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

ECOC Theroy stuff

Browse Source
This commit is contained in:
Silas Oettinghaus
2025-09-26 14:25:52 +02:00
parent 2c0e7a81aa
commit 9d63457030
9 changed files with 397 additions and 175 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
Classes/04_DSP/Sequence Detection/MLSE.m
View File

@@ -54,9 +54,9 @@ classdef MLSE < handle
function [VITERBI_ESTIMATION_SYMBOLS,LLR_maxlogmap,GMI] = process_(obj,data_in,data_ref) function [VITERBI_ESTIMATION_SYMBOLS,LLR_maxlogmap,GMI] = process_(obj,data_in,data_ref)
debug = 0; debug = 1;
trellis_state_mode = 2; % General: States should match the target states of the prev. EQ (EQ's job was to reduce the error between signal and the target) trellis_state_mode = 0; % General: States should match the target states of the prev. EQ (EQ's job was to reduce the error between signal and the target)
% 0 = use provided states (MUST provide the correct states); % 0 = use provided states (MUST provide the correct states);
% 1 = normalize to = 1 rms; % 1 = normalize to = 1 rms;
% 2 = use target symbols; % 2 = use target symbols;
@@ -65,7 +65,7 @@ classdef MLSE < handle
trellis_exclusion = 0; % PAM-6 only (only if data is NOT precoded!) trellis_exclusion = 0; % PAM-6 only (only if data is NOT precoded!)
scale_mode = 2; % scale_mode: scale_mode = 0; % scale_mode:
% 0 = no scaling, % 0 = no scaling,
% 1 = RMS→scale MODEL, % 1 = RMS→scale MODEL,
% 2 = MMSE/time-corr→scale MODEL, % 2 = MMSE/time-corr→scale MODEL,

42
projects/ECOC_2025/load_signal_standalone.m
View File

@@ -1,34 +1,20 @@
db = DBHandler("type","mysql","dataBase",'labor');
fp = QueryFilter();
% fp.where('Runs', 'run_id','EQUALS', 987);
M = 4;
fp.where('Runs', 'pam_level','EQUALS', M);
fp.where('Runs', 'symbolrate','EQUALS', 112e9);
fp.where('Runs', 'fiber_length','EQUALS', 0);
fp.where('Runs', 'is_mpi','EQUALS', 1);
fp.where('Runs', 'interference_path_length','EQUALS', 70);
% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
fp.where('Runs', 'sir','EQUALS',20);
savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
databasePath = 'C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
database_name = 'ecoc2025_loops.db';
db = DBHandler("type","mysql");
% db = DBHandler("pathToDB", [databasePath, database_name],"type","sqlite");
filterParams = db.tables; [dataTable,sql_query] = db.queryDB(fp, db.getTableFieldNames('Runs'));
% filterParams.Configurations = struct('run_id', run_id);
filterParams.Configurations = struct( ...
'symbolrate', 112e9, ... %[224,336,360,390,420,448]
'fiber_length', 0, ...
'db_mode', '"no_db"', ...
'interference_attenuation', 4, ...
'interference_path_length', 300, ...
'is_mpi', 1, ...
'pam_level', 4, ...
'wavelength', 1310, ...
'precomp_amp', [], ...
'signal_attenuation', [], ...
'v_awg', [], ...
'v_bias', [] ...
);
selectedFields = {'Runs.run_id','Runs.tx_bits_path','Runs.tx_signal_path','Runs.tx_symbols_path','Runs.rx_sync_path','Runs.rx_raw_path',...
'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
'Configurations.interference_attenuation', 'Measurements.power_mpi_interference'};
[dataTable,sql_query] = db.queryDB(filterParams, selectedFields);
[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices [~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
@@ -55,7 +41,7 @@ for i = 1:size(dataTable,1)
Scpe_sig_raw = load([savePath, char(dataTable_.rx_raw_path(1))]); Scpe_sig_raw = load([savePath, char(dataTable_.rx_raw_path(1))]);
Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw; Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
% Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0); Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",Scpe_sig_raw.fs,"fs_out",fsym); Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",Scpe_sig_raw.fs,"fs_out",fsym);

