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Many changes for 400G DSP

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Minimal Example
...
This commit is contained in:
sioe
2024-12-17 16:17:58 +01:00
parent 397cfa61dd
commit e47a4dbbbe
68 changed files with 2749 additions and 2948 deletions
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Classes/00_signals/Frame 1.png
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131
Classes/00_signals/Signal.m
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@@ -349,13 +349,13 @@ classdef Signal
% spectrum_plot(obj.signal,options.fsamp,options.figurename,options.displayname);
N = 2^(nextpow2(length(obj.signal))-2);
N = 2^(nextpow2(length(obj.signal))-9);
if options.normalizeToNyquist==0
[p_lin,w] = pwelch(obj.signal,hanning(N),N/2,N,obj.fs,"centered","power","mean");
[p_lin,w] = pwelch(obj.signal,hanning(N),N/2,N,obj.fs,"centered","psd","mean");
w=w.*1e-9;
else
[p_lin,w] = pwelch(obj.signal,hanning(N),N/2,N,"centered","power","mean");
[p_lin,w] = pwelch(obj.signal,hanning(N),N/2,N,"centered","psd","mean");
% p_lin = smooth(p_lin,0.05,'rloess');
end
@@ -365,7 +365,7 @@ classdef Signal
p_dbm = 10*log10(p_lin); %dB to dBm in case of "power"
ylab = "normalized to 0 dB";
else
p_dbm = 10*log10(p_lin)+30; %dB to dBm in case of "power"
p_dbm = 10*log10(p_lin); %dB to dBm in case of "power"
ylab = "Power (dBm)";
end
@@ -401,6 +401,125 @@ classdef Signal
end
function move_it_spectrum(obj,options)
arguments
obj
options.fignum
options.displayname = "";
options.color = [];
options.normalizeToNyquist = 0;
options.normalizeTo0dB = 0;
end
data_in = obj.signal;
if size(data_in,1) > size(data_in,2)
data_in = data_in';
end
for pol = 1:size(data_in,1)
%compute FFT of input
Data_in = fft( data_in(pol,:) );
%psd = Data_in.*conj(Data_in);
psd = Data_in;
%Use only magnitude of FFT (which was complex)
psd = abs(psd);
%Shift the spectrum to yield
psd = fftshift(psd);
%divide by N
psd = psd/length(data_in(pol,:));
psd_plot = 20*log10(psd);
% psd_plot = psd_plot - max(psd_plot);
%smoothing
% psd_smoothed = smooth(psd,1000);
%
% psd_smoothed = 10*log10(psd_smoothed);
% psd_smoothed = psd_smoothed - max(psd_smoothed);
carrier_power_time_dbm = 20*log10( mean(abs(data_in)) .^2 )+30; % dB -> +30 -> dBm
carrier_power_freq_dbm = max(psd_plot);
% psd_plot = psd_plot - max(psd_plot);
%% cspr
c = mean(data_in).^2;
s = mean(data_in.^2);
cspr = 10*log10(c / s);
testParseval = 1;
if testParseval == 1
E_FreqDomain =1/length(psd) * sum((psd.*length(psd)).^2);
%test parseval
E_TimeDomain = sum( (data_in(pol,:).^2) );
if isequal(round(E_FreqDomain,1),round(E_TimeDomain,1))
disp('Parseval is right!');
else
% disp('Something is wrong here?!');
end
end
figure(options.fignum); % If figure does not exist, create new figure
if 1
%Frequency Axis
freq_vec = linspace(-obj.fs/2,obj.fs/2,length(psd));
freq_vec = reshape(freq_vec,size(psd_plot));
if nargin == 4
p = plot(freq_vec*1e-9,psd_plot,'Linewidth',0.5,'DisplayName',options.displayname);
% plot(freq_vec*1e-9,psd_smoothed','Linewidth',1,'Color',[0 0 0],'DisplayName',[char(varargin{2}),' smoothed']);
else
p = plot(freq_vec*1e-9,psd_plot,'Linewidth',0.5);
% plot(freq_vec*1e-9,psd_smoothed','Linewidth',1,'Color',[1 1 1],'LineStyle',':');
end
%xlim([freq_vec(1)/1e9-2 freq_vec(end)/1e9+2])
xlabel('frequency [GHz]')
else
%Wavelength Axis
freq_vec = physconst('LightSpeed')*linspace(-obj.fs/2,obj.fs/2,length(psd))./((physconst('LightSpeed')/1310e-9)^2)*1e9;
freq_vec = freq_vec+1310;
if nargin == 4
plot(freq_vec',psd_plot,'Linewidth',0.5,'DisplayName',options.displayname)
else
plot(freq_vec,psd_plot','Linewidth',0.5);
end
%xlim([freq_vec(1)/1e9-2 freq_vec(end)/1e9+2])
xlabel('wavelength [nm]')
end
hold on
end
% xlim([-150 150])
% ylim([-100,0]);
ylabel('magnitude [dBm]')
legend
grid minor;
end
%% Power of signal
function pow = power(obj,options)
@@ -544,7 +663,7 @@ classdef Signal
%Cut occurences of ref signal from signal (only positive shifts)
S = {};
for c = shifts(shifts>0)
for c = shifts(shifts>=0)
sig = obj.delay(-c,'mode','samples');
sig.signal = sig.signal(1:length(b));
S{end+1,1} = sig;
@@ -557,7 +676,7 @@ classdef Signal
end
%return/keep the sinal with the highest correlation (only within positive shifts)
[~,idx]=max(pks(shifts>0));
[~,idx]=max(pks(shifts>=0));
obj.signal = S{idx}.signal;
%put signal with highest corr. to first index in S array
swap = S{1};

10
Classes/01_transmit/AWG.m
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@@ -8,6 +8,7 @@ classdef AWG < handle
upsampling_method
repetitions %repeat the signal to generate a longer sequence?
fdac %needed
precomp_sinc_rolloff = 1;
normalize2dac %want to normalize at first? either 0 or 1
bit_resolution %bit res. of quantizer (e.g. 5 bit)
dac_min
@@ -33,7 +34,8 @@ classdef AWG < handle
arguments
options.kover = 16;
options.upsampling_method upsampling_mode
options.upsampling_method upsampling_mode = upsampling_mode.samplehold;
options.precomp_sinc_rolloff = 1;
options.repetitions = 1;
options.normalize2dac = 1;
options.fdac = 92e9;
@@ -43,7 +45,7 @@ classdef AWG < handle
options.skew_active = 0;
options.awg_skew = 0;
options.lpf_active = 0;
options.lpf_type = 0;
options.lpf_type = filtertypes.butterworth;
options.f_cutoff = 32e9;
options.H_lpf Filter
@@ -95,7 +97,7 @@ classdef AWG < handle
signalclass_in = obj.H_lpf.process(signalclass_in);
else
%4.B) just use a standard filter
lpf = Filter('filtdegree',5,"f_cutoff",obj.f_cutoff,"fsamp",obj.kover*obj.fdac,"filterType",obj.lpf_type);
lpf = Filter('filtdegree',5,"f_cutoff",obj.f_cutoff,"fs",obj.kover*obj.fdac,"filterType",obj.lpf_type);
signalclass_in = lpf.process(signalclass_in);
end
end
@@ -133,7 +135,7 @@ classdef AWG < handle
obj.signal_length = length(data_in);
%%%%%%%%% PRECOMP SINC ROLLOFF %%%%%%%%%
if 1
if obj.precomp_sinc_rolloff
% X: design FIR filter for sinc precomp
% https://www.dsprelated.com/showarticle/1191.php
ntaps = 13;

6
Classes/01_transmit/PAMsource.m
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@@ -151,6 +151,12 @@ classdef PAMsource
sym_max = max(symbols.signal);
end
% symbols.move_it_spectrum("fignum",222,"displayname","Symbols only");
% symbols.spectrum("fignum",222,"displayname","Symbols only","normalizeTo0dB",1);
%%%%% Pulse-forming %%%%%%
if obj.applypulseform
digi_sig = obj.pulseformer.process(symbols);

21
Classes/04_DSP/Coding/Duobinary.m
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@@ -1,10 +1,9 @@
classdef Duobinary
%Duobinary Coding
% should work
% should work
properties(Access=public)
end
methods (Access=public)
@@ -12,6 +11,7 @@ classdef Duobinary
function obj = Duobinary()
%NAME Construct an instance of this class
end
function signal = precode(~,signal)
@@ -52,7 +52,7 @@ classdef Duobinary
% end
for k = 2:numel(data)
bk(k) =mod(data(k)-bk(k-1),M);
bk(k) = mod(data(k)-bk(k-1),M);
end
%make bipolar
@@ -80,6 +80,11 @@ classdef Duobinary
function signal = encode(~,signal)
arguments
~
signal
end
if isa(signal,'Signal')
data = signal.signal;
else
@@ -138,6 +143,8 @@ classdef Duobinary
data = data ./ sqrt(5.8);
elseif M == 8
data = data ./ sqrt(10.5); % 15-level constellation weighted with probability after DB code i.e.
else
errormsg("Error in: Duobinary encode > scale unipolar to bipolar > The data is not PAM4, PAM6 or PAM 8? ")
end
if isa(signal,'Signal')
@@ -169,13 +176,9 @@ classdef Duobinary
if I == 7
data = data .* sqrt(2.5);
elseif I == 11
%todo
data = data .* sqrt(5.8);
warning('Check db decode implementation, mapping and scaling is correct!')
elseif I == 15
data = data .* sqrt(10.5);
elseif I == 16
warning('Check db decode implementation, mapping and scaling is correct!')
end
data = round(data);
@@ -196,6 +199,8 @@ classdef Duobinary
data = data ./ sqrt(10);
elseif M == 8
data = data ./ sqrt(21);
else
errormsg("Error in: Duobinary decode > scale unipolar to bipolar > The data is not PAM4, PAM6 or PAM 8? ")
end
signalclass.signal = data;

6
Classes/04_DSP/Equalizer/Postfilter.m
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@@ -45,14 +45,14 @@ classdef Postfilter < handle
function showFilter(obj,noiseclass_in)
noiseclass_in.spectrum('displayname','Noise PSD shifted to 0dBm','fignum',123,'normalizeTo0dB',1);
noiseclass_in.spectrum('displayname','Noise PSD shifted to 0dBm','fignum',121,'normalizeTo0dB',1);
[h,w] = freqz(1,obj.burg_coeff,length(noiseclass_in),"whole",noiseclass_in.fs);
h = h/max(abs(h));
hold on
w_ = (w - noiseclass_in.fs/2);
plot(w_.*1e-9,20*log10(fftshift(h)),'DisplayName',['Burg Coeffs: ', num2str(obj.burg_coeff), ' ']);
plot(w_.*1e-9,20*log10(fftshift(abs(h))),'DisplayName',['Burg Coeffs: ', num2str(obj.burg_coeff), ' ']);
ylim([-30,2]);
end
end

7
Classes/DataBaseHandler/DBHandler.m
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@@ -159,7 +159,7 @@ classdef DBHandler < handle
duplictae_sync = obj.fetch("SELECT COALESCE(Runs.rx_sync_path,'NaN') AS rx_sync_path, COUNT(*) AS occurrences FROM Runs GROUP BY rx_sync_path HAVING COUNT(*) > 1");
if size(duplictae_raw,2) > 1
for i = 1:size(duplictae_raw,2)-1
for i = 1:size(duplictae_raw,1)
fprintf('Raw Rx Paths: Found %d duplictaes of %s \n',duplictae_raw.occurrences(i),duplictae_raw.rx_raw_path(i));
end
end
@@ -349,6 +349,11 @@ classdef DBHandler < handle
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);

12
Classes/Warehouse_class/classes/DataStorage.m
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@@ -114,6 +114,12 @@ classdef DataStorage < handle
end
function addValueToStorageByLinIdx(obj, valueToStore ,storageVarName, lin_idx)
obj.sto.(storageVarName){lin_idx} = valueToStore;
end
% Access Value(s)
function value = getStoValue(obj,storageVarName, varargin)
@@ -270,7 +276,7 @@ classdef DataStorage < handle
function phys_indices = getPhysIndicesByLinIndex(obj, lin_idx)
function [phys_indices,param_name] = getPhysIndicesByLinIndex(obj, lin_idx)
% Converts a linear index into the corresponding physical parameter values
% Inputs:
% - lin_idx: The linear index within the storage array
@@ -285,8 +291,8 @@ classdef DataStorage < handle
% Map subscripts to physical values for each parameter
for i = 1:numel(obj.fn)
param_name = obj.fn(i);
phys_indices{i} = obj.parameter.(param_name).getPhysForIndex(subscripts{i});
param_name{i} = obj.fn(i);
phys_indices{i} = obj.parameter.(param_name{i}).getPhysForIndex(subscripts{i});
end
end

114
Classes/Warehouse_class/classes/DataStorage2.m
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@@ -1,114 +0,0 @@
classdef DataStorage2 < handle
% DATASTORAGE: Stores data with physical parameter mappings
properties
inputParams = struct;
parameter = struct;
fn = [];
dim = [];
sto = struct;
end
methods
function obj = DataStorage2(inputParams)
% Constructor to initialize the DataStorage object
if nargin > 0
obj.inputParams = inputParams;
obj.fn = string(fieldnames(inputParams));
obj = obj.buildParameter();
obj.dim = obj.getDimension();
obj.sto = struct;
else
error('Input parameters are required.');
end
end
function showInfo(obj)
% Displays information about the storage and its dimensions
disp("Data Structure with fields:");
fprintf('%-12s | %-8s | %-12s\n', 'Name', 'Dimension', 'Physical Values');
disp('-------------------------------------------------------');
for i = 1:numel(obj.fn)
fprintf('%-12s | %-8d | %-12s\n', ...
char(obj.fn(i)), obj.dim(i), ...
strjoin(string(obj.parameter.(obj.fn(i)).values), ', '));
end
disp('-------------------------------------------------------');
end
function dim = getDimension(obj)
% Get the dimensions based on the length of parameters
dim = zeros(1, numel(obj.fn));
for p = 1:numel(obj.fn)
dim(p) = obj.parameter.(obj.fn(p)).length;
end
end
function obj = buildParameter(obj)
% Build the Parameter objects for each input parameter
for p = 1:numel(obj.fn)
name = obj.fn(p);
values = obj.inputParams.(name);
obj.parameter.(name) = Parameter2(name, values);
end
end
function addStorage(obj, varName)
% Create an empty storage for a specific variable name
obj.sto.(string(varName)) = cell(obj.dim);
end
function addValueToStorage(obj, valueToStore, storageVarName, varargin)
% Add a value to the storage at the specified indices
if nargin - 3 == numel(obj.fn)
lin_idx = obj.getIndicesByPhys(varargin);
obj.sto.(storageVarName){lin_idx} = valueToStore;
else
error('Please provide all indices for the storage.');
end
end
function value = getStoValue(obj, storageVarName, varargin)
% Retrieve a value from storage based on physical parameters
if nargin - 2 == numel(obj.fn)
lin_idx = obj.getIndicesByPhys(varargin);
value = cell(1, numel(lin_idx));
for i = 1:numel(lin_idx)
value{i} = obj.sto.(storageVarName){lin_idx(i)};
end
value = value(~cellfun('isempty', value)); % Remove empty entries
else
error('Please provide all physical parameters.');
end
end
function lin_idx = getIndicesByPhys(obj, varargin)
% Unpack nested cell array if needed
if numel(varargin) == 1 && iscell(varargin{1})
varargin = varargin{1}; % Unpack if single cell array is passed
end
indices = cell(1, numel(obj.fn));
% Loop through each parameter (e.g., L, D)
for p = 1:numel(obj.fn)
% Unwrap if it's a cell
if iscell(varargin{p})
physVal = varargin{p}{1}; % Extract scalar from cell
else
physVal = varargin{p}; % It's already a scalar
end
paramName = obj.fn(p); % Get the parameter name (e.g., 'L' or 'D')
% Call getIndexByPhys on the corresponding Parameter2 object
indices{p} = obj.parameter.(paramName).getIndexByPhys(physVal);
end
% Convert subscript indices to a linear index
lin_idx = sub2ind(obj.dim, indices{:});
end
end
end

51
Classes/Warehouse_class/classes/Parameter2.m
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@@ -1,51 +0,0 @@
classdef Parameter2 < handle
% PARAMETER2: Represents a physical parameter with mappings between values and indices
properties
name
values
length
physToIndexMap % Rename this from 'getPhysForIndex'
indexToPhysMap % Rename this from 'getIndexForPhys'
end
methods
function obj = Parameter2(name, values)
% Constructor to initialize the Parameter2 object
obj.name = name;
obj.values = values;
obj.length = numel(values);
% Initialize the mappings
obj.physToIndexMap = containers.Map('KeyType', 'double', 'ValueType', 'any');
obj.indexToPhysMap = containers.Map('KeyType', 'double', 'ValueType', 'any');
obj = obj.buildMappings();
end
function obj = buildMappings(obj)
% Build mappings between physical values and indices
for idx = 1:obj.length
obj.indexToPhysMap(idx) = obj.values(idx);
obj.physToIndexMap(obj.values(idx)) = idx;
end
end
function physVal = getPhysForIndex(obj, idx)
% Return the physical value corresponding to the index
if isKey(obj.indexToPhysMap, idx)
physVal = obj.indexToPhysMap(idx);
else
error('Index out of range for parameter %s', obj.name);
end
end
function idx = getIndexByPhys(obj, physVal)
% Return the index corresponding to the physical value
if isKey(obj.physToIndexMap, physVal)
idx = obj.physToIndexMap(physVal);
else
error('Physical value %g not found in parameter %s', physVal, obj.name);
end
end
end
end

45
Classes/Warehouse_class/classes/minimalExample_gen2.m
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@@ -1,45 +0,0 @@
% Define input parameters for the DataStorage2
inputParams.L = [1, 2, 10, 80]; % Length in kilometers
inputParams.D = [16, 17, 18]; % Diameter in millimeters
% Create a DataStorage2 instance with the input parameters
dataStorage = DataStorage2(inputParams); % Using DataStorage2 class
% Display the current information about the data storage structure
dataStorage.showInfo();
% Add a storage variable named 'testStorage'
dataStorage.addStorage('testStorage');
% Add a value (e.g., 100) to the storage at specific physical parameter values
% For example, we store the value 100 at L = 10 km and D = 17 mm
dataStorage.addValueToStorage(100, 'testStorage', 10, 16);
dataStorage.addValueToStorage(100, 'testStorage', 10, 17);
dataStorage.addValueToStorage(100, 'testStorage', 10, 18);
% Retrieve the value from the storage at the same physical parameter values
storedValue = dataStorage.getStoValue('testStorage', 10, 16:18);
disp('Retrieved value from storage:');
disp(storedValue);
% Retrieve another value at a non-existent location (L = 2 km, D = 8 mm)
% This will show how the function handles empty storage entries
nonExistentValue = dataStorage.getStoValue('testStorage', 2, 8);
disp('Retrieved value from empty location:');
disp(nonExistentValue);
% Use the internal mappings to check how physical values map to indices
% Get the linear index for physical values L = 10 km and D = 17 mm
lin_idx = dataStorage.getIndicesByPhys(10, 17);
disp('Linear index for L=10 km and D=17 mm:');
disp(lin_idx);
% Check the reverse mapping: physical value for index 2 of parameter L
physValForIndex = dataStorage.parameter.L.getPhysForIndex(2);
disp('Physical value for index 2 of parameter L:');
disp(physValForIndex);
% Check the mapping: index for physical value D = 21 mm
indexForPhys = dataStorage.parameter.D.getIndexByPhys(21);
disp('Index for physical value D=21 mm:');
disp(indexForPhys);

