym,"debug_plots",1);
% SYMSYNC = comm.SymbolSynchronizer("Modulation","PAM/PSK/QAM","SamplesPerSymbol",2);
%
% synd = SYMSYNC(Rx_Sig_sync.signal);
% Rx_Sig_sync.signal = synd;
%
% [Rx_Sig_sync,S,isFlipped] = Rx_Sig_sync.tsynch("reference",Tx_symbols,"fs_ref",config.fsym,"debug_plots",1);
%
% filter Bw
output = struct();
vnle_package = {};
vnle_pf_package = {};
@@ -139,78 +145,44 @@ function [results] = dsp_ief_file(varargin)
for s = 1%:length(S)
Rx_Sig_sync = S{s};
Rx_Sig_sync = Rx_Sig_sync.normalize("mode","rms");
if 1
Tx_symbols.spectrum("fignum",3,"displayname",'Tx Symbols','normalizeTo0dB',1);
Rx_Sig_sync.spectrum("fignum",3,"displayname",'No Matched Filter','normalizeTo0dB',1);
Nsym = 64;
sampsPerSym = 2;
rcrFilt = comm.RaisedCosineReceiveFilter(...
'Shape', 'Square root', ...
'RolloffFactor', config.ROF, ...
'FilterSpanInSymbols', Nsym, ...
'InputSamplesPerSymbol', sampsPerSym, ...
'DecimationFactor', 1);
yr = rcrFilt([Rx_Sig_sync.signal;zeros(Nsym*sampsPerSym/2, 1)]);
fltDelay = Nsym / (2*config.fsym);
yr = yr(fltDelay*Rx_Sig_sync.fs+1:end);
Rx_matched = Rx_Sig_sync;
Rx_matched.signal = yr;
% Rx_matched = Rx_matched.resample("fs_out",2*config.fsym);
% length(Rx_matched);
% symbolSync = comm.SymbolSynchronizer("Modulation","PAM/PSK/QAM","SamplesPerSymbol",2,"TimingErrorDetector","Mueller-Muller (decision-directed)");
% Rx_syncd = Rx_matched;
% [Rx_syncd.signal,tError] = symbolSync(Rx_syncd.signal);
% Rx_syncd.fs = config.fsym;
% length(Rx_syncd);
% figure();hold on;
% stem(Tx_symbols.normalize("mode","oneone").signal);
% stem(Rx_syncd.normalize("mode","oneone").signal);
%
% figure();hold on;
% plot(Rx_Sig_sync.normalize("mode","oneone").signal);
% plot(Rx_matched.normalize("mode","oneone").signal);
Rx_syncd = Rx_syncd.resample("fs_out",2*config.fsym);
Rx_matched.spectrum("fignum",3,"displayname",'After Matched Filter','normalizeTo0dB',1);
Rx_syncd.spectrum("fignum",3,"displayname",'Zero Crossing TR','normalizeTo0dB',1);
end
Rx_Sig_sync = Rx_Sig_sync.resample("fs_out",2*config.fsym);
mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
vnle_order=[vnle_order1,vnle_order2,vnle_order3];
eq_ = EQ("Ne",vnle_order,"Nb",[0,0,0],"training_length",4096*2,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",1,"ideal_dfe",1);
eq_ = EQ("Ne",vnle_order,"Nb",[0,0,0],"training_length",4096*4,"training_loops",4,"dd_loops",3,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe_training],"DFEmu",dfe_mu,"FFEmu",0,"plotfinal",0,"ideal_dfe",0,"plottrain",0);
%%%%% VNLE only (or DFE) %%%%
if 1
if 0
[result] = vnle(eq_,config.M,Rx_syncd,Tx_symbols,Tx_bits,"precode_mode",db_mode.no_db,"showAnalysis",1,'eth_style',1);
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",1001,"sps",1,"decide",0);
[result] = vnle(eq_,config.M,Rx_Sig_sync,Tx_symbols,Tx_bits,"precode_mode",db_mode.no_db,"showAnalysis",1,'eth_style',1,'postFFE',eq_post);
netRate = TransmissionPerformance().calculateNetRate(log2(config.M)*config.fsym,'NGMI',result.inf_rate_vnle, 'BER',result.ber_vnle);
vnle_package{s} = result;
end
%%%%% VNLE + PF + MLSE %%%%
if 0
if 1
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",1001,"sps",1,"decide",0);
pf_ = Postfilter("ncoeff",1,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels);
mlse_ = MLSE_viterbi("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels);
doub_mode = db_mode.no_db;
doub_mode = db_mode.no_db;
[result] = vnle_postfilter_mlse(eq_,pf_,mlse_,config.M,Rx_Sig_sync,Tx_symbols,Tx_bits,"precode_mode",doub_mode,'showAnalysis',1,'eth_style_symbol_mapping',1);
[result] = vnle_postfilter_mlse(eq_,pf_,mlse_,config.M,Rx_Sig_sync,Tx_symbols,Tx_bits,"precode_mode",doub_mode,'showAnalysis',1,'eth_style_symbol_mapping',1,'postFFE',eq_post);
netRate = TransmissionPerformance().calculateNetRate(log2(config.M)*config.fsym,'NGMI',result.gmi./log2(config.M), 'BER',result.ber_mlse);
fprintf('VNLE SD: %.1f GBd \n',netRate.SDHD.NetRate.*1e-9);
fprintf('MLSE HD: %.1f GBd \n',netRate.HD.NetRate.*1e-9);
vnle_pf_package{s} = result;
end
%%%%% Duobinary Targeting %%%%
if 0
if 1
mlse_db = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",config.M,"trellis_states",PAMmapper(config.M,0).levels);
doub_mode = db_mode.db_emulate;
@@ -225,6 +197,7 @@ function [results] = dsp_ief_file(varargin)
results.vnle_package = vnle_package;
results.vnle_pf_package = vnle_pf_package;
results.dbtgt_package = dbtgt_package;
results.config = config;
end