86
projects/ECOC_2025/plots_from_database/plot_mpi_trial.m
View File

@@ -1,49 +1,57 @@
% basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
% database = DBHandler("pathToDB",[basePath,'silas_labor.db']);
database = DBHandler("type",'mysql','dataBase','labor');
filterParams = database.tables; % dsp_options.database_type = 'mysql';
filterParams.Runs.loop_id = 209; % dsp_options.dataBase = 'labor';
% filterParams.Configurations = struct( ... % dsp_options.storage_path = 'Z:\2025\ECOC Silas\ecoc_2025\';
% 'symbolrate', 112e9, ... %[224,336,360,390,420,448] % database = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
% 'fiber_length', 0, ... % filterParams = database.tables;
% 'db_mode', '"no_db"', ... % filterParams.Runs.loop_id = 209;
% 'interference_attenuation', [], ... % % filterParams.Configurations = struct( ...
% 'interference_path_length', 1000, ... % % 'symbolrate', 112e9, ... %[224,336,360,390,420,448]
% 'is_mpi', 1, ... % % 'fiber_length', 0, ...
% 'pam_level', 4, ... % % 'db_mode', '"no_db"', ...
% 'wavelength', 1310, ... % % 'interference_attenuation', [], ...
% 'precomp_amp', [], ... % % 'interference_path_length', 1000, ...
% 'signal_attenuation', [], ... % % 'is_mpi', 1, ...
% 'v_awg', [], ... % % 'pam_level', 4, ...
% 'v_bias', [] ... % % 'wavelength', 1310, ...
% ); % % 'precomp_amp', [], ...
% % 'signal_attenuation', [], ...
% % 'v_awg', [], ...
% % 'v_bias', [] ...
% % );
%
% % if 1
% % % filterParams.EqualizerParameters.dc_buffer_len = 1;
% % filterParams.EqualizerParameters.ffe_buffer_len = 1;
% % filterParams.EqualizerParameters.smoothing_buffer_len = 4096;
% % filterParams.EqualizerParameters.smoothing_buffer_update = 224;
% % filterParams.EqualizerParameters.DCmu = 0;
% % end
% a = database.getTableFieldNames('Runs');
% b = database.getTableFieldNames('Results');
% c = database.getTableFieldNames('EqualizerParameters');
% d = [a;b;c];
%
% [dataTable,~] = database.queryDB(filterParams, d);
%
% selectedFields = {'Configurations.run_id' 'Runs.loop_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Runs.bitrate' 'Runs.v_bias' 'Runs.v_awg' 'Runs.precomp_amp' 'Runs.symbolrate' 'Runs.pam_level'...
% 'Runs.db_mode' 'Runs.rop_attenuation' 'Runs.is_mpi' 'Runs.interference_attenuation' 'Runs.interference_path_length' 'Runs.signal_attenuation' ...