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Classes/matlab.mat
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1
Datatypes/db_mode.m
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@@ -4,6 +4,7 @@ classdef db_mode < int32
no_db (0)
db_precoded (1)
db_encoded (2)
db_emulate (3)
end
end

13
Functions/EQ_structures/vnle.m
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@@ -1,4 +1,4 @@
function [eq_signal,eq_noise,ber,numErrors] = vnle(EQ,M,rx_signal,tx_symbols,tx_bits)
function [eq_signal,eq_noise,ber,numErrors] = vnle(EQ,M,rx_signal,tx_symbols,tx_bits,emulate_precode)
%VNLE Apply an equalization algorithm to the received signal and calculate BER
% This function takes an equalizer object, a received signal, and the
% transmitted symbols to apply equalization, map the received signal back to bits,
@@ -16,6 +16,17 @@ function [eq_signal,eq_noise,ber,numErrors] = vnle(EQ,M,rx_signal,tx_symbols,tx_
%FFE or VNLE
[eq_signal,eq_noise] = EQ.process(rx_signal,tx_symbols);
eq_signal = PAMmapper(M,0).quantize(eq_signal);
if emulate_precode
eq_signal = Duobinary().encode(eq_signal);
eq_signal = Duobinary().decode(eq_signal);
tx_symbols= Duobinary().encode(tx_symbols);
tx_symbols = Duobinary().decode(tx_symbols);
tx_bits = PAMmapper(M,0).demap(tx_symbols);
end
% M = numel(unique(tx_symbols.signal));
rx_bits = PAMmapper(M,0).demap(eq_signal);

3
Functions/EQ_structures/vnle_postfilter_mlse.m
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@@ -3,12 +3,15 @@ function [eq_signal,eq_noise,ber,numErrors] = vnle_postfilter_mlse(eq_,pf_,mlse_
%FFE or VNLE
[eq_signal,eq_noise] = eq_.process(rx_signal,tx_symbols);
% eq_noise.signal = eq_noise.signal - movmean(eq_noise.signal,[5000,0]);
eq_signal = pf_.process(eq_signal,eq_noise);
%M = numel(unique(tx_symbols.signal));
mlse_.DIR = pf_.burg_coeff;
mlse_.trellis_states = PAMmapper(M,0).levels;
mlse_.M = M;
eq_signal = mlse_.process(eq_signal);
rx_bits = PAMmapper(M,0).demap(eq_signal);

57
Functions/beautifyBERplot.m Normal file
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@@ -0,0 +1,57 @@
function beautifyBERplot()
% BEAUTIFYBERPLOT Enhances a BER plot for publication-quality figures.
%
% This function automatically adjusts the aesthetics of an existing plot.
% It sets limits based on the data already plotted and modifies the
% appearance for publication-ready figures.
% Set line properties for all current plot lines
lines = findall(gca, 'Type', 'Line');
markers = {'o', 's', 'd', '^', 'v', '>', '<', 'p', 'h'}; % Define marker styles
num_markers = length(markers);
for i = 1:length(lines)
lines(i).LineWidth = 1.5; % Set a thicker line width
lines(i).LineStyle = '-'; % Solid lines for simplicity
lines(i).Marker = markers{mod(i-1, num_markers) + 1}; % Assign markers cyclically
lines(i).MarkerSize = 6; % Set marker size
lines(i).MarkerFaceColor = 'auto'; % Use line color for marker face
end
% Change all text interpreters to LaTeX
set(findall(gca, '-property', 'Interpreter'), 'Interpreter', 'latex');
% Set figure background to white
set(gcf, 'Color', 'w');
% Set axis labels with LaTeX
xlabel('Bit Rate in Gbps', 'Interpreter', 'latex');
ylabel('Bit Error Rate', 'Interpreter', 'latex');
% Determine x and y limits from the data in the plot
x_data = [];
y_data = [];
for i = 1:length(lines)
x_data = [x_data; lines(i).XData(:)]; %#ok<AGROW> % Append x data
y_data = [y_data; lines(i).YData(:)]; %#ok<AGROW> % Append y data
end
x_min = min(x_data);
x_max = max(x_data);
y_min = 1e-4; % Ensure no negative or zero values for log scale
y_max = 0.5;
% Adjust axis ranges
xlim([x_min, x_max]);
ylim([y_min, y_max]);
% Set logarithmic scale for y-axis
set(gca, 'YScale', 'log');
% Customize grid and box appearance
set(gca, 'Box', 'on', 'LineWidth', 1.2); % Add a thicker border
grid on;
grid minor;
% Adjust font size and style for better readability
set(gca, 'FontSize', 12, 'FontName', 'Times New Roman');
end

5
Functions/channel_structures/awgn_channel.m Normal file
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@@ -0,0 +1,5 @@
function signal_out = awgn_channel(signal_in)
signal_out = signal_in;
end

76
Functions/copyStylingFrom.m Normal file
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@@ -0,0 +1,76 @@
function copyStylingFrom(figNumSource, figNumTgt)
% Get handles to the source and target figures
sourceFig = figure(figNumSource);
targetFig = figure(figNumTgt);
% Get axes of source and target figures
sourceAxes = findall(sourceFig, 'type', 'axes');
targetAxes = findall(targetFig, 'type', 'axes');
% Ensure the number of axes match
if length(sourceAxes) ~= length(targetAxes)
error('Number of axes in source and target figures must be the same.');
end
% Loop through each pair of axes and copy styling properties
for i = 1:length(sourceAxes)
copyAxesProperties(sourceAxes(i), targetAxes(i));
end
% Apply general figure properties if desired
targetFig.Color = sourceFig.Color; % Background color
end
function copyAxesProperties(sourceAx, targetAx)
% List of properties to copy from source to target axes
propsToCopy = {'XColor', 'YColor', 'ZColor', 'FontSize', 'FontName', ...
'GridColor', 'GridLineStyle', 'MinorGridColor', 'Box', ...
'XGrid', 'YGrid', 'ZGrid', 'XMinorGrid', 'YMinorGrid', 'ZMinorGrid', ...
'LineWidth', 'TitleFontSizeMultiplier', 'LabelFontSizeMultiplier'};
% Copy properties from source to target
for i = 1:length(propsToCopy)
try
targetAx.(propsToCopy{i}) = sourceAx.(propsToCopy{i});
catch
% Skip property if it doesn't exist or can't be copied
end
end
% Copy axis labels and titles
targetAx.Title.String = sourceAx.Title.String;
targetAx.XLabel.String = sourceAx.XLabel.String;
targetAx.YLabel.String = sourceAx.YLabel.String;
targetAx.ZLabel.String = sourceAx.ZLabel.String;
% Copy children elements like lines, patches, etc.
sourceChildren = allchild(sourceAx);
targetChildren = allchild(targetAx);
% Ensure the number of children elements match
if length(sourceChildren) ~= length(targetChildren)
warning('Number of elements in source and target axes differ. Styling may not be applied completely.');
end
% Copy properties of children (like lines, patches, etc.), except colors and legends
for i = 1:min(length(sourceChildren), length(targetChildren))
copyObjectProperties(sourceChildren(i), targetChildren(i));
end
end
function copyObjectProperties(sourceObj, targetObj)
% List of common properties to copy for plot elements (lines, patches, etc.)
propsToCopy = {'LineStyle', 'LineWidth', 'Marker', 'MarkerSize', ...
'MarkerEdgeColor', 'MarkerFaceColor', 'DisplayName'};
% Copy properties from source to target, excluding colors
for i = 1:length(propsToCopy)
try
if ~contains(propsToCopy{i}, 'Color') % Skip color properties
targetObj.(propsToCopy{i}) = sourceObj.(propsToCopy{i});
end
catch
% Skip property if it doesn't exist or can't be copied
end
end
end

0
projects/HighSpeedExperiment_2024/removeDotsFromFileNames.m → Functions/removeDotsFromFileNames.m
View File

290
db_eq_mpi.tex
View File

@@ -1,290 +0,0 @@
% This file was created by matlab2tikz.
%
%The latest updates can be retrieved from
% http://www.mathworks.com/matlabcentral/fileexchange/22022-matlab2tikz-matlab2tikz
%where you can also make suggestions and rate matlab2tikz.
%
\definecolor{mycolor1}{rgb}{0.89020,0.10196,0.10980}%
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ylabel style={font=\color{white!15!black}},
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]
\end{axis}
\end{tikzpicture}%

276
db_eq_wo_mpi.tex
View File

@@ -1,276 +0,0 @@
% This file was created by matlab2tikz.
%
%The latest updates can be retrieved from
% http://www.mathworks.com/matlabcentral/fileexchange/22022-matlab2tikz-matlab2tikz
%where you can also make suggestions and rate matlab2tikz.
%
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%
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295
db_noise_mpi.tex
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299
db_noise_wo_mpi.tex
View File

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186
pam6_db.tex
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196
pam6_pf.tex
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width=1.227\fwidth,
height=1.227\fheight,
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scale only axis,
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xmax=1,
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axis line style={draw=none},
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ymajorgrids
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175
projects/A_minimal_example/imdd_simulation_minimal.m Normal file
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function [ber] = imdd_simulation_minimal(varargin)
% BASIC IMDD Model...
% varargin is either empty or a struct, i.e.:
% optionalvars = struct('bitrate',300e9,'M',6);
% then:
% imdd_simulation_minimal(optionalvars)
% replaces the respective variables -> nice for looping parameter sets
curFolder = pwd;
funcFolder=fileparts(mfilename('fullpath'));
if ~isempty(funcFolder)
cd(funcFolder);
end
% TX
M = 4;
fsym = 112e9;
apply_pulsef = 1;
fdac = 256e9;
fadc = 256e9;
random_key = 1;
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 = 1310;
laser_linewidth = 0;
tx_bw_nyquist = 0.95;
% Channel
link_length = 1; %km
% RX
rop = -7;
rx_bw_nyquist = 0.9;
% EQ
vnle_order=[50,7,7];
dfe_order = [2 0 0];
len_tr = 4096*2;
mu_ffe = [0.0004 0.0004 0.0004];
mu_dfe = 0.0004;
mu_dc = 0.00;
% Replace optional input arguments if there are any
if ~isempty(varargin)
var_s = varargin{1};
if isstruct(var_s)
fields = fieldnames(var_s);
for i = 1:numel(fields)
eval([fields{i}, ' = ', num2str( var_s.(fields{i}) ), ';']);
fprintf("%s <-- %.2f \n", fields{i}, var_s.(fields{i}));
end
else
error('Optional variables should be passed as a struct.');
end
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;
%%%% TX Signal
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"rrcalpha",rcalpha);
[Digi_sig,Tx_symbols,Tx_bits] = PAMsource(...
"fsym",fsym,"M",M,"order",18,"useprbs",1,...
"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",'Digital Spectrum at TX','fignum',10,'normalizeTo0dB',1);
%%%%% AWG
% El_sig = M8199A("kover",kover).process(Digi_sig);
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover,"bit_resolution",12).process(Digi_sig);
% El_sig.spectrum("displayname",'Digi Spectrum','fignum',100,'normalizeTo0dB',0);
El_sig = El_sig.setPower(0,"dBm");
El_sig.eye(fsym,M,"fignum",11,'displayname','Rx Eye');
%%%%% Low-pass el. components %%%%%%
El_sig = Filter('filtdegree',4,"f_cutoff",75e9,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(El_sig);
%%%%% Electrical Driver Amplifier %%%%%%
El_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",3).process(El_sig);
%%%%% MODULATE E/O CONVERSION %%%%%%
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1).process(El_sig);
%%%%%% Fiber %%%%%%
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
%%%%%% ROP %%%%%%
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
%%%%%% PD Square Law %%%%%%
Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
Rx_sig.eye(fsym,M,"fignum",30,'displayname','Rx Eye');
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
Rx_sig = Filter('filtdegree',4,"f_cutoff",75e9,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(Rx_sig);
%%%%%% Low-pass inside Scope %%%%%%
Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
%%%%%% Scope %%%%%%
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);
Scpe_sig.spectrum("displayname",'Digital (256 GSa/s) Rx Spectrum','fignum',10,'normalizeTo0dB',1);
Scpe_sig.normalize("mode","rms").plot("displayname",'Digital (256 GSa/s) Rx Spectrum','fignum',21,'clear',1);
%%%%%% Sample to 2x fsym %%%%%%
Scpe_sig = Scpe_sig.resample("fs_in",fadc,"fs_out",2*fsym);
%%%%%% Sync Rx signal with reference %%%%%%
[Scpe_sig,S] = Scpe_sig.tsynch("reference",Tx_symbols,"fs_ref",fsym);
%%% EQUALIZING
%FFE or VNLE
[Eq_signal,Eq_noise] = 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).process(Scpe_sig,Tx_symbols);
Rx_bits = PAMmapper(M,0).demap(Eq_signal);
[~,numErrors,ber,~] = calc_ber(Rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
Eq_signal.normalize("mode","rms").plot("displayname",'Digital (256 GSa/s) Rx Spectrum','fignum',21,'clear',0);
%%% Visualize EQ Stuff
figure(40);
clf
title(sprintf('PAM %d after EQ ; BER: %1.2e',M, ber ));
constellation = unique(Tx_symbols.signal);
received = NaN(numel(constellation),length(Tx_symbols));
for lvl = 1:numel(constellation)
%Separate the equalized signal into the
%respective levels based on the actually
%transmitted level!
received(lvl,Tx_symbols.signal==constellation(lvl)) = Eq_signal.signal(Tx_symbols.signal==constellation(lvl));
intermediate = received(lvl,:);
cnt(lvl) = numel(intermediate(~isnan(intermediate)));
hold on
histogram(received(lvl,:),1000,"EdgeAlpha",0,'DisplayName',['Lvl ',num2str(lvl),' | ',num2str(cnt(lvl)),' entries']);
end
legend
fprintf('BER: %.2e \n',ber);
autoArrangeFigures;
if ~isempty(curFolder)
cd(curFolder);
end
end

23
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uloops = struct;
uloops.bitrate = [300,330,360,390,420,450,480].*1e9;
uloops.M = [4,6,8];
wh = DataStorage(uloops);
wh.addStorage("ber");
fprintf("Let's simulate %d configuratiuons! \n",wh.getLastLinIndice);
wh = submit_simulations(wh,"parallel",1);
for m_ = uloops.M
ber_row = wh.getStoValue('ber', uloops.bitrate,m_);
figure(2024)
hold on
plot(uloops.bitrate.*1e-9,ber_row,'DisplayName',sprintf('PAM %d',m_));
end
yline(3.8e-3,'LineWidth',2,'DisplayName','3.8e-3');
yline(2e-2,'LineWidth',2,'LineStyle','--','DisplayName','2e-2');
beautifyBERplot()
legend

72
projects/A_minimal_example/submit_simulations.m Normal file
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function wh = submit_simulations(wh,options)
arguments
wh
options.parallel = 1;
end
%%% 2) SUBMIT SIMULATION
% Initialize job results
if options.parallel
if isempty(gcp('nocreate'))
parpool;
end
results = parallel.FevalFuture.empty();
else
results = [];
end
lin_idx = 1;
for lin_idx = 1:wh.getLastLinIndice
optionalVars = struct();
if ~isempty(wh.getDimension)
% Build the optionalVars struct
[parametervalues,parameternames]=wh.getPhysIndicesByLinIndex(lin_idx);
for pidx = 1:numel(parameternames)
optionalVars.(parameternames{pidx}) = parametervalues{pidx};
end
end
%%% SIMULATION HERE
if options.parallel
numOutputs = 1;
results(lin_idx) = parfeval(@imdd_simulation_minimal, numOutputs, optionalVars);
else
finalresults{lin_idx} = imdd_simulation_minimal(optionalVars);
wh.addValueToStorageByLinIdx(finalresults{lin_idx}, 'ber', lin_idx);
end
end
if options.parallel
%%% 4) Setup waitbar
h = waitbar(0, 'Processing Simulations...');
% Helper function to compute progress
updateWaitbar = @(~) waitbar(mean(arrayfun(@(f) strcmp(f.State, 'finished'), results)), h);
fprintf('Fetching results... \n');
% Update the waitbar after each simulation
updateWaitbarFutures = afterEach(results, updateWaitbar, 0);
% Close the waitbar after all simulations complete
afterAll(updateWaitbarFutures, @(~) delete(h), 0);
%%% 7) Fetch final results after all computations
fetchOutputs(results);
for ridx = 1:length(results)
wh.addValueToStorageByLinIdx(results(ridx).OutputArguments{1}, 'ber', ridx);
end
end
end