19
projects/Messung_Zürich/minimal_pam4_mlse.m Normal file
View File

@@ -0,0 +1,19 @@
mlse_sig_sd=load("imdd_simulation\projects\Messung_Zürich\mlse_sig_sd.mat","mlse_sig_sd");
mlse_sig_sd = mlse_sig_sd.mlse_sig_sd;
tx_symbols=load("imdd_simulation\projects\Messung_Zürich\tx_symbols.mat","tx_symbols");
tx_symbols = tx_symbols.tx_symbols;
mlse_ = MLSE_viterbi("duobinary_output",0,'M',4,'trellis_states',PAMmapper(4,0).levels);
mlse_.DIR = [1.0000 0.5452];
mlse_sig_sd = mlse_.process(mlse_sig_sd);
%
% mlse_ = MLSE_viterbi("duobinary_output",0,'M',4,'trellis_states',PAMmapper(4,0).levels);
% mlse_.DIR = [1.0000 0.5452];
% mlse_sig_sd = mlse_.process(mlse_sig_sd,tx_symbols);
h = [1.0000 0.5452];
chatgpt_answer(mlse_sig_sd.signal,tx_symbols.signal,h)

40
test/matched_filter_minimal.m
View File

@@ -26,24 +26,34 @@ tx_bw_nyquist = 0.8;
O = 18; %order of prbs
N = 2^(O-1); %length of prbs
rcalpha = 1;
Pform = Pulseformer("fsym",fsym,"fdac",fdac,"pulse","rrc","pulselength",1024,"alpha",rcalpha);
%%%%% MOVE-IT PRMS %%%%
Mi_prms = Moveit_wrapper("prms");
if M == 6
Mi_prms.para.bl = 2^(O-2);
Mi_prms.para.dimension = 5;
else
Mi_prms.para.bl = 2^(O-1);
Mi_prms.para.dimension = log2(M); %2.5bits/sym -> 2 bit/sym
end
Mi_prms.para.rand = 0;
Mi_prms.para.order = floor(O / log2(M));
Mi_prms.para.skip =0;
Mi_prms.para.bruijn = 0;
Mi_prms.para.reset_prms = 0;
Mi_prms.para.method = 1;
bitpattern = Mi_prms.process([]);
if M == 6
bitpattern = reshape(bitpattern',[],1);
bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
end
bits = Informationsignal(bitpattern);
symbols = PAMmapper(M,0).map(bits);
symbols.fs = fsym;
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
"fsym",fsym,"M",M,"order",18,"useprbs",0,...
"fs_out",fdac,...
"applyclipping",0,"clipfactor",1.5,...
"applypulseform",apply_pulsef,"pulseformer",Pform,...
"randkey",random_key,...
"db_precode",db_precode,"db_encode",db_encode,...
"mrds_code",0,"mrds_blocklength",512).process();
Digi_sig.spectrum("displayname",'Signal after shaping','fignum',1);
% Digi_sig.move_it_spectrum("displayname",'Signal after shaping','fignum',2);
% Digi_sig = Digi_sig.resample("fs_out",fsym);
Pform = Pulseformer("fsym",fsym,"fs",fsym,"alpha",0.6,"pulse","rrc","pulselength",64,"matched_sps",4,"output_sps",2);
Pform.process(symbols);
MF = Pulseformer("fsym",fsym,"fdac",fdac,"pulse","rrc","pulselength",1024,"alpha",rcalpha,"matched",0);
Digi_sig = MF.process(Digi_sig);

24
test/test_mapping_eth.m
View File

@@ -1,21 +1,32 @@
% Setup PRBS parameters
O = 12;
M = 2;
O = 6;
M = 6;
N = 2^(O-1); % Length of PRBS
randkey = 1; % Random key for random stream
use_eth_mapping =1;
if M ~= 6
dimension = log2(M);
else
dimension = 5;
end
[~, seed] = prbs(O, 1); % Initialize first seed of PRBS
bitpattern = [];
s = RandStream('twister', 'Seed', randkey);
for i = 1:log2(M)
for i = 1:dimension
bitpattern(:, i) = randi(s, [0 1], N, 1);
end
if M == 6
bitpattern = reshape(bitpattern',[],1);
bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
end
Tx_bits = Informationsignal(bitpattern);
Digi_Mod = PAMmapper(M, 0,"eth_style",1);
Digi_Mod = PAMmapper(M, 0,"eth_style",use_eth_mapping);
% Map bits to symbols
Symbols = Digi_Mod.map(Tx_bits);
@@ -23,7 +34,6 @@ Symbols = Digi_Mod.map(Tx_bits);
% Demap symbols back to bits
Rx_bits = Digi_Mod.demap(Symbols);
[~, error_num, ber, ~] = calc_ber(Tx_bits.signal, Rx_bits.signal, ...
"skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
[~, error_num, ber, ~] = calc_ber(Tx_bits.signal(1:length(Rx_bits.signal)), Rx_bits.signal,"skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
fprintf('BER VNLE: %.1E \n',ber);
fprintf('BER: %.1E \n',ber);