% 'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'EqualizerParameters.dc_buffer_len' 'EqualizerParameters.ffe_buffer_len' 'EqualizerParameters.smoothing_buffer_len' 'EqualizerParameters.smoothing_buffer_update' 'EqualizerParameters.DCmu' 'Measurements.power_pd_in' ...
% 'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.EVM' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'};
% if 1 db = DBHandler("type","mysql","dataBase",'labor');
% % filterParams.EqualizerParameters.dc_buffer_len = 1;
% filterParams.EqualizerParameters.ffe_buffer_len = 1;
% filterParams.EqualizerParameters.smoothing_buffer_len = 4096;
% filterParams.EqualizerParameters.smoothing_buffer_update = 224;
% filterParams.EqualizerParameters.DCmu = 0;
% end
a = database.getTableFieldNames('Runs');
b = database.getTableFieldNames('Results');
c = database.getTableFieldNames('EqualizerParameters');
d = [a;b;c];
[dataTable,~] = database.queryDB(filterParams, d); fp = QueryFilter();
% fp.where('Runs', 'loop_id','EQUALS', 209);
selectedFields = {'Configurations.run_id' 'Runs.loop_id' 'Runs.date_of_run' 'Runs.rx_raw_path' 'Runs.bitrate' 'Runs.v_bias' 'Runs.v_awg' 'Runs.precomp_amp' 'Runs.symbolrate' 'Runs.pam_level'... % fp.where('Runs', 'sir','EQUALS', 21);
'Runs.db_mode' 'Runs.rop_attenuation' 'Runs.is_mpi' 'Runs.interference_attenuation' 'Runs.interference_path_length' 'Runs.signal_attenuation' ... fp.where('Runs', 'pam_level','EQUALS', 4);
'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'EqualizerParameters.dc_buffer_len' 'EqualizerParameters.ffe_buffer_len' 'EqualizerParameters.smoothing_buffer_len' 'EqualizerParameters.smoothing_buffer_update' 'EqualizerParameters.DCmu' 'Measurements.power_pd_in' ... fn = [db.getTableFieldNames('Runs');db.getTableFieldNames('Results')];
'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.BER_precoded' 'Results.EVM' 'Results.SNR' 'Results.GMI' 'Results.Alpha' 'Results.date_of_processing'}; [dataTable,sql_query] = db.queryDB(fp,fn);
[dataTable_raw,sql_query] = database.queryDB(filterParams, selectedFields);
%% %%
dataTable_clean = dataTable_raw; dataTable_clean = dataTable;
dataTable_clean.SIR = -7 - round(dataTable_clean.power_mpi_interference); dataTable_clean.SIR = -7 - round(dataTable_clean.power_mpi_interference);
dataTable_clean.NGMI = dataTable_clean.GMI ./ log2(dataTable_clean.pam_level); dataTable_clean.NGMI = dataTable_clean.GMI ./ log2(dataTable_clean.pam_level);
dataTable_clean = cleanUpTable(dataTable_clean); dataTable_clean = cleanUpTable(dataTable_clean);