150
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projects/HighSpeedExperiment_2024/auswertung/analyzeDB.m
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basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
useGui = 0;
db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
if useGui
filterParams = db.promptFilterParameters();
selectedFields = db.promptSelectFields();
else
filterParams = db.tables;
filterParams.Configurations = struct( ...
'bitrate', [], ...
'db_mode', [], ...
'fiber_length', 10, ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 0, ...
'pam_level', [], ...
'precomp_amp', [], ...
'rop_attenuation', 0, ...
'symbolrate', [], ...
'v_awg', [], ...
'v_bias', [], ...
'wavelength', 1310 ...
);
filterParams.Equalizer.eq_type = equalizer_structure.vnle_pf_mlse;
filterParams.Equalizer.eq_id = equalizer_structure.vnle;
selectedFields = {'Runs.run_id','BERs.ber_id','Equalizer.eq_id','Equalizer.eq_type','BERs.ber','BERs.occurrence',...
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp',...
'Measurements.power_rop','Measurements.power_laser','Measurements.power_pd_in'};
end
[dataTable,~] = db.queryDB(filterParams, selectedFields);
% Calculate the mean BER for each combination of 'run_id' and 'eq_type'
groupedData = groupsummary(dataTable, {'run_id', 'eq_type'}, 'mean', {'ber', 'power_rop','power_pd_in'});
% Extract unique rows from dataTable for each run_id with relevant configuration details
uniqueConfigFields = {'run_id', 'pam_level', 'bitrate','symbolrate', 'fiber_length', 'wavelength', 'precomp_amp', 'db_mode'};
[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
configDetails = dataTable(uniqueIdx, uniqueConfigFields); % Extract unique configurations for each run_id
% Prepare the join key for both groupedData and configDetails
groupedDataTable = table(groupedData.run_id, groupedData.eq_type, groupedData.GroupCount, groupedData.mean_ber,groupedData.mean_power_rop,groupedData.mean_power_pd_in, ...
'VariableNames', {'run_id', 'eq_type', 'GroupCount', 'mean_ber','mean_power_rop','mean_power_pd_in'});
% Join groupedData with configDetails on the run_id field
joinedData = join(groupedDataTable, configDetails, 'Keys', 'run_id');
a=plot(fsym_vals.*1e-9.*log2(M_vals(modulation_iter)),ber_ffe(:,modulation_iter),...
'Color',cols(modulation_iter,:),'MarkerSize',2,'LineWidth',1,...
'Marker','o','MarkerFaceColor',cols(modulation_iter,:),'MarkerEdgeColor','black',...
'DisplayName',['PAM ',num2str(M_vals(modulation_iter))]);
a.DataTipTemplate.DataTipRows(1).Label = 'Bitr';
a.DataTipTemplate.DataTipRows(1).Format = ['%.1f',' Gbps'];
a.DataTipTemplate.DataTipRows(2).Label = 'BER';
a.DataTipTemplate.DataTipRows(2).Format ='%.1e';
a.DataTipTemplate.DataTipRows(3).Label = 'P_{out}';
a.DataTipTemplate.DataTipRows(3).Value = rop(:,modulation_iter);
a.DataTipTemplate.DataTipRows(3).Format = ['%.2f',' dBm'];
a.DataTipTemplate.DataTipRows(4).Label = 'Baudr';
a.DataTipTemplate.DataTipRows(4).Value = fsym_vals.*1e-9;
a.DataTipTemplate.DataTipRows(4).Format = ['%.1f',' GBd'];
a.DataTipTemplate.FontSize = 9;
a.DataTipTemplate.FontName = 'arial';
% Continue with the rest of your plot settings
title('Opt B2B')
yline(3.8e-3, 'DisplayName', 'HD-FEC', 'LineStyle', '--', 'HandleVisibility', 'off');
xlabel('Bit Rate in GBps');
ylabel('Bit Error Rate (BER)');
set(gca, 'yscale', 'log');
set(gca, 'Box', 'on');
grid on;
grid minor;
legend('Interpreter', 'none');

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basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
useGui = 0;
pamlvls = [4, 6, 8];
wlengths = [1310];
db = DBHandler("pathToDB", [basePath, 'silas_labor.db']);
for w = 1:numel(wlengths)
for p = 1:numel(pamlvls)
% Load data from database
joinedData = loadDataFromDB(db, pamlvls(p), wlengths(w), useGui);
% Plot filtered data
figure(pamlvls(p));
hold on;
plotFilteredData(joinedData, wlengths(w));
% Continue with the rest of your plot settings
finalizePlot();
end
end
% Custom function to load data from database
function joinedData = loadDataFromDB(db, pamlvl, wlength, useGui)
if useGui
filterParams = db.promptFilterParameters();
selectedFields = db.promptSelectFields();
else
filterParams = db.tables;
filterParams.Configurations = struct( ...
'bitrate', [], 'db_mode', [], 'fiber_length', 1, ...
'interference_attenuation', [], 'interference_path_length', [], ...
'is_mpi', 0, 'pam_level', pamlvl, 'precomp_amp', [], ...
'rop_attenuation', 0, 'symbolrate', [], 'v_awg', [], 'v_bias', [], ...
'wavelength', wlength ...
);
selectedFields = {'Runs.run_id', 'BERs.ber_id', 'Equalizer.eq_id', 'Equalizer.eq_type', 'BERs.ber', 'BERs.occurrence', ...
'Configurations.db_mode', 'Configurations.pam_level', 'Configurations.bitrate', 'Configurations.symbolrate', ...
'Configurations.fiber_length', 'Configurations.wavelength', 'Configurations.precomp_amp', ...
'Measurements.power_rop', 'Measurements.power_laser', 'Measurements.power_pd_in'};
end
% Get data table from DB
[dataTable, ~] = db.queryDB(filterParams, selectedFields);
% Extract unique rows for each run_id
uniqueConfigFields = {'run_id', 'pam_level', 'bitrate', 'symbolrate', 'fiber_length', 'wavelength', 'precomp_amp', 'db_mode'};
[~, uniqueIdx] = unique(dataTable.run_id);
configDetails = dataTable(uniqueIdx, uniqueConfigFields);
% Calculate the mean BER for each combination of 'run_id' and 'eq_type'
groupedData = groupsummary(dataTable, {'run_id', 'eq_type'}, {'mean', 'min', @(x) meanExcludingOutliers(x)}, {'ber', 'power_rop', 'power_pd_in'});
% Join groupedData with configDetails on the run_id field
joinedData = join(groupedData, configDetails, 'Keys', 'run_id');
end
% Custom function to plot filtered data
function plotFilteredData(joinedData, wlength)
filterFields = {'eq_type'};
cols = linspecer(8);
lst = ["-", ":", "--"];
% Loop over each field you want to filter by
for f = 1:numel(filterFields)
currentField = filterFields{f};
uniqueValues = unique(joinedData.(currentField));
for i = 1:numel(uniqueValues)
currentValue = uniqueValues(i);
% Filter joinedData for the current value
if isnumeric(currentValue)
filteredData = joinedData(joinedData.(currentField) == currentValue, :);
else
filteredData = joinedData(strcmp(joinedData.(currentField), currentValue), :);
end
% Group and average BERs of several run ids => repeated measurements in lab!
groupVars = {'bitrate'};
groupedDataWithMeans = groupsummary(filteredData, groupVars, {'mean', 'min'}, {'mean_ber', 'min_ber', 'fun1_ber', 'mean_power_rop', 'mean_power_pd_in'});
[~, uniqueIdx] = unique(filteredData.bitrate);
constantFields = filteredData(uniqueIdx, {'bitrate', 'GroupCount', 'pam_level', 'symbolrate', 'fiber_length', 'wavelength', 'precomp_amp', 'db_mode'});
groupedRunIDs = varfun(@(x) {unique(x)}, filteredData, 'GroupingVariables', groupVars, 'InputVariables', 'run_id');
groupedRunIDs.Properties.VariableNames(end) = {'GroupedRunIDs'};
groupedDataWithMeans = join(groupedDataWithMeans, constantFields, 'Keys', 'bitrate');
groupedDataWithMeans = join(groupedDataWithMeans, groupedRunIDs, 'Keys', 'bitrate');
filteredData = groupedDataWithMeans;
% Plotting
a = plot(filteredData.bitrate .* 1e-9, filteredData.mean_fun1_ber, ...
'Color', cols(i, :), 'MarkerSize', 4, 'LineWidth', 1, 'LineStyle', lst(mod(wlength - 1, numel(lst)) + 1), ...
'Marker', 'o', 'MarkerFaceColor', 'auto', 'MarkerEdgeColor', cols(i, :), ...
'DisplayName', [char(currentValue), '; ', num2str(wlength), ' nm']);
a.DataTipTemplate.DataTipRows(1).Label = 'Bitrate';
a.DataTipTemplate.DataTipRows(1).Format = ['%.1f', ' Gbit/s'];
a.DataTipTemplate.DataTipRows(2).Label = 'BER';
a.DataTipTemplate.DataTipRows(2).Format = '%.1e';
a.DataTipTemplate.DataTipRows(3).Label = 'P_{out}';
a.DataTipTemplate.DataTipRows(3).Value = filteredData.mean_mean_power_rop;
a.DataTipTemplate.DataTipRows(3).Format = ['%.2f', ' dBm'];
a.DataTipTemplate.DataTipRows(4).Label = 'Baudr';
a.DataTipTemplate.DataTipRows(4).Value = filteredData.bitrate .* 1e-9;
a.DataTipTemplate.DataTipRows(4).Format = ['%.1f', ' GBd'];
a.DataTipTemplate.DataTipRows(5).Label = 'Run ID';
a.DataTipTemplate.DataTipRows(5).Value = filteredData.GroupedRunIDs;
a.DataTipTemplate.FontSize = 9;
a.DataTipTemplate.FontName = 'arial';
end
end
end
% Custom function to finalize the plot settings
function finalizePlot()
yline(2e-2, 'DisplayName', '20% O-FEC', 'LineStyle', '--', 'HandleVisibility', 'off');
yline(3.8e-3, 'DisplayName', 'HD-FEC', 'LineStyle', '--', 'HandleVisibility', 'off');
xlabel('Bit Rate in GBps');
ylabel('Bit Error Rate (BER)');
xlim([300, 480]);
set(gca, 'yscale', 'log');
set(gca, 'Box', 'on');
grid on;
grid minor;
legend('Interpreter', 'none');
end
% Custom function using rmoutliers to calculate mean after removing outliers
function meanWithoutOutliers = meanExcludingOutliers(x)
[xWithoutOutliers,outlierpos] = rmoutliers(x);
if isempty(xWithoutOutliers)
meanWithoutOutliers = NaN;
else
meanWithoutOutliers = mean(xWithoutOutliers);
end
end

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basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
useGui = 0;
pamlvls = [4,6,8];
wlengths = [1302];
figoffset = 0;
figure(30)
tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
for p = 1:numel(pamlvls)
nexttile;
for w = 1:numel(wlengths)
sgtitle(['Lambda: ',num2str(wlengths),' nm'])
hold on
pamlvl = pamlvls(p);
wlength = wlengths(w);
db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
if useGui
filterParams = db.promptFilterParameters();
selectedFields = db.promptSelectFields();
else
filterParams = db.tables;
filterParams.Configurations = struct( ...
'bitrate', [], ...
'db_mode', [], ...
'fiber_length', 10, ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 0, ...
'pam_level', pamlvl, ...
'precomp_amp', [], ...
'rop_attenuation', 0, ...
'symbolrate', [], ...
'v_awg', [], ...
'v_bias', [], ...
'wavelength', wlength ...
);
selectedFields = {'Runs.run_id','BERs.ber_id','Equalizer.eq_id','Equalizer.eq_type','BERs.ber','BERs.occurrence',...
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp',...
'Measurements.power_rop','Measurements.power_laser','Measurements.power_pd_in'};
end
% Get data table from DB
[dataTable,~] = db.queryDB(filterParams, selectedFields);
% Grouping variables: 'bitrate' and 'eq_type'
groupVars = {'bitrate', 'eq_type'};
% Calculate mean BER for each combination of 'bitrate' and 'eq_type'
groupedData = groupsummary(dataTable, groupVars, {'mean', 'min', @(x) meanExcludingOutliers(x)}, {'ber', 'power_rop','power_pd_in'});
% Collect the run_id values for each group
groupedRunIDs = varfun(@(x) {unique(x)}, dataTable, 'GroupingVariables', groupVars, 'InputVariables', 'run_id');
groupedRunIDs.Properties.VariableNames(end) = {'GroupedRunIDs'};
% Join the grouped data with the grouped run_id list
avgdBerTable = join(groupedData, groupedRunIDs, 'Keys', groupVars);
% Define the fields that you want to use for filtering
filterFields = {'eq_type'};
% Loop over each field you want to filter by
for f = 1:numel(filterFields)
currentField = filterFields{f};
% Determine unique values for the current field
uniqueValues = unique(avgdBerTable.(currentField));
% Loop over each unique value for the current field
for i = 1:numel(uniqueValues)
currentValue = uniqueValues(i);
% Filter joinedData for the current value
if isnumeric(currentValue)
filteredData = avgdBerTable(avgdBerTable.(currentField) == currentValue, :);
else
filteredData = avgdBerTable(strcmp(avgdBerTable.(currentField), currentValue), :);
end
cols = linspecer(8);
lst = ["-",":","--"];
a=plot(filteredData.bitrate.*1e-9,filteredData.min_ber,...
'Color',cols(i,:),'MarkerSize',4,'LineWidth',1,'LineStyle',lst(w),...
'Marker','o','MarkerFaceColor','auto','MarkerEdgeColor',cols(i,:),...
'DisplayName',[char(currentValue),'; ',num2str(wlength),' nm' ]);
a.DataTipTemplate.DataTipRows(1).Label = 'Bitrate';
a.DataTipTemplate.DataTipRows(1).Format = ['%.1f',' Gbit/s'];
a.DataTipTemplate.DataTipRows(2).Label = 'BER';
a.DataTipTemplate.DataTipRows(2).Format ='%.1e';
a.DataTipTemplate.DataTipRows(3).Label = 'P_{out}';
a.DataTipTemplate.DataTipRows(3).Value = filteredData.mean_power_rop;
a.DataTipTemplate.DataTipRows(3).Format = ['%.2f',' dBm'];
a.DataTipTemplate.DataTipRows(4).Label = 'Run ID';
a.DataTipTemplate.DataTipRows(4).Value = filteredData.GroupedRunIDs;
a.DataTipTemplate.DataTipRows(4).Format = ['%d',' '];
a.DataTipTemplate.FontSize = 9;
a.DataTipTemplate.FontName = 'arial';
%
% a=scatter(filteredData.bitrate.*1e-9,filteredData.min_ber,5,'Marker','diamond',...
% 'Color',cols(i,:),'LineWidth',1,...
% 'MarkerFaceColor','auto','MarkerEdgeColor',cols(i,:),...
% 'DisplayName',currentValue);
%
% a.DataTipTemplate.DataTipRows(1).Label = 'Bitrate';
% a.DataTipTemplate.DataTipRows(1).Format = ['%.1f',' Gbit/s'];
%
% a.DataTipTemplate.DataTipRows(2).Label = 'BER';
% a.DataTipTemplate.DataTipRows(2).Format ='%.1e';
%
% a.DataTipTemplate.DataTipRows(3).Label = 'P_{out}';
% a.DataTipTemplate.DataTipRows(3).Value = filteredData.mean_power_rop;
% a.DataTipTemplate.DataTipRows(3).Format = ['%.2f',' dBm'];
%
% a.DataTipTemplate.DataTipRows(4).Label = 'Run ID';
% a.DataTipTemplate.DataTipRows(4).Value = filteredData.GroupedRunIDs;
% a.DataTipTemplate.DataTipRows(4).Format = ['%d',' '];
%
%
% a.DataTipTemplate.FontSize = 9;
% a.DataTipTemplate.FontName = 'arial';
end
end
% Continue with the rest of your plot settings
title(sprintf('%d km | %d nm | PAM %d',unique(filterParams.Configurations.fiber_length),wlength,pamlvl));
yline(2e-2, 'DisplayName', '20% O-FEC', 'LineStyle', '--', 'HandleVisibility', 'off','LineWidth',1);
yline(3.8e-3, 'DisplayName', 'HD-FEC', 'LineStyle', '--', 'HandleVisibility', 'off','LineWidth',1);
xlabel('Bit Rate in Gbps');
ylabel('Bit Error Rate');
xlim([300, 480])
ylim([8e-4,0.5])
set(gca, 'yscale', 'log');
set(gca, 'Box', 'on');
grid on;
grid minor;
end
end
legend('Interpreter', 'none');
set(gcf, 'Units', 'pixels', 'Position', 1.0e+03 * [0.2483 0.7303 1.2093 0.3980]);
% Custom function using rmoutliers to calculate mean after removing outliers
function meanWithoutOutliers = meanExcludingOutliers(x)
% Remove outliers using rmoutliers with default method (based on median)
xWithoutOutliers = rmoutliers(x);
% Calculate the mean of the non-outliers
if isempty(xWithoutOutliers)
% Handle the case where all values are outliers
meanWithoutOutliers = NaN;
else
meanWithoutOutliers = mean(xWithoutOutliers);
end
end

192
projects/HighSpeedExperiment_2024/auswertung/ber_vs_length.m Normal file
View File

@@ -0,0 +1,192 @@
basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
useGui = 0;
pamlvls = [6];
wlengths = [1293,1302,1310];%db.distinctValues.Configurations.wavelength
figoffset = 20;
figure(21)
tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
for w = 1:numel(wlengths)
nexttile;
for p = 1:numel(pamlvls)
pamlvl = pamlvls(p);
wlength = wlengths(w);
db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
if useGui
filterParams = db.promptFilterParameters();
selectedFields = db.promptSelectFields();
else
filterParams = db.tables;
filterParams.Configurations = struct( ...
'bitrate', 420e9, ...
'db_mode', [], ...
'fiber_length', [], ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 0, ...
'pam_level', pamlvl, ...
'precomp_amp', [], ...
'rop_attenuation', 0, ...
'symbolrate', [], ...
'v_awg', [], ...
'v_bias', [], ...
'wavelength', wlength ...
);
% filterParams.Equalizer.eq_type = equalizer_structure.vnle;
selectedFields = {'Runs.run_id','BERs.ber_id','Equalizer.eq_id','Equalizer.eq_type','BERs.ber','BERs.occurrence',...
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp',...
'Measurements.power_rop','Measurements.power_laser','Measurements.power_pd_in'};
end
sgtitle(['Rate: ',num2str(filterParams.Configurations.bitrate),' Gbit/s'])
% Get data table from DB
[dataTable,~] = db.queryDB(filterParams, selectedFields);
% Extract unique rows from dataTable for each run_id with relevant configuration details
uniqueConfigFields = {'run_id', 'pam_level', 'bitrate','symbolrate', 'fiber_length', 'wavelength', 'precomp_amp', 'db_mode'};
[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
configDetails = dataTable(uniqueIdx, uniqueConfigFields); % Extract unique configurations for each run_id
% Calculate the mean BER for each combination of 'run_id' and 'eq_type'
groupedData = groupsummary(dataTable, {'run_id', 'eq_type'}, {'mean','min'}, {'ber', 'power_rop','power_pd_in'});
groupedData = groupsummary(dataTable, {'run_id', 'eq_type'}, {'mean', 'min', @(x) meanExcludingOutliers(x)}, {'ber', 'power_rop','power_pd_in'});
% Join groupedData with configDetails on the run_id field
joinedData = join(groupedData, configDetails, 'Keys', 'run_id');
% Define the fields that you want to use for filtering
filterFields = {'eq_type'};
% Create a cell array to store filtered data tables for each filter field
filteredDataByField = struct();
hold on
% Loop over each field you want to filter by
for f = 1:numel(filterFields)
currentField = filterFields{f};
% Determine unique values for the current field
uniqueValues = unique(joinedData.(currentField));
% Create a struct entry for the current field
filteredDataByField.(currentField) = cell(numel(uniqueValues), 1);
% Loop over each unique value for the current field
for i = 1:numel(uniqueValues)
currentValue = uniqueValues(i);
% Filter joinedData for the current value
if isnumeric(currentValue)
filteredData = joinedData(joinedData.(currentField) == currentValue, :);
else
filteredData = joinedData(strcmp(joinedData.(currentField), currentValue), :);
end
%%% workaround to average the BERs of several runs (ie in 1km case or trials)
%%% Workaround to average the BERs of several runs (e.g., in 1 km case or trials)
% Grouping variable(s)
groupVars = {'fiber_length'};
% Use groupsummary to calculate the mean and min of relevant fields
groupedDataWithMeans = groupsummary(filteredData, groupVars, {'mean', 'min'}, {'mean_ber', 'min_ber', 'fun1_ber', 'mean_power_rop', 'mean_power_pd_in'});
% Extract representative values for constant fields
[~, uniqueIdx] = unique(filteredData.(groupVars{1})); % Get the first occurrence of each bitrate value
constantFields = filteredData(uniqueIdx, {'bitrate', 'GroupCount', 'pam_level', 'symbolrate', 'fiber_length', 'wavelength', 'precomp_amp', 'db_mode'});
% Keep track of which run_id values were grouped
groupedRunIDs = varfun(@(x) {unique(x)}, filteredData, 'GroupingVariables', groupVars, 'InputVariables', 'run_id');
groupedRunIDs.Properties.VariableNames(end) = {'GroupedRunIDs'};
% Join the grouped data with the constant fields
groupedDataWithMeans = join(groupedDataWithMeans, constantFields, 'Keys', groupVars);
% Join the grouped data with the grouped run_id list
groupedDataWithMeans = join(groupedDataWithMeans, groupedRunIDs, 'Keys', groupVars);
% Update filteredData to include the grouped information
filteredData = groupedDataWithMeans;
%%% end of workaround
cols = linspecer(8);
% a=plot(filteredData.bitrate.*1e-9,filteredData.mean_mean_ber,...
% 'Color',cols(i,:),'MarkerSize',4,'LineWidth',1,'LineStyle',':',...
% 'Marker','o','MarkerFaceColor','auto','MarkerEdgeColor',cols(i,:),...
% 'DisplayName',currentValue);
lst = ["-",":","--"];
a=plot(filteredData.(groupVars{1}),filteredData.mean_fun1_ber,...
'Color',cols(i,:),'MarkerSize',4,'LineWidth',1,'LineStyle',lst(1),...
'Marker','o','MarkerFaceColor','auto','MarkerEdgeColor',cols(i,:),...
'DisplayName',[char(currentValue),'; ',num2str(wlength),' nm' ]);
%
% scatter(filteredData.symbolrate.*1e-9,filteredData.min_min_ber,5,'Marker','_',...
% 'Color',cols(i,:),'LineWidth',1,...
% 'MarkerFaceColor',cols(i,:),'MarkerEdgeColor','black',...
% 'DisplayName',currentValue);
a.DataTipTemplate.DataTipRows(1).Label = groupVars{1};
a.DataTipTemplate.DataTipRows(1).Format = ['%.1f',''];
a.DataTipTemplate.DataTipRows(2).Label = 'BER';
a.DataTipTemplate.DataTipRows(2).Format ='%.1e';
a.DataTipTemplate.DataTipRows(3).Label = 'P_{out}';
a.DataTipTemplate.DataTipRows(3).Value = filteredData.mean_mean_power_rop;
a.DataTipTemplate.DataTipRows(3).Format = ['%.2f',' dBm'];
a.DataTipTemplate.DataTipRows(4).Label = 'Baudr';
a.DataTipTemplate.DataTipRows(4).Value = filteredData.bitrate .*1e-9;
a.DataTipTemplate.DataTipRows(4).Format = ['%.1f',' GBd'];
a.DataTipTemplate.DataTipRows(5).Label = 'Run ID';
a.DataTipTemplate.DataTipRows(5).Value = filteredData.GroupedRunIDs;
a.DataTipTemplate.DataTipRows(5).Format = ['%f',' GBd'];
a.DataTipTemplate.FontSize = 9;
a.DataTipTemplate.FontName = 'arial';
end
end
% Continue with the rest of your plot settings
title(sprintf('Lambda: %f',wlength));
yline(2e-2, 'DisplayName', '20% O-FEC', 'LineStyle', '--', 'HandleVisibility', 'off');
yline(3.8e-3, 'DisplayName', 'HD-FEC', 'LineStyle', '--', 'HandleVisibility', 'off');
xlabel(groupVars{1},'Interpreter','none');
ylabel('Bit Error Rate (BER)');
%xlim([300, 480])
ylim([8e-4,0.5])
set(gca, 'yscale', 'log');
set(gca, 'Box', 'on');
grid on;
grid minor;
legend('Interpreter', 'none');
end
end
% Custom function using rmoutliers to calculate mean after removing outliers
function meanWithoutOutliers = meanExcludingOutliers(x)
% Remove outliers using rmoutliers with default method (based on median)
xWithoutOutliers = rmoutliers(x);
% Calculate the mean of the non-outliers
if isempty(xWithoutOutliers)
% Handle the case where all values are outliers
meanWithoutOutliers = NaN;
else
meanWithoutOutliers = mean(xWithoutOutliers);
end
end

165
projects/HighSpeedExperiment_2024/auswertung/buildDBfromMeasurements.m
View File

@@ -17,18 +17,56 @@ folderNames = folderNames(~ismember(folderNames, {'.', '..'}));
% Combine folder names with paths
fullFolderPaths = flip(fullfile(folderPaths, folderNames));
only_mpi=0;
% process only MPI?
only_mpi=0;
if only_mpi
fullFolderPaths = fullFolderPaths(contains(fullFolderPaths,"mpi"));
end
relativeFolderPaths = strrep(fullFolderPaths, sioe_labor_path, '');
% Shorten folder paths to display only the folder name after the last filesep
displayFolderPaths = cellfun(@(x) x(find(x == filesep, 1, 'last') + 1 : end), fullFolderPaths, 'UniformOutput', false);
% Create checkbox settings for each folder path
numFolders = numel(fullFolderPaths);
checkboxSettings = cell(1, 2 * numFolders);
for i = 1:numFolders
checkboxSettings{2*i-1} = {displayFolderPaths{i}; sprintf('Folder_%d', i)};
checkboxSettings{2*i} = true; % Set false for unchecked by default
end
% Create settings dialog using settingsdlg
[settings, button] = settingsdlg( ...
'Description', 'Select Folders to Process:', ...
'title', 'Folder Selection', ...
checkboxSettings{:});
% Check if the user pressed OK
if strcmp(button, 'OK')
% Get all the field names from settings
allFields = fieldnames(settings);
% Determine which folders were selected
selectedFoldersIdx = cellfun(@(x) settings.(x), allFields);
% Get the list of selected folders
selectedFolders = fullFolderPaths(selectedFoldersIdx==1);
% Display the selected folders
fprintf('Selected Folders to Process:\n');
disp(selectedFolders);
else
fprintf('No folders selected.\n');
end
relativeFolderPaths = strrep(selectedFolders, sioe_labor_path, '');
disp(['Start to process ',num2str(numel(relativeFolderPaths)), ' folder in the directory']);
for f = 1:numel(fullFolderPaths)
folder = fullFolderPaths{f};
for f = 1:numel(selectedFolders)
folder = selectedFolders{n};
relfolder = relativeFolderPaths{f};
@@ -106,18 +144,18 @@ for f = 1:numel(fullFolderPaths)
params_merge = struct;
for c = 1:numel(big_wh_list)
fnames = fieldnames(big_wh_list{c}.parameter);
for f = 1:numel(fnames)
for fn = 1:numel(fnames)
% Initialize field if not already present
if ~isfield(params_merge, fnames{f})
params_merge.(fnames{f}) = [];
if ~isfield(params_merge, fnames{fn})
params_merge.(fnames{fn}) = [];
end
% Merge unique parameter values into params_merge
a = big_wh_list{c}.parameter.(fnames{f}).values;
b = params_merge.(fnames{f});
a = big_wh_list{c}.parameter.(fnames{fn}).values;
b = params_merge.(fnames{fn});
vals_to_add = setdiff(a, b); % New values in a that aren't in b
b = sort([b, vals_to_add]); % Combine and sort values
params_merge.(fnames{f}) = b;
params_merge.(fnames{fn}) = b;
end
end
@@ -157,11 +195,11 @@ for f = 1:numel(fullFolderPaths)
isMPI = isfield(measurementStruct,'i_power');
% Process record if it contains data
% Process record if it contains *any* data
if recordIsFilled
if ~isMPI
% Generate filenames with conditionally formatted parameters
% Format the L parameter value (show decimal only if non-zero)
if configStruct.lambda == floor(configStruct.lambda)
@@ -175,8 +213,13 @@ for f = 1:numel(fullFolderPaths)
configStruct.M, L_str, configStruct.bitrate, configStruct.duobinary, 0);
fbody_tx = strrep(fbody_tx, '.', '_'); % Replace decimal point with underscore
fbody_rx = sprintf('%s_PAM_%d_L_%s_R_%d_DB_%d_ROP_%d', datebody, ...
configStruct.M, L_str, configStruct.bitrate, configStruct.duobinary, configStruct.rop_atten);
if configStruct.rop_atten == round(configStruct.rop_atten)
fbody_rx = sprintf('%s_PAM_%d_L_%s_R_%d_DB_%d_ROP_%d', datebody, ...
configStruct.M, L_str, configStruct.bitrate, configStruct.duobinary, configStruct.rop_atten);
else
fbody_rx = sprintf('%s_PAM_%d_L_%s_R_%d_DB_%d_ROP_%.1f', datebody, ...
configStruct.M, L_str, configStruct.bitrate, configStruct.duobinary, configStruct.rop_atten);
end
fbody_rx = strrep(fbody_rx, '.', '_');
elseif isMPI
@@ -185,8 +228,13 @@ for f = 1:numel(fullFolderPaths)
configStruct.M, configStruct.bitrate, configStruct.duobinary, 0);
fbody_tx = strrep(fbody_tx, '.', '_'); % Replace decimal point with underscore
fbody_rx = sprintf('%s_PAM_%d_R_%d_DB_%d_I_atten_%d', datebody, ...
configStruct.M, configStruct.bitrate, configStruct.duobinary, configStruct.interference_atten);
if configStruct.interference_atten == round(configStruct.interference_atten)
fbody_rx = sprintf('%s_PAM_%d_R_%d_DB_%d_I_atten_%d', datebody, ...
configStruct.M, configStruct.bitrate, configStruct.duobinary, configStruct.interference_atten);
else
fbody_rx = sprintf('%s_PAM_%d_R_%d_DB_%d_I_atten_%.1f', datebody, ...
configStruct.M, configStruct.bitrate, configStruct.duobinary, configStruct.interference_atten);
end
fbody_rx = strrep(fbody_rx, '.', '_'); % Replace decimal point with underscore
end
@@ -223,6 +271,7 @@ for f = 1:numel(fullFolderPaths)
% SYNCHRONIZED RX SIGNAL
fn_rxtsynch = [filesep, fbody_rx, '_rx_signal.mat'];
fp_rxtsynch = fullfile([folder, fn_rxtsynch]);
fn_rxtsynch_rel = [];
if exist(fp_rxtsynch, "file") == 2
fn_rxtsynch_rel = [relfolder, fn_rxtsynch];
@@ -244,13 +293,42 @@ for f = 1:numel(fullFolderPaths)
end
end
end
elseif exist(fp_rxtsynch, "file") == 0
disp(['RX Signal not found at: ', fn_rxtsynch,' generate and save new one using symbols and raw signal']);
Scpe_sig_raw = load(fp_rxraw);
Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
Symbols = load(fp_symbols);
Symbols = Symbols.Symbols;
%%%%%% Sample to 2x fsym %%%%%%
Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",Scpe_sig_raw.fs,"fs_out",2*Symbols.fs);
%%%%%% Sync Rx signal with reference (S is a cell array with all occurences) %%%%%%
[Scpe_sig_syncd,S,isFlipped] = Scpe_sig_resampled.tsynch("reference",Symbols,"fs_ref",Symbols.fs);
%%%%% Plot and Save Routines: SAVE RECEIVED SIGNALS %%%%%%%%%%%%%%%%%%%%%%%%%
save(fp_rxtsynch,"S");
if exist(fp_rxtsynch, "file") == 0
warndlg('Something still wromg... No rx file here but we just generated it from raw signal');
end
fn_rxtsynch_rel = [relfolder, fn_rxtsynch];
elseif missing_raw_flag
warning(['RX Signal not found at: ', fn_rxtsynch]);
warndlg('No RAW or RX signal found! This is bad!');
% look at measurementStruct and configStruct
end
else
disp('Record not filled?')
% look at measurementStruct and configStruct
end
% Call the duplicate check function
exists = db.checkIfRunExists('Runs', 'rx_sync_path', fn_rxtsynch_rel);
if ~isempty(fn_rxtsynch_rel)
exists = db.checkIfRunExists('Runs', 'rx_sync_path', fn_rxtsynch_rel);
end
if ~exists
@@ -335,31 +413,32 @@ for f = 1:numel(fullFolderPaths)
% Append the new row to the Measurements table
db.appendToTable('Measurements', newMeas);
% Table 4: Append to Bers
[ber, structure, settings] = getBers(configStruct,measurementStruct);
for t = 1:numel(ber)
if iscell(ber(t))
ber_ = ber(t);
ber_ = ber_{1};
else
ber_ = ber(t);
end
if ber_~=-1
newBer = struct(...
'ber_id', NaN,...
'run_id', run_id,...
'processing_structure', structure(t),...
'processing_settings', settings(t),...
'ber', jsonencode(ber_)...
);
db.appendToTable('BERs', newBer);
end
end
% % Table 4: Append to Bers
% [ber, structure, settings] = getBers(configStruct,measurementStruct);
% for t = 1:numel(ber)
%
% if iscell(ber(t))
% ber_ = ber(t);
% ber_ = ber_{1};
% else
% ber_ = ber(t);
% end
%
% if ber_~=-1
%
% newBer = struct(...
% 'ber_id', NaN,...
% 'run_id', run_id,...
% 'processing_structure', structure(t),...
% 'processing_settings', settings(t),...
% 'ber', jsonencode(ber_)...
% );
%
% db.appendToTable('BERs', newBer);
%
% end
%
% end
end

42
projects/HighSpeedExperiment_2024/auswertung/checkDBpaths.m Normal file
View File

@@ -0,0 +1,42 @@
% Load the Runs table from the database
db = DBHandler("pathToDB", 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor.db');
runsTable = db.queryDB(db.tables, {'Runs.run_id', 'Runs.tx_bits_path', 'Runs.tx_symbols_path', 'Runs.rx_sync_path', 'Runs.rx_raw_path', 'Runs.filename'});
% Initialize an array to store the result of existence check
fileExistenceResults = false(height(runsTable), 4); % 4 columns for the paths: tx_bits, tx_symbols, rx_sync, rx_raw
% Main file path
sioe_labor_path = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor';
% Loop through all rows in the table and check paths
for i = 1:height(runsTable)
fileExistenceResults(i, 1) = exist([sioe_labor_path, runsTable.tx_bits_path{i}], 'file') == 2;
fileExistenceResults(i, 2) = exist([sioe_labor_path, runsTable.tx_symbols_path{i}], 'file') == 2;
fileExistenceResults(i, 3) = exist([sioe_labor_path, runsTable.rx_sync_path{i}], 'file') == 2;
fileExistenceResults(i, 4) = exist([sioe_labor_path, runsTable.rx_raw_path{i}], 'file') == 2;
end
% Identify corrupted run_ids (where any path does not exist)
corruptedRunIds = runsTable.run_id(~all(fileExistenceResults, 2));
% Delete entries in all tables related to corrupted run_ids
for i = 1:numel(corruptedRunIds)
run_id = corruptedRunIds(i);
% Delete from BERs table
db.executeSQL(sprintf('DELETE FROM BERs WHERE run_id = %d;', run_id));
% Delete from Equalizer table (if applicable)
db.executeSQL(sprintf('DELETE FROM Equalizer WHERE eq_id IN (SELECT eq_id FROM BERs WHERE run_id = %d);', run_id));
% Delete from Measurements table
db.executeSQL(sprintf('DELETE FROM Measurements WHERE run_id = %d;', run_id));
% Delete from Configurations table
db.executeSQL(sprintf('DELETE FROM Configurations WHERE run_id = %d;', run_id));
% Delete from Runs table
db.executeSQL(sprintf('DELETE FROM Runs WHERE run_id = %d;', run_id));
end
%
% disp('Entries for corrupted run_ids have been removed from the database.');

32
projects/HighSpeedExperiment_2024/auswertung/getBerForRunId.m Normal file
View File

@@ -0,0 +1,32 @@
function [berTable, foundBerFlag] = getBerForRunId(db, run_id)
% getBerForRunId Queries the BER data for the specified run_id.
% Inputs:
% db - The database handler object.
% run_id - The run ID for which to get the BER data.
% Outputs:
% berData - A table containing BER results for the given run ID.
% Set up filter parameters to query the BER data for the specific run_id
filterParams = db.tables;
filterParams.Runs.run_id = run_id; % Filter by specific run_id
% Define the fields to be retrieved from the database
selectedFields = {'Runs.run_id', 'BERs.ber_id', 'Equalizer.eq_id', 'Equalizer.eq_type', ...
'BERs.ber', 'BERs.occurrence', 'Configurations.db_mode', ...
'Configurations.pam_level', 'Configurations.bitrate', 'Configurations.symbolrate', ...
'Configurations.fiber_length', 'Configurations.wavelength', ...
'Configurations.precomp_amp', 'Measurements.power_rop', 'Measurements.power_laser', ...
'Measurements.power_pd_in'};
% Query the database for the specified run_id
[berTable, ~] = db.queryDB(filterParams, selectedFields);
if ~isnumeric(berTable.ber)
foundBerFlag = ~isnan(str2num(berTable.ber));
else
foundBerFlag = 1;
end
% Display information about the found BER entries
% fprintf('Found %d BER entries for run_id %d.\n', size(berTable, 1), run_id);
end

85
projects/HighSpeedExperiment_2024/auswertung/only_show_precomp.m Normal file
View File

@@ -0,0 +1,85 @@
precomp_path = "C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\precomp";
precomp_fn = "lab_high_speed";
precomp_mode = 2; %0=do nothing ; 1= measure; 2=precomp active
db_precode = 1;
db_coding_approach = 1;
fsym = 224e9;
fdac = 256e9;
random_key = 0;
M = 4;
if (db_precode==1)&&(db_coding_approach==0)
if M == 4
pulsef=1;
precomp_amp_max = -50;
elseif M == 6
pulsef=0;
precomp_amp_max = -50;
elseif M == 8
pulsef=0;
precomp_amp_max = -50;
end
elseif (db_precode==1)&&(db_coding_approach==1)
if M == 4
pulsef=1;
precomp_amp_max = -38;
pulsef = 1;
elseif M == 6
pulsef=0;
precomp_amp_max = -38;
pulsef = 1;
elseif M == 8
pulsef=0;
precomp_amp_max = -38;
pulsef = 1;
end
elseif (db_precode==0)&&(db_coding_approach==0)
if M == 4
pulsef=1;
precomp_amp_max = -37;
pulsef = 1;
elseif M == 6
pulsef=0;
precomp_amp_max = -34;
pulsef = 1;
elseif M == 8
pulsef=0;
precomp_amp_max = -34;
pulsef = 0;
end
end
rcalpha = 0.05;
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"rrcalpha",rcalpha);
Pamsource = PAMsource(...
"fsym",fsym,"M",M,"order",19,"useprbs",1,...
"fs_out",fdac,...
"applyclipping",0,"clipfactor",1.2,...
"applypulseform",pulsef,"pulseformer",Pform,...
"randkey",random_key,...
"db_precode",db_precode,"db_encode",db_coding_approach,...
"mrds_code",0,"mrds_blocklength",512);
[Digi_sig,Symbols,Bits] = Pamsource.process();
Digi_sig.spectrum("displayname","TX with precomp","fignum",2223,"normalizeToNyquist",0,"normalizeTo0dB",0);
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
Digi_sig = precomp_est.precomp(Digi_sig,'maxampdb',precomp_amp_max,'loadPath',precomp_path,'fileName',precomp_fn);
Digi_sig = Digi_sig.resample("fs_out",fdac);
Digi_sig= Digi_sig.normalize("mode","rms");
Digi_sig.spectrum("displayname","TX After precomp","fignum",2223,"normalizeToNyquist",0,"normalizeTo0dB",0);