19
projects/ECOC_2025/theory/analytic_mpi_evaluation.m
View File

@@ -1,13 +1,13 @@
% This script is used to evaluate Fig. 1b) in the paper "Adaptive Removal of Multipath Interference in Short Reach 112 GBd PAM-4 IM/DD Systems" % This script is used to evaluate Fig. 1b) in the paper "Adaptive Removal of Multipath Interference in Short Reach 112 GBd PAM-4 IM/DD Systems"
%% Parameters %% Parameters
df = 1e6;%150e3; % Laser linewidth [Hz] df = 1e6; % Laser linewidth [Hz]
SIR_dB = 20; % Interference attenuation [dB] SIR_dB = 20; % Interference attenuation [dB]
alpha = 10^(-SIR_dB/20); % Interference attenuation [linear] alpha = 10^(-SIR_dB/20); % Interference attenuation [linear]
n_fiber = 1.467; % Refractive index n_fiber = 1.467; % Refractive index
c = physconst('lightspeed'); % [m/s] c = physconst('lightspeed'); % [m/s]
L = linspace(0,400,40); % Interference delay [m] L = linspace(0,250,50); % Interference delay [m]
tau = n_fiber./c.*L; % Interference time (= tau) [s] tau = n_fiber./c.*L; % Interference time (= tau) [s]
tau_c = 1/(pi*df); % laser coherence time [s] tau_c = 1/(pi*df); % laser coherence time [s]
@@ -22,7 +22,7 @@ N = round(Tsim*fs); % number of samples for each realization
max_delay_samples = round(max(tau)*fs); % largest delay that is evaluated (based on max. Interference delay) max_delay_samples = round(max(tau)*fs); % largest delay that is evaluated (based on max. Interference delay)
phase_noise_std = sqrt(2*pi*df/fs); % standard dev. phase noise phase_noise_std = sqrt(2*pi*df/fs); % standard dev. phase noise
num_realizations = 10; % number of parallel runs num_realizations = 50; % number of parallel runs
monte_carlo_variance = zeros(num_realizations, length(L)); monte_carlo_variance = zeros(num_realizations, length(L));
parfor r = 1:num_realizations parfor r = 1:num_realizations
@@ -48,7 +48,9 @@ avg_of_mc_variances = mean(monte_carlo_variance, 1);
std_of_mc_variances = std(monte_carlo_variance, 0, 1); std_of_mc_variances = std(monte_carlo_variance, 0, 1);
%% Analytic variance %% Analytic variance
analytic_variance = 2*alpha^2 * (1 - exp(-2*pi*df.*tau)).^2; L_ = linspace(0,250,500); % Interference delay [m]
tau_ = n_fiber./c.*L_;
analytic_variance = 2*alpha^2 * (1 - exp(-2*pi*df.*tau_)).^2;
%% Plot %% Plot
cols = [0.3467 0.5360 0.6907 cols = [0.3467 0.5360 0.6907
@@ -62,20 +64,21 @@ hold on;
plot(L, avg_of_mc_variances, 'LineWidth',2, 'DisplayName','Simulation','Color',cols(1,:),'LineStyle','-'); plot(L, avg_of_mc_variances, 'LineWidth',2, 'DisplayName','Simulation','Color',cols(1,:),'LineStyle','-');
errorbar(L, avg_of_mc_variances,std_of_mc_variances, 'LineWidth',0.7,'LineStyle','none', 'DisplayName','Simulation','Color',cols(1,:),'HandleVisibility','off'); errorbar(L, avg_of_mc_variances,std_of_mc_variances, 'LineWidth',0.7,'LineStyle','none', 'DisplayName','Simulation','Color',cols(1,:),'HandleVisibility','off');
plot(L, analytic_variance, 'LineWidth',2, 'DisplayName','Analytic','Color',cols(2,:),'LineStyle','-'); plot(L_, analytic_variance, 'LineWidth',2, 'DisplayName','Analytic','Color',cols(2,:),'LineStyle','-');
xticks(coherence_length_multiples.*L_c); xticks(coherence_length_multiples.*L_c);
xticklabels(round(coherence_length_multiples.*L_c)); xticklabels(round(coherence_length_multiples.*L_c,1));
norm_to_coherence_len = 1; norm_to_coherence_len = 1;
if norm_to_coherence_len if norm_to_coherence_len
xticklabels(coherence_length_multiples); xticklabels(coherence_length_multiples);
xlabel('$\tau_c$', 'FontSize',12); xlabel('$n \cdot L_c$', 'FontSize',12);
else
xlabel('Interference Delay [m]', 'FontSize',12);
end end
xline(L_c.*coherence_length_multiples, 'LineWidth',1.5, 'DisplayName','Coh. Length','HandleVisibility','off','Color',[0.7,0.7,0.7],'LineStyle','-'); xline(L_c.*coherence_length_multiples, 'LineWidth',1.5, 'DisplayName','Coh. Length','HandleVisibility','off','Color',[0.7,0.7,0.7],'LineStyle','-');
xlim([0,L(end)]); xlim([0,L(end)]);
yline(var_sat, '-.k','LineWidth',1.5, 'DisplayName','Saturation: 2$\alpha ^2$'); yline(var_sat, '-.k','LineWidth',1.5, 'DisplayName','Saturation: 2$\alpha ^2$');
xlabel('Interference Delay [m]', 'FontSize',12);
grid on; grid on;
ylabel('Intensity Variance', 'FontSize',12); ylabel('Intensity Variance', 'FontSize',12);
title(sprintf('MPI Variance; %d MHz; SIR: %d dB',df.*1e-6,SIR_dB), 'FontSize',14); title(sprintf('MPI Variance; %d MHz; SIR: %d dB',df.*1e-6,SIR_dB), 'FontSize',14);