46
projects/HighSpeedExperiment_2024/auswertung/runDSP.m
View File

@@ -1,23 +1,25 @@
tic
basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
useGui = 0;
db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
toc
if useGui
filterParams = db.promptFilterParameters();
selectedFields = db.promptSelectFields();
else
filterParams = db.tables;
filterParams.Configurations = struct( ...
'bitrate', [], ...
'db_mode', [], ...
'fiber_length', 10, ...
'bitrate', 300e9, ...
'db_mode', 0, ...
'fiber_length', 1, ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 0, ...
'pam_level', [], ...
'pam_level', 4, ...
'precomp_amp', [], ...
'rop_attenuation', 0, ...
'rop_attenuation', [], ...
'symbolrate', [], ...
'v_awg', [], ...
'v_bias', [], ...
@@ -26,15 +28,30 @@ else
% filterParams.Runs.run_id = 3303;
%filterParams.Equalizer.eq_id = equalizer_structure.vnle;
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.fiber_length','Configurations.wavelength','Configurations.precomp_amp'};
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','BERs.ber'};
end
[dataTable,sql_query] = db.queryDB(filterParams, selectedFields);
fprintf('Found %d entries for requested Configuration. IDs are: %s \n',size(dataTable,1),jsonencode(dataTable.run_id));
toc
[dataTable,sql_query] = db.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))));
toc
fprintf('Processing: %d%%', 0);
%2) Process Measurement Config
for i = 1:size(dataTable,1)
fprintf('\n%s CURRENT ID: %d == %d KM == %s PAM %d == %d Gbit/s == %d nm %s \n \n', repmat('=', 1, 10), dataTable.run_id(i), dataTable.fiber_length(i) ,db_mode(dataTable.db_mode(i)), dataTable.pam_level(i) ,dataTable.bitrate(i).*1e-9,dataTable.wavelength(i), repmat('=', 1, 10)); % Print a blank line, then a thick line of 80 '=' characters, then another blank line
fprintf('\b\b\b\b%4d', i); % Backspace 3 characters, then overwrite
[~, foundBerFlag] = getBerForRunId(db, dataTable.run_id(i));
if foundBerFlag
continue
end
fprintf('\n%s T: %f CURRENT ID: %d == %d KM == %s PAM %d == %d Gbit/s == %d nm %s \n \n', repmat('=', 1, 10), toc/60, dataTable.run_id(i), dataTable.fiber_length(i) ,db_mode(dataTable.db_mode(i)), dataTable.pam_level(i) ,dataTable.bitrate(i).*1e-9,dataTable.wavelength(i), repmat('=', 1, 10)); % Print a blank line, then a thick line of 80 '=' characters, then another blank line
% FROM NOW ON, ONE Run_id IS CHOSEN AND WILL BE DSP'd
@@ -74,13 +91,13 @@ for i = 1:size(dataTable,1)
%VNLE
eq_vnle(o) = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.05,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
[eq_sig,eq_noise,ber_vnle(o),totalErrors] = vnle(eq_vnle(o),M,rx_sig,tx_symbols, tx_bits);
%VNLE + PF + MLSE
eq_mlse(o) = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.05,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_(o) = Postfilter("ncoeff",2);
mlse_(o) = MLSE("DIR",[0,0],"duobinary_output",0,"M",[],"trellis_states",[]);
[eq_sig,eq_noise,ber_mlse(o),totalErrors] = vnle_postfilter_mlse(eq_mlse(o) , pf_(o), mlse_(o),M, rx_sig,tx_symbols, tx_bits);
case db_mode.db_precoded
%EQ targets DB => less precompensation; pre-coded
@@ -141,7 +158,8 @@ for i = 1:size(dataTable,1)
end
end
fprintf('\n%s SIMULATION COMPLETE %s \n \n', repmat('=', 1, 35), repmat('=', 1, 35)); % Print a blank line, then a thick line of 80 '=' characters, then another blank line
fprintf('\n%s SIMULATION COMPLETE AFTER %f MINUTES %s \n \n', repmat('=', 1, 35), toc/60 ,repmat('=', 1, 35)); % Print a blank line, then a thick line of 80 '=' characters, then another blank line

5
projects/HighSpeedExperiment_2024/master_auswertung_10km.m
View File

@@ -13,9 +13,14 @@ rop_atten_vals = wh.parameter.rop_atten.values;
figure(18)
tiledlayout(3, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
%CHANGE PAM FORMAT HERE (use 4,6,8)
for M_choose = [8]
sgtitle(['PAM',num2str(M_choose)])
for l = 1:numel(lambda_vals)
ber_vnle = [];
ber_vnle_mlse= [];
ber_db= [];
ber_db_enc= [];
for b = 1:numel(bitrate_vals)
cel = wh.getStoValue('ber_vnle',M_choose(1),lambda_vals(l),bitrate_vals(b),duobinary_vals(1),rop_atten_vals(1));

4
projects/HighSpeedExperiment_2024/single_auswertung_10km.m
View File

@@ -16,6 +16,10 @@ l = 6;
for m = 1:numel(M_vals)
sgtitle(['Lambda: ',num2str(lambda_vals(l)),' nm'])
%for l = 1:numel(lambda_vals)
ber_vnle = [];
ber_vnle_mlse= [];
ber_db= [];
ber_db_enc= [];
for b = 1:numel(bitrate_vals)
M_choose = M_vals(m);

70
projects/IMDD_base_system/filter_debug.m
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@@ -1,70 +0,0 @@
clear
%% Set Simulation Variables
O = 18; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% CONSTRUCT ALL CLASSES
digimod = PAMmapper(M,0);
pulsef = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.05);
awg = AWG('preset','M8199B','fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",30e9,"fsamp",fdac,"filterType",filtertypes.bessel_inp);
%% PROCESS
% PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% Build Inf. signal class
bits = Informationsignal(bitpattern);
% Digi Mod
mod_out = digimod.map(bits);
% merken für EQ training
reference = mod_out;
% shape shape
mod_out = pulsef.process(mod_out);
test = applyPulseShaping(reference.signal,fsym,fdac);
% AWG -> ELECTRICAL DOMAIN
X = lp_laser.process(mod_out);
%X.spectrum(fsimu,"displayname",'AWG out','figurename','after AWG');

220
projects/IMDD_base_system/imdd_example.m Normal file
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@@ -0,0 +1,220 @@
function [ber] = imdd_example(varargin)
% BASIC IMDD Model...
% varargin is either empty or a struct, i.e.:
%
curFolder = pwd;
funcFolder=fileparts(mfilename('fullpath'));
if ~isempty(funcFolder)
cd(funcFolder);
end
% TX
M = 4;
fsym = 180e9;
f_nyquist = fsym/2;
apply_pulsef = 0;
fdac = 2*fsym;%256e9;
fadc = 2*fsym;%256e9;
fdac = 256e9;
fadc = 256e9;
random_key = 1;
db_precode = 0;
emulate_precode = 0;
discard_precode = 0;
db_encode = 0;
% duob_mode = db_mode.db_emulate;
emulate_db = 1;
rcalpha = 0.05;
kover = 16;
vbias_rel = 0.5;
u_pi = 2.9;
vbias = -vbias_rel*u_pi;
laser_wavelength = 1310;
laser_linewidth = 0;
tx_bw_nyquist = 1.5;
% Channel
link_length = 1;
% RX
rop = -8;
rx_bw_nyquist = 0.7;
% EQ
eq_mode = equalizer_structure.vnle_pf_mlse;
ffe_order=[50,0,0];
vnle_order=[50,7,7];
dfe_order = [2 0 0];
len_tr = 4096*2;
mu_ffe = [0.0004 0.0004 0.0004];
mu_dfe = 0.0004;
mu_dc = 0.00;
dfe_ = sum(dfe_order)>0;
% Parse optional input arguments
if ~isempty(varargin)
var_s = varargin{1};
if isstruct(var_s)
fields = fieldnames(var_s);
for i = 1:numel(fields)
eval([fields{i}, ' = ', num2str( var_s.(fields{i}) ), ';']);
fprintf("%s <-- %.2f \n", fields{i}, var_s.(fields{i}));
end
else
error('Optional variables should be passed as a struct.');
end
end
%%%% TX Signal
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"rrcalpha",rcalpha);
[Digi_sig,Symbols,Bits] = PAMsource(...
"fsym",fsym,"M",M,"order",19,"useprbs",1,...
"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",'Digi Spectrum','fignum',10,'normalizeTo0dB',1);
%%%%% AWG
% El_sig = M8199A("kover",kover).process(Digi_sig);
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1).process(Digi_sig);
% El_sig.spectrum("displayname",'Digi Spectrum','fignum',100,'normalizeTo0dB',0);
% El_sig = El_sig.setPower(0,"dBm");
%%%%% Low-pass el. components %%%%%%
El_sig = Filter('filtdegree',4,"f_cutoff",tx_bw_nyquist.*f_nyquist,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(El_sig);
%%%%% Electrical Driver Amplifier %%%%%%
El_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",3).process(El_sig);
El_sig = El_sig.normalize("mode","oneone");
%%%%% MODULATE E/O CONVERSION %%%%%%
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1).process(El_sig);
%%%%%% Fiber %%%%%%
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
%%%%%% ROP %%%%%%
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
%%%%%% PD Square Law %%%%%%
Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
Rx_sig = Filter('filtdegree',4,"f_cutoff",rx_bw_nyquist.*f_nyquist,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(Rx_sig);
% %%%%%% Low-pass Scope %%%%%%
%dactivated in scope module!
Lp_scpe = Filter('filtdegree',4,"f_cutoff",10e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
% Rx_sig.spectrum("displayname",'Analog Rx Spectrum','fignum',100,'normalizeTo0dB',1);
%%%%%% Scope %%%%%%
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',0,'H_lpf',Lp_scpe).process(Rx_sig);
% Scpe_sig.spectrum("displayname",'Digital (256 GSa/s) Rx Spectrum','fignum',100,'normalizeTo0dB',1);
%%%%%% Sample to 2x fsym %%%%%%
Scpe_sig = Scpe_sig.resample("fs_in",fadc,"fs_out",2*fsym);
%%%%%% Sync Rx signal with reference %%%%%%
[Scpe_sig,S] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym);
Scpe_sig.spectrum("displayname",'Prior DSP (2x fsym) Spectrum','fignum',100,'normalizeTo0dB',1);
%%% EQUALIZING
ber = struct();
switch eq_mode
case equalizer_structure.ffe
%FFE
if db_precode
Bits_ = PAMmapper(M,0).demap(Symbols);
else
Bits_ = Bits;
end
eq_ffe = EQ("Ne",ffe_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_ffe = 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_ffe = 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);
[eq_sig,eq_noise,ber.ber_ffe,totalErrors] = vnle( eq_ffe,M,Scpe_sig ,Symbols, Bits_);
eq_noise.spectrum("displayname",'Noise Spectrum after FFE','fignum',41,'normalizeTo0dB',0);
case equalizer_structure.vnle
if db_precode
Bits_ = PAMmapper(M,0).demap(Symbols);
else
Bits_ = Bits;
end
%VNLE
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_sig,eq_noise,ber.ber_vnle,totalErrors] = vnle(eq_vnle,M,Scpe_sig ,Symbols, Bits_);
eq_noise.spectrum("displayname",'Noise Spectrum after VNLE','fignum',41,'normalizeTo0dB',0);
case equalizer_structure.vnle_pf_mlse
if db_precode
Bits_ = PAMmapper(M,0).demap(Symbols);
else
Bits_ = Bits;
end
%VNLE + PF + MLSE
eq_mlse = 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_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);
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);
pf_ = Postfilter("ncoeff",1);
mlse_ = MLSE("DIR",[0,0],"duobinary_output",0,"M",[],"trellis_states",[]);
[eq_sig,eq_noise,ber.ber_mlse,totalErrors] = vnle_postfilter_mlse(eq_mlse , pf_, mlse_,M, Scpe_sig ,Symbols, Bits_);
pf_.showFilter(eq_noise);
eq_noise.spectrum("displayname",'Noise Spectrum after VNLE+PF','fignum',41,'normalizeTo0dB',0);
case equalizer_structure.db_precoded
%EQ targets DB => less precompensation; pre-coded
mlse_db_pre = MLSE("DIR",[1,1],"duobinary_output",1,"M",M,"trellis_states",PAMmapper(M,0).levels);
eq_db_pre = 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_sig,eq_noise,ber.ber_db,totalErrors] = duobinary_target(eq_db_pre, mlse_db_pre,M, Scpe_sig ,Symbols, Bits);
eq_noise.spectrum("displayname",'Noise Spectrum after DB','fignum',41,'normalizeTo0dB',0);
%->append BER to DB
case equalizer_structure.db_encoded
%db signaling => db encoded
mlse_db_enc = MLSE("DIR",[1,1],"duobinary_output",1,"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);
[eq_sig,eq_noise,ber.ber_db_enc,totalErrors] = duobinary_signaling(eq_db_enc, mlse_db_enc,M, Scpe_sig ,Symbols, Bits);
%->append BER to DB
end
% fprintf('BER FFE: %.2e \n',ber.ber_mlse);
% % El_sig.spectrum("displayname",'Tx Spectrum','fignum',10,'normalizeTo0dB',1);
% Scpe_sig.spectrum("displayname",'Rx Spectrum','fignum',100,'normalizeTo0dB',1);
if ~isempty(curFolder)
cd(curFolder);
end
end

BIN
projects/IMDD_base_system/imdd_example_live.mlx Normal file
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26
projects/IMDD_base_system/imdd_it.m Normal file
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@@ -0,0 +1,26 @@
% basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
% db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
uloops = struct;
uloops.bitrate = [300,330,360,390,420,450,480].*1e9;
uloops.M = 4;
wh = DataStorage(uloops);
wh.addStorage("ber");
wh = submit_simulations(wh,db,"parallel",0,"simulation_mode",0);
a = wh.getStoValue('ber', uloops.bitrate,uloops.M);
for i = 1:numel(a)
bers(i) = a{i}.ber_mlse;
end
figure(2024)
hold on
plot(uloops.bitrate.*1e-9,bers);
yline(3.8e-3,'LineWidth',2,'DisplayName','3.8e-3');
yline(2e-2,'LineWidth',2,'LineStyle','--','DisplayName','2e-2');
beautifyBERplot()
legend

172
projects/IMDD_base_system/imdd_labdata_example.m Normal file
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@@ -0,0 +1,172 @@
% function [ber] = imdd_labdata_example(varargin)
% TX
M = 4;
fsym = 224e9;
f_nyquist = fsym/2;
apply_pulsef = 0;
fdac = 2*fsym;%256e9;
fadc = 2*fsym;%256e9;
fdac = 256e9;
fadc = 256e9;
random_key = 1;
db_precode = 1;
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 = 1.5;
% Channel
link_length = 0;
% RX
rop = -7.5;
rx_bw_nyquist = 1.5;
% EQ
eq_mode = equalizer_structure.vnle_pf_mlse;
ffe_order=[50,0,0];
vnle_order=[50,7,7];
dfe_order = [0 0 0];
len_tr = 4096*2;
mu_ffe = [0.0004 0.0004 0.0004];
mu_dfe = 0.0004;
mu_dc = 0.05;
dfe_ = sum(dfe_order)>0;
%
% % Parse optional input arguments
% if ~isempty(varargin)
% var_s = varargin{1};
% if isstruct(var_s)
% fields = fieldnames(var_s);
% for i = 1:numel(fields)
% eval([fields{i}, ' = ', num2str( var_s.(fields{i}) ), ';']);
% fprintf("%s <-- %.2f \n", fields{i}, var_s.(fields{i}));
% end
% else
% error('Optional variables should be passed as a struct.');
% end
% end
basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
useGui = 0;
db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
filterParams = db.tables;
% filterParams.Runs.run_id = 2958; % no db
% filterParams.Runs.run_id = 2937; % no db
filterParams.Configurations = struct( ...
'bitrate', 300e9, ...
'db_mode', 0, ...
'fiber_length', 1, ...
'interference_attenuation', [], ...
'interference_path_length', [], ...
'is_mpi', 0, ...
'pam_level', 4, ...
'precomp_amp', [], ...
'rop_attenuation', 0, ...
'symbolrate', [], ...
'v_awg', [], ...
'v_bias', [], ...
'wavelength', 1310 ...
);
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.fiber_length','Configurations.wavelength','Configurations.precomp_amp','BERs.ber'};
[dataTable,sql_query] = db.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))));
tx_bits = load([basePath, char(dataTable.tx_bits_path(1))]);
tx_bits = tx_bits.Bits;
tx_symbols = load([basePath, char(dataTable.tx_symbols_path(1))]);
tx_symbols = tx_symbols.Symbols;
rx_sync = load([basePath, char(dataTable.rx_sync_path(1))]);
rx_sync = rx_sync.S;
fsym = tx_symbols.fs;
%%%%%% Sample to 2x fsym %%%%%%
Scpe_sig = rx_sync{1}.resample("fs_in",rx_sync{1}.fs,"fs_out",2*fsym);
%%%%%% Sync Rx signal with reference %%%%%%
[Scpe_sig,S] = Scpe_sig.tsynch("reference",tx_symbols,"fs_ref",fsym);
Scpe_sig.spectrum("displayname",'Rx (Scpe+Sync+Resample)','fignum',100,'normalizeTo0dB',0);
Scpe_sig = Filter('filtdegree',4,"f_cutoff",tx_symbols.fs.*0.6,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
Scpe_sig.spectrum("displayname",'Rx (Scpe+Sync+Resample+LPF)','fignum',100,'normalizeTo0dB',0);
%%% EQUALIZING
ber = struct();
switch eq_mode
case equalizer_structure.ffe
%FFE
if db_precode
Bits_ = PAMmapper(M,0).demap(Symbols);
else
Bits_ = Bits;
end
eq_ffe = EQ("Ne",ffe_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_ffe = 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_ffe = 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);
[eq_sig,eq_noise,ber.ber_ffe,totalErrors] = vnle( eq_ffe,M,Scpe_sig ,Symbols, Bits_);
eq_noise.spectrum("displayname",'Noise Spectrum after FFE','fignum',41,'normalizeTo0dB',0);
case equalizer_structure.vnle
%VNLE
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_sig,eq_noise,ber.ber_vnle,totalErrors] = vnle(eq_vnle,M,Scpe_sig ,Symbols, Bits_);
eq_noise.spectrum("displayname",'Noise Spectrum after VNLE','fignum',41,'normalizeTo0dB',0);
case equalizer_structure.vnle_pf_mlse
%VNLE + PF + MLSE
eq_mlse = 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);
pf_ = Postfilter("ncoeff",1);
mlse_ = MLSE("DIR",[0,0],"duobinary_output",0,"M",[],"trellis_states",[]);
[eq_sig,eq_noise,ber.ber_mlse,totalErrors] = vnle_postfilter_mlse(eq_mlse , pf_, mlse_,M, Scpe_sig ,Symbols, Bits_);
pf_.showFilter(eq_noise);
eq_noise.spectrum("displayname",'Noise Spectrum after VNLE+PF','fignum',41,'normalizeTo0dB',0);
case equalizer_structure.db_precoded
%EQ targets DB => less precompensation; pre-coded
mlse_db_pre = MLSE("DIR",[1,1],"duobinary_output",1,"M",M,"trellis_states",PAMmapper(M,0).levels);
eq_db_pre = 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_sig,eq_noise,ber.ber_db,totalErrors] = duobinary_target(eq_db_pre, mlse_db_pre,M, Scpe_sig ,Symbols, Bits);
eq_noise.spectrum("displayname",'Noise Spectrum after DB','fignum',41,'normalizeTo0dB',0);
%->append BER to DB
case equalizer_structure.db_encoded
%db signaling => db encoded
mlse_db_enc = MLSE("DIR",[1,1],"duobinary_output",1,"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);
[eq_sig,eq_noise,ber.ber_db_enc,totalErrors] = duobinary_signaling(eq_db_enc, mlse_db_enc,M, Scpe_sig ,Symbols, Bits);
%->append BER to DB
end
fprintf('BER FFE: %.2e \n',ber.ber_mlse);
% % El_sig.spectrum("displayname",'Tx Spectrum','fignum',10,'normalizeTo0dB',1);
% Scpe_sig.spectrum("displayname",'Rx Spectrum','fignum',100,'normalizeTo0dB',1);
% end

353
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function [output] = imdd_model(simulation_mode,database,varargin)
%%% Change folder
curFolder = pwd;
funcFolder=fileparts(mfilename('fullpath'));
if ~isempty(funcFolder)
cd(funcFolder);
end
%%% Run parameters
% TX
M = 4;
fsym = 180e9;
f_nyquist = fsym/2;
apply_pulsef = 1;
fdac = 256e9;
fadc = 256e9;
random_key = 1;
db_precode = 1;
emulate_precode = 0;
discard_precode = 1;
db_encode = 0;
% duob_mode = db_mode.db_emulate;
emulate_db = 1;
rcalpha = 0.05;
kover = 16;
vbias_rel = 0.5;
u_pi = 2.9;
vbias = -vbias_rel*u_pi;
laser_wavelength = 1310;
laser_linewidth = 0;
tx_bw_nyquist = 0.9;
% Channel
link_length = 1;
% RX
rop = -8;
rx_bw_nyquist = 0.7;
% EQ
eq_mode = equalizer_structure.vnle_pf_mlse;
ffe_order=[50,0,0];
vnle_order=[50,7,7];
dfe_order = [2 0 0];
len_tr = 4096*2;
mu_ffe = [0.0004 0.0004 0.0004];
mu_dfe = 0.0004;
mu_dc = 0.00;
dfe_ = sum(dfe_order)>0;
%%% 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
%%% run the simulation or measurement or ...
if simulation_mode
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
tx_simulation;
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
rx_simulation;
Scpe_cell{1} = Scpe_sig;
else
basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
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', 0, ...
'pam_level', M, ...
'precomp_amp', [], ...
'rop_attenuation', 0, ...
'symbolrate', [], ...
'v_awg', [], ...
'v_bias', [], ...
'wavelength', 1310 ...
);
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','BERs.ber'};
[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))));
Tx_bits = load([basePath, char(dataTable.tx_bits_path(2))]);
Tx_bits = Tx_bits.Bits;
Symbols = load([basePath, char(dataTable.tx_symbols_path(2))]);
Symbols = Symbols.Symbols;
Scpe_load = load([basePath, char(dataTable.rx_sync_path(2))]);
Scpe_cell = Scpe_load.S;
% Raw_signal = load([basePath, char(dataTable.rx_raw_path(1))]);
% Raw_signal = Raw_signal.Scpe_sig_raw;
fsym = Symbols.fs;
end
output = struct();
for occ = 1:1%length(Scpe_cell)
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,S] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym);
% Scpe_sig.spectrum("displayname",'Rx (Scpe+Sync+Resample)','fignum',100,'normalizeTo0dB',0);
Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.6,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
Scpe_sig.spectrum("displayname",'Rx (Scpe+Sync+Resample+LPF)','fignum',110,'normalizeTo0dB',0);
Scpe_sig.plot("displayname",'Filtered Scope Signal','fignum',111,'clear',1);
Scpe_sig.eye(fsym,M);
%%% EQUALIZING
switch eq_mode
case equalizer_structure.ffe
%FFE
if db_precode
Bits_ = PAMmapper(M,0).demap(Symbols);
else
Bits_ = Bits;
end
eq_ffe = EQ("Ne",ffe_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_ffe = 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_ffe = 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);
[eq_sig,Eq_noise,ber_ffe(occ),totalErrors] = vnle( eq_ffe,M,Scpe_sig ,Symbols, Bits_);
Eq_noise.spectrum("displayname",'Noise Spectrum after FFE','fignum',41,'normalizeTo0dB',0);
fprintf('BER FFE: %.2e \n',ber_ffe(occ));
case equalizer_structure.vnle
%VNLE
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",0);
[Eq_sig,Eq_noise] = eq_vnle.process(Scpe_sig,Symbols);
Eq_sig = PAMmapper(M,0).quantize(Eq_sig);
if emulate_precode && db_precode == 0
% emulation
Eq_sig = Duobinary().encode(Eq_sig);
Eq_sig = Duobinary().decode(Eq_sig);
Symbols= Duobinary().encode(Symbols);
Symbols = Duobinary().decode(Symbols);
Tx_bits = PAMmapper(M,0).demap(Symbols);
elseif db_precode == 1 && db_encode == 0 && discard_precode == 1
% normal dsp for precoded sequence
Tx_bits = PAMmapper(M,0).demap(Symbols);
elseif db_precode == 1 && db_encode == 1
error('not implemented')
elseif db_precode == 1 && db_encode == 0
Eq_sig = Duobinary().encode(Eq_sig);
Eq_sig = Duobinary().decode(Eq_sig);
end
% M = numel(unique(tx_symbols.signal));
Rx_bits = PAMmapper(M,0).demap(Eq_sig);
[~,numErrors,ber,~] = calc_ber(Rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
Eq_noise.spectrum("displayname",'Noise Spectrum after VNLE','fignum',41,'normalizeTo0dB',0);
output.ber_vnle(occ) = ber;
if 1c
figure(51);
clf
title(sprintf('DB coded PAM after EQ ; BER: %1.2e',M, ber ));
constellation = unique(Symbols.signal);
received = NaN(numel(constellation),length(Symbols));
for lvl = 1:numel(constellation)
%Separate the equalized signal into the
%respective levels based on the actually
%transmitted level!
received(lvl,Symbols.signal==constellation(lvl)) = Eq_sig.signal(Symbols.signal==constellation(lvl));
intermediate = received(lvl,:);
cnt(lvl) = numel(intermediate(~isnan(intermediate)));
hold on
histogram(received(lvl,:),1000,"EdgeAlpha",0,'DisplayName',['Lvl ',num2str(lvl),' | ',num2str(cnt(lvl)),' entries']);
end
legend
end
Eq_sig.eye(fsym,M,"displayname",'Eye after EQ','fignum',52);
Eq_sig.spectrum("displayname",'Spectrum after EQ','fignum',53,'normalizeTo0dB',1);
Scpe_sig.spectrum("displayname",'Spectrum before EQ','fignum',53,'normalizeTo0dB',1);
fprintf('BER FFE: %.2e \n',ber);
case equalizer_structure.vnle_pf_mlse
%VNLE + PF + MLSE
eq_mlse = 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_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);
pf_ = Postfilter("ncoeff",2);
mlse_ = MLSE("DIR",[0,0],"duobinary_output",0,"M",[],"trellis_states",[]);
%FFE or VNLE
[Eq_sig,Eq_noise] = eq_mlse.process(Scpe_sig,Symbols);
Eq_sig = pf_.process(Eq_sig,Eq_noise);
%M = numel(unique(tx_symbols.signal));
mlse_.DIR = pf_.burg_coeff;
mlse_.trellis_states = PAMmapper(M,0).levels;
mlse_.M = M;
Eq_sig = mlse_.process(Eq_sig);
pf_.showFilter(Eq_noise);
% Eq_noise.spectrum("displayname",'Noise Spectrum after VNLE+PF','fignum',41,'normalizeTo0dB',0);
if emulate_precode && db_precode == 0
% emulation
Eq_sig = Duobinary().encode(Eq_sig);
Eq_sig = Duobinary().decode(Eq_sig);
Symbols= Duobinary().encode(Symbols);
Symbols = Duobinary().decode(Symbols);
Tx_bits = PAMmapper(M,0).demap(Symbols);
elseif db_precode == 1 && db_encode == 0 && discard_precode == 1
% normal dsp for precoded sequence
Tx_bits = PAMmapper(M,0).demap(Symbols);
elseif db_precode == 1 && db_encode == 1
error('not implemented')
elseif db_precode == 1 && db_encode == 0
Eq_sig = Duobinary().encode(Eq_sig);
Eq_sig = Duobinary().decode(Eq_sig);
end
Rx_bits = PAMmapper(M,0).demap(Eq_sig);
[~,numErrors,ber,~] = calc_ber(Rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
output.ber_mlse(occ) = ber;
output.pf_taps(occ,:) = pf_.burg_coeff;
fprintf('BER MLSE: %.2e \n',ber);
case equalizer_structure.db_precoded
%EQ targets DB => less precompensation; pre-coded
mlse_db_pre = MLSE("DIR",[1,1],"duobinary_output",1,"M",M,"trellis_states",PAMmapper(M,0).levels);
eq_db_pre = 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_sig,Eq_noise,ber_db(occ),totalErrors] = duobinary_target(eq_db_pre, mlse_db_pre,M, Scpe_sig ,Symbols, Bits);
Eq_noise.spectrum("displayname",'Noise Spectrum after DB','fignum',41,'normalizeTo0dB',0);
%->append BER to DB
fprintf('BER VNLE+DB: %.2e \n',ber_db(occ));
case equalizer_structure.db_encoded
%db signaling => db encoded
mlse_db_enc = MLSE("DIR",[1,1],"duobinary_output",1,"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);
[eq_sig,Eq_noise,ber_db_enc,totalErrors] = duobinary_signaling(eq_db_enc, mlse_db_enc,M, Scpe_sig ,Symbols, Bits);
%->append BER to DB
fprintf('BER DB: %.2e \n',ber_db_enc(occ));
end
autoArrangeFigures;
end
if ~isempty(curFolder)
cd(curFolder);
end
end

227
projects/IMDD_base_system/imddmodel.m
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@@ -1,227 +0,0 @@
function output = imddmodel(sir,dcmode)
for realiz = 1:3
rng(realiz);
%% Set Simulation Variables
delay = 0;10+(10*realiz); %mpi delay in meter
fiblen = 0; %main link in km
laser_linewidth =0e6;
O = 18; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% B) CONSTRUCT ALL CLASSES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
digimod = PAMmapper(M,0);
pulseform = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.027);
awg = AWG('fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
u_pi = 3.5;
vbias = (0.5*u_pi)-u_pi;
extmodlaser = EML("mode",eml_mode.im_cosinus,"power",5,"fsimu",fsimu,"lambda",1550,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth);
fib = Fiber("fsimu",fdac*kover,"fiber_length",fiblen,"alpha",0.2,"D",16,"lambda0",thz2nm(193.1),"gamma",0);
reflectionpoint = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",sir);
reflectionprop = Fiber("fsimu",fdac*kover,"fiber_length",delay/1000,"alpha",0.2,"D",16,"lambda0",1550,"gamma",0);
opticatten = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",-8);
phdiode = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20);
lp_diode = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
scp = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"lpf_bw",113e9,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",6,"quantbuffer",0.1,'block_dc',1);
eq = EQ("K",2,"plottrain",0,"plotfinal",0,...
"training_length",4096,"training_loops",2,...
"Ne",[50,5,3],"Nb",[3,2,2],...
"DCmu",0.05,"DDmu",[0.0004 0.0005 0.0006 0.0007 ],"DFEmu",0.005,"FFEmu",0.005,...
"dd_loops",2,"epsilon",[10 100 1000 ],"M",2,...
"thres",[0.005 0.004 0.0005 ],"l1act",0,"delay",0,"rho",0.0005,"ideal_dfe",0,"DB_aim",0);
eq2 = EQ_silas("Ne",[20,0,0],"Nb",[3,0,0],"trainlength",4096,"mu_dc_dd",0.005,"mu_dc_train",0.05,...
"mu_ffe_train",0.005,"mu_combined_dd",[0.0004 0.0006 0.0003 0.005],"ddloops",3,"dcmode",dcmode);
%% C) PROCESS TX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% 2 ) Build Inf. signal class
bits = Informationsignal(bitpattern);
% 3) Digi modulation -> PAM-M signal
digimod_out = digimod.map(bits);
% 4) Pulse shaping -> racos
X = pulseform.process(digimod_out);
% 5) AWG (lowpass, quantization, sample and hold)
X = awg.process(X);
% 6) Lowpass behavior of laser and hf-cable? why twice?
X = lp_laser.process(X);
X = lp_laser.process(X);
% 7) Normalize signal
X = X.normalize("mode","oneone");
X.signal = X.signal .* 1.3800;
%% D) PROCESS OPTICAL CHANNEL %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Laser; Modulation -> OPTICAL DOMAIN
[X,extmodlaser] = extmodlaser.process(X);
% 2) Main fiber propagation
X = fib.process(X);
if delay ~= 0
% 3) Reflection
% Reflection is just an attenuation
R = reflectionpoint.process(X);
% Propagate back and forth (actual fiber propagation)
R = reflectionprop.process(R);
% Delay the reflected signal
[R,n] = R.delay("delay_meter",delay);
% Add together
X.signal = X.signal(n:end);
R.signal = R.signal(n:end);
%disp(['SIR ',num2str(10*log10(X.power/R.power))]);
X = X+R;
% 4) Equalize
% cut reference signal to correct length (nessecary due to MPI delay)
digimod_out.signal = digimod_out.signal(round(n * fsym/fsimu) : end,:);
bitpattern = bitpattern(round(n * fsym/fsimu):end,:);
end
% 4) Attenuation
X = opticatten.process(X);
% X = edfaamp.process(X);
% 5) Photo Diode -> ELECTRICAL DOMAIN
X = phdiode.process(X);
X = lp_diode.process(X);
%% E) PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Oscilloscope (Sampling to f_adc; Quantization; Bandwidth Limitation)
X = scp.process(X);
% 2) Resample to 2x symbol rate
X = X.resample("fs_out",2*fsym,"fs_in",fadc);
% 3) Normalize
Eq_in = X.normalize("mode","rms");
% MPI reduction DC removal BEFORE EQ
wl = 3000; % symbols
Eq_in.signal = Eq_in.signal - 1/wl .* movsum( Eq_in.signal,[wl/2,wl/2]);
% Equalize Signal
[Eq_out] = eq2.process(Eq_in,digimod_out);
%% A1: MPI reduction DC removal
wl = 1000; % symbols
yk_dcsm = Eq_out;
yk_dcsm.signal = Eq_out.signal - 1/wl .* movsum( Eq_out.signal,[wl/2,wl/2]);
%% A2: MPI reduction Level wise error removal
yk_lvsm = Eq_out;
yk_lvlp = Eq_out;
pre_decision_level_uni = digimod.decide_pamlevel(Eq_out);
pre_decision_level_bi = ( pre_decision_level_uni*2-3 ) .* 1/sqrt(5);
e = Eq_out.signal - pre_decision_level_bi;
lp_mpi = Filter('filtdegree',1,"f_cutoff",2e6,"fsamp",fsym,"filterType",filtertypes.bessel_inp);
filtered = lp_mpi.process(e);
wl = 30; % symbols
smoothed = ( 1/wl .* movsum(e,[wl/2,wl/2]) );
% remove interference
for level = 0:3
yk_lvsm.signal(pre_decision_level_uni==level) = yk_lvsm.signal(pre_decision_level_uni==level) - smoothed(pre_decision_level_uni==level);
yk_lvlp.signal(pre_decision_level_uni==level) = yk_lvlp.signal(pre_decision_level_uni==level) - filtered(pre_decision_level_uni==level);
end
%% PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Digi Demod
d_bm = digimod.demap(Eq_out);
d_dcsm = digimod.demap(yk_dcsm);
d_lvsm = digimod.demap(yk_lvsm);
d_lvlp = digimod.demap(yk_lvlp);
% 2) BER
dbit = length(d_bm.signal)-length(bitpattern);
[~,errors_bm,ber_bm(realiz),loc] = calc_ber(d_bm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",1);
[~,errors_dcsm,ber_dcsm(realiz)] = calc_ber(d_dcsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvsm,ber_lvsm(realiz)] = calc_ber(d_lvsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvlp,ber_lvlp(realiz)] = calc_ber(d_lvlp.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
end
output.ber_bm = mean(ber_bm);
output.ber_dcsm = mean(ber_dcsm);
output.ber_lvsm = mean(ber_lvsm);
output.ber_lvlp = mean(ber_lvlp);
end

46
projects/IMDD_base_system/parameters.m Normal file
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% TX
M = 4;
fsym = 180e9;
f_nyquist = fsym/2;
apply_pulsef = 0;
fdac = 2*fsym;%256e9;
fadc = 2*fsym;%256e9;
fdac = 256e9;
fadc = 256e9;
random_key = 1;
db_precode = 0;
emulate_precode = 0;
discard_precode = 0;
db_encode = 0;
% duob_mode = db_mode.db_emulate;
emulate_db = 1;
rcalpha = 0.05;
kover = 16;
vbias_rel = 0.5;
u_pi = 2.9;
vbias = -vbias_rel*u_pi;
laser_wavelength = 1310;
laser_linewidth = 0;
tx_bw_nyquist = 1.5;
% Channel
link_length = 1;
% RX
rop = -8;
rx_bw_nyquist = 0.7;
% EQ
eq_mode = equalizer_structure.vnle_pf_mlse;
ffe_order=[50,0,0];
vnle_order=[50,7,7];
dfe_order = [2 0 0];
len_tr = 4096*2;
mu_ffe = [0.0004 0.0004 0.0004];
mu_dfe = 0.0004;
mu_dc = 0.00;
dfe_ = sum(dfe_order)>0;