38
projects/ECOC_2025/theory/coherence_length_plot.m
View File

@@ -1,32 +1,32 @@
%% Parameters %% Parameters
df = linspace(100e3,50e6,10000); % Laser FWHM linewidth [Hz] df = linspace(1,50e6,10000); % Laser FWHM linewidth [Hz]
n_fiber = 1.467; % Fiber group index n_fiber = 1.467; % Fiber group index
c = 3e8; % Speed of light [m/s] c = 3e8; % Speed of light [m/s]
% Compute coherence length (1/e of mean-fringe decay) % Compute coherence length (1/e of mean-fringe decay)
tau_c = 1./(pi*df); tau_c = 1./(pi*df);
L_c = (c/n_fiber) .* tau_c; % Coherence length [m] L_c = (c.* tau_c/n_fiber) ; % Coherence length [m]
%% Plot %% Plot
figure('Color','w'); figure('Color','w');
loglog(df/1e6, L_c, 'LineWidth',2,'LineStyle','-'); % linewidth in MHz loglog(df/1e6, L_c, 'LineWidth',2,'LineStyle','-'); % linewidth in MHz
xticks([0.1, 1, 10, 50]); % xticks([0.1, 1, 10, 50]);
yticks([1, 10, 100, 1000]); % yticks([1, 10, 100, 1000]);
yticklabels({'1','10','100','1000'}) % yticklabels({'1','10','100','1000'})
grid on; box on; grid on; box on;
xlabel('Laser linewidth [MHz]','FontSize',12,'Interpreter','none'); xlabel('Laser linewidth [MHz]','FontSize',12,'Interpreter','latex');
ylabel('Coherence length [m]','FontSize',12,'Interpreter','none'); ylabel('Coherence length [m]','FontSize',12,'Interpreter','latex');
title('Coherence Length vs. Laser Linewidth','FontSize',14,'Interpreter','none'); title('Coherence Length vs. Laser Linewidth','FontSize',14,'Interpreter','latex');
%% Annotate some key points %% Annotate some key points
hold on; % hold on;
freqs = [150e3, 1e6, 10e6, 50e6]; % [Hz] % freqs = [150e3, 1e6, 10e6, 50e6]; % [Hz]
for f = freqs % for f = freqs
x = f/1e6; % x = f/1e6;
y = (c/n_fiber) * (1/(pi*f)); % y = (c/n_fiber) * (1/(pi*f));
scatter(x,y,'Marker','x','LineWidth',1,'MarkerEdgeColor','black'); % scatter(x,y,'Marker','x','LineWidth',1,'MarkerEdgeColor','black');
%
text(x*1.1,y, sprintf('%.2f MHz', f/1e6), ... % text(x*1.1,y, sprintf('%.2f MHz', f/1e6), ...
'FontSize',10,'HorizontalAlignment','left'); % 'FontSize',10,'HorizontalAlignment','left');
%
end % end

4
projects/HighSpeedExperiment_2024/Auswertung_JLT/ber_vs_rate.m
View File

@@ -5,7 +5,7 @@ db = DBHandler("dataBase", [dataBase], "type", database_type);
fp = QueryFilter(); fp = QueryFilter();
% fp.where('Runs', 'run_id','EQUALS', 987); % fp.where('Runs', 'run_id','EQUALS', 987);
M = 8; M = 4;
fp.where('Runs', 'pam_level','EQUALS', M); fp.where('Runs', 'pam_level','EQUALS', M);
% fp.where('Runs', 'bitrate','LESS_THAN', 310e9); % fp.where('Runs', 'bitrate','LESS_THAN', 310e9);
fp.where('Runs', 'fiber_length','EQUALS', 2); fp.where('Runs', 'fiber_length','EQUALS', 2);
@@ -13,7 +13,7 @@ fp.where('Runs', 'is_mpi','EQUALS', 0);
% fp.where('Runs', 'interference_path_length','EQUALS', 1000); % fp.where('Runs', 'interference_path_length','EQUALS', 1000);
% fp.where('Runs', 'loop_id','GREATER_THAN', 11); % fp.where('Runs', 'loop_id','GREATER_THAN', 11);
% fp.where('Runs', 'sir','EQUALS',18); % fp.where('Runs', 'sir','EQUALS',18);
fp.where('Runs', 'wavelength','EQUALS', 1293); fp.where('Runs', 'wavelength','EQUALS', 1310);
% fp.where('Runs', 'db_mode','EQUALS', 0); % fp.where('Runs', 'db_mode','EQUALS', 0);
fp.where('Runs', 'rop_attenuation','EQUALS', 0); fp.where('Runs', 'rop_attenuation','EQUALS', 0);

4
projects/IMDD_base_system/simulation_bwl.m
View File

@@ -1,6 +1,6 @@
%%% Run parameters %%% Run parameters
% TX % TX
M = 6; M = 4;
m = floor(log2(M)*10)/10; m = floor(log2(M)*10)/10;
fsym = 224e9; fsym = 224e9;
@@ -48,7 +48,7 @@ cols = linspecer(6);
rop = [-6]; rop = [-6];
bwl = [0.5:0.1:1.5]; bwl = [0.5:0.1:1.5];
fsym = [120:8:256].*1e9; fsym = [120:8:256].*1e9;
fsym =150e9; fsym =210e9;
ber_vnle = []; ber_vnle = [];
ber_mlse = []; ber_mlse = [];