208
projects/IMDD_base_system/run_loop.m
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clear
sir_loop = [-25:2:-13];
sir_loop = 0;
lw_loop = [1,2,3];
data = cell(length(sir_loop),length(lw_loop));
iterations=size(data);
parfor ix = 1:numel(data)
[u1,u2] = ind2sub(iterations,ix);
output = imddmodel(sir_loop(u1),lw_loop(u2));
data{ix} = output;
end
for sir = 1:size(data,1)
for lw = 1:size(data,2)
ber_bm(sir,lw) = data{sir,lw}.ber_bm;
ber_dcsm(sir,lw) = data{sir,lw}.ber_dcsm;
ber_lvsm(sir,lw) = data{sir,lw}.ber_lvsm;
ber_lvlp(sir,lw) = data{sir,lw}.ber_lvlp;
end
end
col = [ 0.6510 0.8078 0.8902
0.6980 0.8745 0.5412
0.9922 0.7490 0.4353];
figure(22)
for l = 1:numel(lw_loop)
hold on
plot(sir_loop,ber_bm(:,l),'DisplayName',['DC mode:', num2str(lw_loop(l)),', Linewidth= 50 MHz'],'Marker','o','MarkerFaceColor',col(l,:),'Color',col(l,:),'LineWidth',2,'LineStyle','--');
end
set(gca,'yscale','log');
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
set(gca,'xdir','reverse');
%% Plot
col = linspecer(8);
figure(1)
hold on
m = ["x","o","pentagram","hexagram","*","+"];
cnt = 1;
for d = 1%:size(data,2)
for dc = 1:size(data,1)
for lw = 1:size(data,3)
ber_bm(dc,d,lw) = (data{dc,d,lw}.ber_bm);
ber_dcsm(dc,d,lw) = data{dc,d,lw}.ber_dcsm;
ber_lvsm(dc,d,lw) = data{dc,d,lw}.ber_lvsm;
ber_lvlp(dc,d,lw) = (data{dc,d,lw}.ber_lvlp);
end
end
i = 1;
comm_dn = [];% ['EQ DC Tap: ',num2str(dc_tap_loop(d)),' m'];
title("Dependency on Laser Linewidth; B2B; Delay : 2*50m")
plot(lw_loop*1e-6,mean(squeeze(ber_bm(1:end,d,:)),1),"LineWidth",1.2,"Marker",m(1),"MarkerSize",5,'Color',col(cnt,:),'DisplayName',[' ',comm_dn]);
plot(lw_loop*1e-6,mean(squeeze(ber_dcsm(1:end,d,:)),1),"LineWidth",1,"Marker",m(1+4),"MarkerSize",5,'LineStyle','--','Color',col(cnt,:),'DisplayName',['DC smoothing ',comm_dn],'HandleVisibility','on');
plot(lw_loop*1e-6,mean(squeeze(ber_lvsm(1:end,d,:)),1),"LineWidth",1.2,"Marker",m(1+1),"MarkerSize",5,'LineStyle',':','Color',col(cnt,:),'DisplayName',['Lvl Smoothing ',comm_dn],'HandleVisibility','on');
plot(lw_loop*1e-6,mean(squeeze(ber_lvlp(1:end,d,:)),1),"LineWidth",1,"Marker",m(1+3),"MarkerSize",5,'LineStyle','-.','Color',col(cnt,:),'DisplayName',['Lvl Lowpass ',comm_dn],'HandleVisibility','on');
set(gca,'yscale','log');
set(gca,'xscale','log');
%xticklabels([10 100 1000 10000]);
grid minor
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
ylim([1e-3 4e-2]);
ylabel("BER");
xlabel("Linewidth in MHz")
legend
cnt = cnt+1;
end
figure(1)
contour(lw_loop,sir_loop,thres_a0,16:0.4:21,'LineWidth',2,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("Bandwidth in Multiples of Linewidth");
xlabel("Linewidth in MHz");
% set(gca,'yscale','log');
grid minor
title("MPI removal - Optimization of Lowpass Filter Bandwidth")
figure(2)
contour(lw_loop,sir_loop([1:10,12:end]),thres_a0([1:10,12:end],:),16:0.3:21,'LineWidth',2,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("Window Length");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
title("MPI removal - Optimization of Averaging Window Length")
%% Plot Winlen Contour
hdfec = 3.8e-3.*ones(size(sir_loop));
for dc = 1:size(data,2)
for lw = 1:size(data,3)
for s = 1:size(data,1)
ber_lvlp(s,dc,lw) = data{s,dc,lw}.ber_dcsm;
% ber_dcsm(wl,lw,s) = data{wl,s,lw}.ber_dcsm;
end
a_bm = InterX([sir_loop;squeeze(ber_lvlp(:,dc,lw))'],[sir_loop;hdfec]);
% a_dcsm = InterX([sirloop;squeeze(ber_dcsm(wl,lw,:))'],[sirloop;hdfec]);
try
thres_a0(dc,lw) = -a_bm(1);
%thres_a1(wl,lw) = -a_dcsm(1);
catch
thres_a0(dc,lw) = NaN;
%thres_a1(wl,lw) = NaN;
end
end
end
figure(1)
contour(lw_loop,bw_loop,thres_a0,14:0.2:21,'LineWidth',1.5,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
a = flip(cbrewer2('seq','Spectral',32));
a = [a(1:12,:); a(22:end,:)];
colormap(a);
clim([16 21]);
ylabel("Window Length");
xlabel("Linewidth in MHz");
yticks(bw_loop);
yticklabels(bw_loop);
set(gca,'yscale','log');
set(gca,'xscale','log');
grid minor
%% Plot DC Tap Contour
hdfec = 3.8e-3.*ones(size(bw_loop));
thres_a0 = zeros(size(ber,1),size(ber,3));
thres_a1 = zeros(size(ber,1),size(ber,3));
for dc = 1:size(ber,1)
for lw = 1:size(ber,3)
a_lvsm = InterX([bw_loop;ber(dc,:,lw)],[bw_loop;hdfec]);
thres_a0(dc,lw) = -a_lvsm(1);
a1 = InterX([bw_loop;ber_a1(dc,:,lw)],[bw_loop;hdfec]);
thres_a1(dc,lw) = -a1(1);
end
end
figure(1)
subplot(2,1,1)
contour(lw_loop,sir_loop,thres_a0,16:0.5:21,'LineWidth',3,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("DC Tap");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
subplot(2,1,2)
contour(lw_loop,sir_loop,thres_a1,16:0.5:21,'LineWidth',3,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("DC Tap");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
%% Plot Curves of required SIR to see the minimum a bit better
col = flip(cbrewer2('seq','Spectral',16));
col = col([1:4, 10:end],:);
figure(2)
hold on
for lw = [size(ber,3):-2:2 2 1]
plot(sir_loop,thres_a0(:,lw),'DisplayName',[' Linewidth: ',num2str(lw_loop(lw)*1e-6), ' MHz'],'Color',col(lw,:),'Marker','o','MarkerFaceColor',col(lw,:),'LineWidth',2);
set(gca,'xscale','log');
end
xlabel("DC Tap Value");
ylabel("Required SIR to rech FEC in dB");
%%
col = linspecer(7);
figure(3)
cnt=1;
for lw = [1,2,11]
for i = 1:length(sir_loop)-1
subplot(1,3,cnt)
hold on
plot(-1.*bw_loop,ber(i,:,lw),"LineWidth",2,"Marker","o","MarkerSize",5,'Color',col(i,:),'DisplayName',['DC tap ',num2str(sir_loop(i))]);
plot(-1.*bw_loop,ber_a1(i,:,lw),"LineWidth",2,"Marker","x","MarkerSize",5,'LineStyle','--','Color',col(i,:),'DisplayName',['A1. DC tap ',num2str(sir_loop(i))]);
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
set(gca,'yscale','log');
grid minor
xlim([15,30]);
ylim([1e-4,1e-2]);
xlabel("SIR in dB");
ylabel("BER");
title(['BER for different SIR;',' Linewidth: ',num2str(lw_loop(lw)*1e-6), ' MHz'])
text(25,4.2e-3,"FEC $3.8 e^{-3}$");
end
cnt = cnt+1;
end
legend

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projects/IMDD_base_system/rx_simulation.m Normal file
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%%%%%% ROP %%%%%%
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
%%%%%% PD Square Law %%%%%%
Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
Rx_sig = Filter('filtdegree',4,"f_cutoff",rx_bw_nyquist.*f_nyquist,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(Rx_sig);
% %%%%%% Low-pass Scope %%%%%%
Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
% Rx_sig.spectrum("displayname",'Analog Rx Spectrum','fignum',100,'normalizeTo0dB',1);
%%%%%% Scope %%%%%%
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);

399
projects/IMDD_base_system/setup_simulation.m
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lw = [0.1e6 1e6 10e6];
for lp = 1
rng(9);
%% A) Set Simulation Variables
sir = -18;
delay = 50; %mpi delay in meter
fiblen = 0; %main link in km
laser_linewidth =5e6;
O = 19; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% B) CONSTRUCT ALL CLASSES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
digimod = PAMmapper(M,0);
pulseform = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.027);
awg = AWG('fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
u_pi = 3.5;
vbias = (0.5*u_pi)-u_pi;
extmodlaser = EML("mode",eml_mode.im_cosinus,"power",5,"fsimu",fsimu,"lambda",1310,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth);
fib = Fiber("fsimu",fdac*kover,"fiber_length",fiblen,"alpha",0.2,"D",16,"lambda0",thz2nm(193.1),"gamma",0);
reflectionpoint = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",sir);
reflectionprop = Fiber("fsimu",fdac*kover,"fiber_length",delay/1000,"alpha",0.2,"D",16,"lambda0",1550,"gamma",0);
opticatten = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",-8);
phdiode = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20);
lp_diode = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
scp = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"lpf_bw",113e9,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",6,"quantbuffer",0.1,'block_dc',1);
eq = EQ("K",2,"plottrain",0,"plotfinal",0,...
"training_length",4096,"training_loops",2,...
"Ne",[50,5,3],"Nb",[3,0,0],...
"DCmu",0.05,"DDmu",[0.0004 0.0005 0.0006 0.0007 ],"DFEmu",0.005,"FFEmu",0.00,...
"dd_loops",2,"epsilon",[10 100 1000 ],"M",2,...
"thres",[0.005 0.004 0.0005 ],"l1act",0,"delay",0,"rho",0.0005,"ideal_dfe",0,"DB_aim",0);
eq = EQ_silas("Ne",[50,5,3],"Nb",[2,0,0],"trainlength",4096,...
"sps",2,...
"mu_dc_dd",0.05,...
"mu_dc_train",0.05,...
"mu_ffe_train",0,...
"mu_dfe_train",0.005,...
"mu_ffe_dd",[0.0004 0.0004 0.0004],...
"mu_dfe_dd",0.005,...
"ddloops",3,...
"trainloops",4,...
"dcmode",1);
%% C) PROCESS TX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% 2 ) Build Inf. signal class
bits = Informationsignal(bitpattern);
% 3) Digi modulation -> PAM-M signal
digimod_out = digimod.map(bits);
% 4) Pulse shaping -> racos
X = pulseform.process(digimod_out);
% 5) AWG (lowpass, quantization, sample and hold)
X = awg.process(X);
% 6) Lowpass behavior of laser and hf-cable? why twice?
X = lp_laser.process(X);
% X = lp_laser.process(X);
% 7) Normalize signal
X = X.normalize("mode","oneone");
X.signal = X.signal .* 1.3800;
%% D) PROCESS OPTICAL CHANNEL %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Laser; Modulation -> OPTICAL DOMAIN
[X,extmodlaser] = extmodlaser.process(X);
% 2) Main fiber propagation
X = fib.process(X);
if delay ~= 0
% 3) Reflection
% Reflection is just an attenuation
R = reflectionpoint.process(X);
% Propagate back and forth (actual fiber propagation)
R = reflectionprop.process(R);
% Delay the reflected signal
[R,n] = R.delay("delay_meter",delay);
% Add together
col = cbrewer2('qual','Paired',8);
xax = (1:X.length);% / fsimu * (physconst("LightSpeed")/1.4677);
xax_sec = (1:X.length) / fsimu .* 1e6;%* (physconst("LightSpeed")/1.4677);
xax_sec = xax_sec(n:end).';
thresh = pi/4;
phaseX = phase(X.signal(n:end));
phaseR = phase(R.signal(n:end));
phasediff = wrapToPi(phaseX-phaseR);
[pos_high] = find(abs(phasediff)>thresh);
[pos_low] = find(abs(phasediff)<=thresh);
figure(6);hold on;histogram(phaseX-phaseR,500,'EdgeAlpha',0)
figure()
subplot(3,1,1)
scatter(xax_sec.',wrapToPi(phaseX),4,'.','MarkerEdgeColor',col(2,:),'DisplayName','Desired Signal')
hold on
scatter(xax_sec,wrapToPi(phaseR),4,'.','MarkerEdgeColor',col(1,:),'DisplayName','Reflected Signal')
hold on
lg = legend;
lg.Location = "southwest";
xlim([xax_sec(1) xax_sec(end)]);
xlabel('time in $\mu$s')
ylim([-pi pi]);
yticks([-pi 0 pi])
yticklabels({'$-\pi$',0, '$\pi$'})
ylabel('$\phi$')
subplot(3,1,2)
scatter(xax_sec(pos_low),phasediff(pos_low),4,'.','MarkerEdgeColor',col(5,:),'DisplayName','Phase Difference')
hold on
scatter(xax_sec(pos_high),phasediff(pos_high),4,'.','MarkerEdgeColor',col(2,:),'HandleVisibility','off')
% yline(thresh,'LineWidth',2,'LineStyle','-','HandleVisibility','off')
% yline(-thresh,'LineWidth',2,'LineStyle','-','HandleVisibility','off')
lg = legend;
lg.Location = "southwest";
xlabel('time in $\mu$s')
xlim([xax_sec(1) xax_sec(end)]);
ylim([-pi pi]);
yticks([-pi 0 pi])
yticklabels({'$-\pi$',0, '$\pi$'})
ylabel('$\Delta \phi$')
X.signal = X.signal(n:end);
R.signal = R.signal(n:end);
X1 = X;
X = X+R;
subplot(3,1,3)
scatter(xax_sec(pos_low),abs(X.signal(pos_low).^2)*1000,4,'.','MarkerEdgeColor',col(5,:),'DisplayName','Constructive Interference');
hold on
scatter(xax_sec(pos_high),abs(X.signal(pos_high).^2)*1000,4,'.','MarkerEdgeColor',col(2,:),'DisplayName','Destructive Interference');
%scatter(xax_sec(pos_high),abs(X1.signal(pos_high).^2)*1000,4,'.','MarkerEdgeColor',col(1,:),'MarkerFaceAlpha',0.3,'DisplayName','Destructive Interference');
xlim([xax_sec(1) xax_sec(end)]);
xlabel('time in $\mu$s')
lg = legend;
lg.Location = "southwest";
ylabel('Optical power in mW');
disp(['SIR ',num2str(10*log10(X.power/R.power))]);
% cut reference signal to correct length (nessecary due to MPI delay)
digimod_out.signal = digimod_out.signal(round(n * fsym/fsimu) : end,:);
bitpattern = bitpattern(round(n * fsym/fsimu):end,:);
end
% plot(angle(R.signal))
% 4) Attenuation
X = opticatten.process(X);
% 5) Photo Diode -> ELECTRICAL DOMAIN
X = phdiode.process(X);
X = lp_diode.process(X);
%% E) PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Oscilloscope (Sampling to f_adc; Quantization; Bandwidth Limitation)
X = scp.process(X);
% 2) Resample to 2x symbol rate
X = X.resample("fs_out",2*fsym,"fs_in",fadc);
% 3) Normalize
Eq_in = X.normalize("mode","rms");
% 4) Equalize
% MPI reduction DC removal BEFORE EQ
wl = 3000; % symbols
Eq_in.signal = Eq_in.signal - 1/wl .* movsum( Eq_in.signal,[wl/2,wl/2]);
% Equalize Signal
[Eq_out] = eq.process(Eq_in,digimod_out);
%% A1: MPI reduction DC removal
wl = 1000; % symbols
yk_dcsm = Eq_out;
yk_dcsm.signal = Eq_out.signal - 1/wl .* movsum( Eq_out.signal,[wl/2,wl/2]);
%% A2: MPI reduction Level wise error removal
yk_lvsm = Eq_out;
yk_lvlp = Eq_out;
pre_decision_level_uni = digimod.decide_pamlevel(Eq_out);
pre_decision_level_bi = ( pre_decision_level_uni*2-3 ) .* 1/sqrt(5);
e = Eq_out.signal - pre_decision_level_bi;
lp_mpi = Filter('filtdegree',1,"f_cutoff",2e6,"fsamp",fsym,"filterType",filtertypes.bessel_inp);
filtered = lp_mpi.process(e);
wl = 30; % symbols
smoothed = ( 1/wl .* movsum(e,[wl/2,wl/2]) );
% remove interference
for level = 0:3
yk_lvsm.signal(pre_decision_level_uni==level) = yk_lvsm.signal(pre_decision_level_uni==level) - smoothed(pre_decision_level_uni==level);
yk_lvlp.signal(pre_decision_level_uni==level) = yk_lvlp.signal(pre_decision_level_uni==level) - filtered(pre_decision_level_uni==level);
end
% Calc EVM
evm_bm = calc_evm(Eq_out.signal, pre_decision_level_bi);
evm_dcsm = calc_evm(yk_dcsm.signal, pre_decision_level_bi);
evm_lsm = calc_evm(yk_lvsm.signal, pre_decision_level_bi);
evm_llp = calc_evm(yk_lvlp.signal, pre_decision_level_bi);
% figure(1);bar([evm_bm' evm_dcsm' evm_llp' evm_lsm']);ylim([0.01 0.1]);set(gca,'yscale','log');
%% PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Digi Demod
d_bm = digimod.demap(Eq_out);
d_dcsm = digimod.demap(yk_dcsm);
d_lvsm = digimod.demap(yk_lvsm);
d_lvlp = digimod.demap(yk_lvlp);
% 2) BER
dbit = length(d_bm.signal)-length(bitpattern);
[~,errors_bm,ber_bm,loc] = calc_ber(d_bm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",1);
[~,errors_dcsm,ber_dcsm] = calc_ber(d_dcsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvsm,ber_lvsm] = calc_ber(d_lvsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvlp,ber_lvlp] = calc_ber(d_lvlp.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
% Display BER
disp(['BER benchmark: ', sprintf('%2E',ber_bm), ' ERRORS: ' ,num2str(sum(errors_bm))]);
disp(['BER dc smooth (A1): ', sprintf('%2E',ber_dcsm), ' ERRORS: ' ,num2str(sum(errors_dcsm))]);
disp(['BER lv smooth (A2): ', sprintf('%2E',ber_lvsm), ' ERRORS: ' ,num2str(sum(errors_lvsm))]);
disp(['BER lv lowpas: ', sprintf('%2E',ber_lvlp), ' ERRORS: ' ,num2str(sum(errors_lvlp))]);
%% Generate some Plots
if 1
% SCATTER
col = cbrewer2('Paired',8);
xax = 1:Eq_out.length;
figure(3)
sgtitle('')
subplot(1,3,1)
hold on
eq_decision = digimod.decide_pamlevel(Eq_out);
true_symbols = digimod.decide_pamlevel(digimod_out);
xindices = 1:Eq_in.length;
errorpos = find(loc~=0)*2;
errorpos(errorpos>length(Eq_in.signal)) = length(Eq_in.signal);
correct = find(loc==0)*2;
correct(correct>length(Eq_in.signal)) = length(Eq_in.signal);
scatter(xindices(correct),Eq_in.signal(correct),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After EQ');
hold on
scatter(xindices(errorpos),Eq_in.signal(errorpos),6,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xindices(end)]);
ylim([-3 3]);
xlabel('Sampling Index')
ylabel('Amplitude')
a = legend;
a.Location = "best";
subplot(1,3,2)
xindices = 1:Eq_out.length;
xax_sec = (1:Eq_out.length) / fsym .* 1e6;
scatter(xax_sec(loc==0),Eq_out.signal(loc==0),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After EQ');
hold on
scatter(xindices(loc~=0),Eq_out.signal(loc~=0),8,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xax(end)]);
ylim([-2 2]);
xlabel('Sampling Index')
ylabel('Amplitude')
legend
a = legend;
a.Location = "best";
hold off
subplot(1,3,3)
xindices = 1:yk_lvsm.length;
scatter(xindices(loc==0),yk_lvsm.signal(loc==0),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After A2');
hold on
scatter(xindices(loc~=0),yk_lvsm.signal(loc~=0),8,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xax(end)]);
ylim([-2 2]);
xlabel('Sampling Index')
ylabel('Amplitude')
legend
a = legend;
a.Location = "best";
hold off
end
if 0
col = cbrewer2('Paired',8);
figure(11)
clf
subplot(2,1,1)
hold on
plot(digimod_out.signal(4150:4175),'DisplayName','Tx','Color',col(1,:),'LineWidth',3);
plot(Eq_out.signal(4150:4175),'DisplayName','Rx after EQ','Color',col(6,:),'LineWidth',1);
title('Modulated Sequence Zoom');
legend
hold off
subplot(2,1,2)
hold on
stem(d_bm.signal(4150:4175,1),'DisplayName','Tx','Color',col(1,:),'LineStyle','-','LineWidth',5)
stem(bitpattern(4150:4175,1)','DisplayName','Rx','Color',col(6,:),'LineStyle','--','LineWidth',2)
title('Bitpattern Tx - Rx');
legend
hold off
end
end

74
projects/IMDD_base_system/submit_simulations.m Normal file
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function wh = submit_simulations(wh,db,options)
arguments
wh
db
options.parallel = 1;
options.simulation_mode = 1;
end
%%% 2) SUBMIT SIMULATION
% Initialize job results
if options.parallel
if isempty(gcp('nocreate'))
parpool;
end
results = parallel.FevalFuture.empty();
else
results = [];
end
lin_idx = 1;
for lin_idx = 1:wh.getLastLinIndice
optionalVars = struct();
if ~isempty(wh.getDimension)
% Build the optionalVars struct
[parametervalues,parameternames]=wh.getPhysIndicesByLinIndex(lin_idx);
for pidx = 1:numel(parameternames)
optionalVars.(parameternames{pidx}) = parametervalues{pidx};
end
end
%%% SIMULATION HERE
if options.parallel
numOutputs = 1;
results(lin_idx) = parfeval(@imdd_model, numOutputs, options.simulation_mode, db, optionalVars);
else
finalresults{lin_idx} = imdd_model(options.simulation_mode,db,optionalVars);
wh.addValueToStorageByLinIdx(finalresults{lin_idx}, 'ber', lin_idx);
end
end
if options.parallel
%%% 4) Setup waitbar
h = waitbar(0, 'Processing Simulations...');
% Helper function to compute progress
updateWaitbar = @(~) waitbar(mean(arrayfun(@(f) strcmp(f.State, 'finished'), results)), h);
fprintf('Fetching results... \n');
% Update the waitbar after each simulation
updateWaitbarFutures = afterEach(results, updateWaitbar, 0);
% Close the waitbar after all simulations complete
afterAll(updateWaitbarFutures, @(~) delete(h), 0);
%%% 7) Fetch final results after all computations
fetchOutputs(results);
for ridx = 1:length(results)
wh.addValueToStorageByLinIdx(results(ridx).OutputArguments{1}, 'ber', ridx);
end
end
end

31
projects/IMDD_base_system/tx_simulation.m Normal file
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Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"rrcalpha",rcalpha);
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
"fsym",fsym,"M",M,"order",19,"useprbs",1,...
"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",'Digi Spectrum','fignum',10,'normalizeTo0dB',1);
%%%%% AWG
% El_sig = M8199A("kover",kover).process(Digi_sig);
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1).process(Digi_sig);
% El_sig.spectrum("displayname",'Digi Spectrum','fignum',100,'normalizeTo0dB',0);
% El_sig = El_sig.setPower(0,"dBm");
%%%%% Low-pass el. components %%%%%%
El_sig = Filter('filtdegree',4,"f_cutoff",tx_bw_nyquist.*f_nyquist,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(El_sig);
%%%%% Electrical Driver Amplifier %%%%%%
El_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",3).process(El_sig);
El_sig = El_sig.normalize("mode","oneone");
%%%%% MODULATE E/O CONVERSION %%%%%%
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1).process(El_sig);

BIN
projects/MPI_August/auswertung/M_4_datarate_224_sir_35_laser_linewidth_1000000_pn_key_1_rop_0.mat Normal file
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Binary file not shown.

7
python_version/amp_mode.py
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@@ -1,7 +0,0 @@
from enum import IntEnum
class amp_mode(IntEnum):
ideal_no_noise = 1
edfa_increase_nase = 2
edfa_replace_nase = 3
# edfa_set_osnr = 4 TODO: implement mode to achieve desired OSNR

86
python_version/ampclass.py
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@@ -1,86 +0,0 @@
import numpy as np
import scipy.constants as Constant
import amp_mode
import gain_mode
import nase_mode
class Amplifier:
def __init__(self, options=None):
self.amp_mode = options.amp_mode if options and hasattr(options, 'amp_mode') else amp_mode.ideal_no_noise
self.gain_mode = options.gain_mode if options and hasattr(options, 'gain_mode') else gain_mode.output_power
self.nase_mode = options.nase_mode if options and hasattr(options, 'nase_mode') else nase_mode.pass_ase
self.amplification_db = options.amplification_db if options and hasattr(options, 'amplification_db') else 0
self.noifig = options.noifig if options and hasattr(options, 'noifig') else 0
self.fsimu = options.fsimu if options and hasattr(options, 'fsimu') else None
def process(self, signalclass_in):
signalclass_in = self.process_(signalclass_in)
lbdesc = 'Amp '
signalclass_in = signalclass_in.logbookentry(lbdesc)
signalclass_out = signalclass_in
return signalclass_out
def process_(self, X_in):
a_lin = self.calculateGain(X_in.signal)
X_in.signal = a_lin * X_in.signal
if isinstance(X_in, Opticalsignal):
if self.amp_mode == amp_mode.ideal_no_noise:
X_in.nase = self.onlyAmplifyAse(X_in.nase, a_lin)
elif self.amp_mode == amp_mode.edfa_increase_nase:
X_in.nase = self.increaseAse(X_in.nase, a_lin, self.noifig, X_in.lambda_)
elif self.amp_mode == amp_mode.edfa_replace_nase:
X_in.nase = self.replaceAse(X_in.nase, a_lin, self.noifig, X_in.lambda_)
if self.nase_mode == nase_mode.generate_ase:
nase = X_in.nase
fs = X_in.fs
dimension = X_in.signal.shape
nase_numeric = self.generateAseNoise(nase, fs, dimension)
X_in.signal, osnr = self.applyAseToSignal(X_in.signal, nase_numeric)
X_in.nase = 0
elif self.nase_mode == nase_mode.pass_ase:
X_in.nase = X_in.nase
X_out = X_in
return X_out
def calculateGain(self, xin):
if self.gain_mode == gain_mode.output_power:
pow_in = np.mean(np.abs(xin) ** 2)
pow_out = 10 ** (self.amplification_db / 10 - 3)
a_lin = np.sqrt(pow_out / pow_in)
elif self.gain_mode == gain_mode.gain:
a_lin = 10 ** (self.amplification_db / 20)
return a_lin
def onlyAmplifyAse(self, nase_old, a_lin):
nase_amped = a_lin ** 2 * nase_old
return nase_amped
def calculateAseFromThisAmp(self, a_lin, noisefig, lambda_):
h = Constant.Planck
c = Constant.speed_of_light
nase_new = 0.5 * 10 ** (noisefig / 10) * h * c / lambda_ * (a_lin ** 2 - 1)
return nase_new
def increaseAse(self, nase_old, a_lin, noisefig, lambda_):
nase_fromThisAmp = self.calculateAseFromThisAmp(a_lin, noisefig, lambda_)
nase_old = a_lin ** 2 * nase_old
ase_increased = nase_old + nase_fromThisAmp
return ase_increased
def replaceAse(self, nase_old, a_lin, noisefig, lambda_):
nase_fromThisAmp = self.calculateAseFromThisAmp(a_lin, noisefig, lambda_)
nase_new = nase_fromThisAmp
return nase_new
def generateAseNoise(self, nase, fs, dimension):
nase_numeric = (np.random.randn(*dimension) + 1j * np.random.randn(*dimension)) * np.sqrt(nase / 2 * fs)
return nase_numeric
def applyAseToSignal(self, opticalsignal, nase_numeric):
noisy_sig = opticalsignal + nase_numeric
osnr = pow(10 * np.log10(np.mean(np.abs(opticalsignal) ** 2) / np.mean(np.abs(nase_numeric) ** 2)))
print(osnr)
return noisy_sig, osnr

5
python_version/gain_mode.py
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@@ -1,5 +0,0 @@
from enum import IntEnum
class gain_mode(IntEnum):
gain = 1
output_power = 2

5
python_version/nase_mode.py
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@@ -1,5 +0,0 @@
from enum import IntEnum
class nase_mode(IntEnum):
generate_ase = 1
pass_ase = 2

3
python_version/python_trial.py
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@@ -1,3 +0,0 @@
from ampclass import Amplifier
a = Amplifier()

75
test/duobinary_emulation_minimal_example.m Normal file
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useprbs = 1;
M = 4;
randkey = 1;
datarate = 224e9;
fsym = round(datarate / log2(M)) ;
db_pre = 1;
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"rrcalpha",0.05);
[d,Symbols,Bits] = PAMsource(...
"fsym",fsym,"M",M,"order",17,"useprbs",1,...
"fs_out",fsym,...
"applyclipping",0,"clipfactor",1.5,...
"applypulseform",0,"pulseformer",Pform,...
"randkey",1,...
"db_precode",db_pre,"db_encode",0,...
"mrds_code",0,"mrds_blocklength",512).process();
%%%CHANNEL
% s = RandStream('twister','Seed',2);
% start = 10000;
% burstwidth = 100;
% d_burst = d;
% for pos = start:start+burstwidth
% lvls = 1.5 .* PAMmapper(M,0).levels / rms(PAMmapper(M,0).levels);
% d_burst.signal(pos) = d.signal(pos)+randn(s,1,1);
% end
d_resample = d.resample("fs_out",2.*fsym);
eq_ffe = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",1024,"mu_dd",0.0004,"mu_tr",0,"order",25,"sps",2,"decide",1);
d_eq = eq_ffe.process(d_resample,Symbols);
% s = RandStream('twister','Seed',2);
% start = 10000;
% burstwidth = 100;
% d_burst = d_eq;
% for pos = start:start+burstwidth
% lvls = 1.5 .* PAMmapper(M,0).levels / rms(PAMmapper(M,0).levels);
% d_burst.signal(pos) = d_eq.signal(pos)+randn(s,1,1);
% end
%
% d_burst = PAMmapper(M,0).decide_pamlevel(d_burst);
if db_pre
% Entschiedene Symbole codieren: d_DB(n) = d(n) + d(n-1) (im Fall von PAM4 7 level [0 1 2 3 4 5 6])
d_db = Duobinary().encode(d);
% Entschiedene codierte Symbole decodieren: d_dec(n) = d_DB(n) mod4
d_dec = Duobinary().decode(d_db);
else
d_dec = d_burst;
end
% Vergleichen von b(n) und d_dec(n)
Rx_bits = PAMmapper(M,0).demap(d_dec);
Tx_bits = Bits;
[~,error_num,ber,error_pos] = calc_ber(Tx_bits.signal,Rx_bits.signal,"skip_front",0,"skip_end",0,"returnErrorLocation",1);
disp(['BER: ',sprintf('%.1E',ber),' - - PAM-',num2str(M)]);
figure(200)
clf
hold on
idxs = start-10:start+burstwidth+10;
scatter(idxs,d.signal(idxs),'DisplayName',['Orig Signal'],'Marker','o');
scatter(idxs,d_burst.signal(idxs),'DisplayName',['Error Signal'],'Marker','x');
scatter(idxs,d_dec.signal(idxs),'DisplayName',['EQ Signal'],'Marker','x');
yline(PAMmapper(M,0).thresholds,'HandleVisibility','off');
legend
ylim([-2 2])

48
test/duobinary_minimal_example.m
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@@ -1,43 +1,55 @@
useprbs = 1;
M = 6;
M = 4;
randkey = 1;
datarate = 448e9;
fsym = round(datarate / log2(M)) ;
%%%%% PRBS Generation in correct shape for Modulation Format %%%%%%
O = 17; %order of prbs
O = 15; %O of prbs
N = 2^(O); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
bitpattern=[];
if useprbs
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
else
s = RandStream('twister','Seed',randkey);
for i = 1:log2(M)
bitpattern(:,i) = randi(s,[0 1], N, 1);
end
end
state = struct();
para = struct();
if M == 6
bitpattern = reshape(bitpattern,[],1);
bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
para.bl = 2^(O-2);
para.dimension = 5;
else
para.bl = 2^(O-1);
para.dimension = log2(M); %2.5bits/sym -> 2 bit/sym
end
para.rand = 0;
para.order = floor(O / log2(M));
para.skip =0;
para.bruijn = 0;
para.reset_prms = 0;
para.method = 1;
data_in = [];
global loop;
loop = 0;
[data_out,state_] = prms(data_in, state, para);
loop = 1;
[data_out,state_out] = prms(data_in, state_, para);
bitpattern = data_out';
Tx_bits = Informationsignal(bitpattern);
Symbols_tx = PAMmapper(M,0).map(Tx_bits);
Symbols_tx.fs = fsym;
Symbols = Duobinary().precode(Symbols_tx);
Symbols1 = Duobinary().precode(Symbols_tx);
Symbols = Duobinary().encode(Symbols);
Symbols2 = Duobinary().encode(Symbols1);
Symbols = Duobinary().decode(Symbols);
Symbols3 = Duobinary().decode(Symbols2);
Rx_bits = PAMmapper(M,0).demap(Symbols);
Rx_bits = PAMmapper(M,0).demap(Symbols3);
[~,error_num,ber,error_pos] = calc_ber(Tx_bits.signal,Rx_bits.signal,"skip_front",0,"skip_end",0,"returnErrorLocation",1);

32
test/exampleFunction.m Normal file
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function output = exampleFunction(varargin)
% Default values for optional variables
var_1 = 1;
var_2 = 2;
var_4 = 10; % Default value for var4
var_5 = 20; % Default value for var5
var_6 = 30; % Default value for var6
var_7 = 40; % Default value for var7
var_8 = 50; % Default value for var8
var_9 = 60; % Default value for var9
var_10 = 70; % Default value for var10
% Parse optional input arguments
if ~isempty(varargin)
var_s = varargin{1};
if isstruct(var_s)
fields = fieldnames(var_s);
for i = 1:numel(fields)
eval([fields{i}, ' = ', num2str( var_s.(fields{i}) ), ';']);
fprintf("%s <-- %.2f \n",fields{i},var_s.(fields{i}))
end
else
error('Optional variables should be passed as a struct.');
end
end
output = var_4+var_10+var_9+var_8+var_1+var_2;
end

100
test/run_examplefcn.m Normal file
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% Define ranges for variables to iterate over
var1_range = [1, 2, 3, 4, 6];
var2_range = [10, 20];
var3_range = [100, 200];
% Prepare the parallel pool
if isempty(gcp('nocreate'))
parpool; % Start a parallel pool if not already running
end
% Array to hold measurement futures
measurements = parallel.FevalFuture.empty();
% Array to hold DSP results
dsp_results = parallel.Future.empty();
% Nested for loops for all parameter combinations
lin_idx = 1;
for v1 = var1_range
for v2 = var2_range
for v3 = var3_range
% Construct the struct of optional variables for this iteration
optionalVars = struct('var_4', v1, 'var_5', v2, 'var_6', v3);
% Submit the measurement function to the parallel pool
measurements(lin_idx) = parfeval(@measurement, 1, optionalVars);
% Link DSP function to run after measurement completes
dsp_results(lin_idx) = afterEach(measurements(lin_idx), @(output) rundsp(output, optionalVars), 1);
lin_idx = lin_idx + 1; % Increment linear index
end
end
end
% Fetch and display DSP results
final_results = cell(numel(dsp_results), 1);
for i = 1:numel(dsp_results)
fprintf('Fetching DSP result for job %d...\n', i);
final_results{i} = fetchOutputs(dsp_results(i)); % Fetch each DSP result individually
end
fprintf('All DSP evaluations completed.\n');
disp('Final Results:');
disp(final_results);
% --- Measurement Function ---
function output = measurement(varargin)
% Default values for optional variables
var_1 = 1;
var_2 = 2;
var_4 = 10; % Default value for var4
var_5 = 20; % Default value for var5
var_6 = 30; % Default value for var6
var_7 = 40; % Default value for var7
var_8 = 50; % Default value for var8
var_9 = 60; % Default value for var9
var_10 = 70; % Default value for var10
% Parse optional input arguments
if ~isempty(varargin)
var_s = varargin{1};
if isstruct(var_s)
fields = fieldnames(var_s);
for i = 1:numel(fields)
eval([fields{i}, ' = ', num2str( var_s.(fields{i}) ), ';']);
fprintf("%s <-- %.2f \n", fields{i}, var_s.(fields{i}));
end
else
error('Optional variables should be passed as a struct.');
end
end
% Simulate output with a random delay
output = randi(5); % Random result
pause(output); % Simulate processing time
end
% --- DSP Function ---
function output = rundsp(measurement_output, 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)
eval([fields{i}, ' = ', num2str( var_s.(fields{i}) ), ';']);
fprintf("%s <-- %.2f \n", fields{i}, var_s.(fields{i}));
end
else
error('Optional variables should be passed as a struct.');
end
end
% Simulate DSP processing based on measurement output
output = measurement_output + 10; % Add 10 to measurement output
pause(measurement_output); % Simulate DSP processing time
end