251
projects/WDM/WDM_auswertung.m Normal file
View File

@@ -0,0 +1,251 @@
figure(); hold on;
cols = cbrewer2('set2',N); % one color per wavelength (Ch)
try
rop = res.settings.rop; % 12 points
wavelengthplan = res.settings.wavelengthplan;
catch
wavelengthplan = [1295,1305,1315,1325];
% wavelengthplan = calcWavelengthPlan(16,400e9,1310);
rop = -8.25:0.75:0;
end
N = length(wavelengthplan);
fec = 2.2e-4;
fec = 3.8e-3;
Sffe = cell(1,N);
Svnle = cell(1,N);
Smlse = cell(1,N);
Sdbt = cell(1,N);
% Choose your quantile band. For your old style, use 0.04/0.99:
qLow = 0.0; % lower quantile (e.g., 0.04 for old script)
qHigh = 1; % upper quantile (e.g., 0.99 for old script)
cols = linspecer(N); % one color per wavelength (Ch)
cols = cbrewer2('set1',N);
for l = 1:N
% Slice 12x50 cell arrays
ffe_cells = reshape(squeeze(res.ffe(l,:,:)),length(rop),[]);
vnle_cells = reshape(squeeze(res.vnle(l,:,:)),length(rop),[]);
mlse_cells = reshape(squeeze(res.mlse(l,:,:)),length(rop),[]);
dbt_cells = reshape(squeeze(res.dbt(l,:,:)),length(rop),[]);
[Sffe{l}, noX_ffe] = fecCrossings(rop, ffe_cells, fec);
[Svnle{l}, noX_ffe] = fecCrossings(rop, vnle_cells, fec);
[Smlse{l}, noX_ffe] = fecCrossings(rop, mlse_cells, fec);
[Sdbt{l}, noX_ffe] = fecCrossings(rop, dbt_cells, fec);
% Extract BER matrices using only complete realizations (12/12 ROP filled)
ffe_mat = extractCompleteBER(ffe_cells); % 12 x K_ffe
vnle_mat = extractCompleteBER(vnle_cells); % 12 x K_vnle
mlse_mat = extractCompleteBER(mlse_cells); % 12 x K_mlse
mlse_alpha_mat = extractCompleteAlphas(mlse_cells); % 12 x K_mlse
dbt_mat = extractCompleteBER(dbt_cells); % 12 x K_dbt
showLegend = 1; % one legend entry per technique
% Plot shaded band + mean line with boundedline
% plotBandMeanBL(rop, ffe_mat, cols(l,:), sprintf('FFE @ %.1d nm',round(wavelengthplan(l))), qLow, qHigh, '--s', showLegend);
% scatter(Sffe,fec.*ones(size(Sffe)),20,'v','MarkerFaceColor','black');
plotBandMeanBL(rop, vnle_mat, cols(l,:), sprintf('VNLE @ %.1d nm',round(wavelengthplan(l))), qLow, qHigh, '--x', showLegend);
% plotBandMeanBL(rop, mlse_mat, cols(l,:), sprintf('VNLE+PF+MLSE @ %.1d nm',round(wavelengthplan(l))), qLow, qHigh, '-o', showLegend);
% plotBandMeanBL(rop, dbt_mat, cols(l,:), sprintf('DBt.+MLSE @ %.1d nm',round(wavelengthplan(l))), qLow, qHigh, '--v', showLegend);
set(gca,'XScale','linear','YScale','log','TickLabelInterpreter','latex','FontSize',11);
yline([3.8e-3, 2.2e-4], 'HandleVisibility','off','LineWidth',1.5);
end
ylabel('BER');
xlabel('ROP');
title('BER vs. ROP');
xlim([min(rop) max(rop)]);
ylim([1e-5 0.3]);
grid on;
legend show;
S_cell = Sdbt;
S_cell =Smlse;
S_cell = {Svnle,Smlse,Sdbt};
S_cell = {Svnle};
figure(5); hold on;
for i = 1:length(S_cell)
% Pad to rectangular matrix: rows = realizations, cols = wavelengths
Kmax = max(cellfun(@numel, S_cell{i}));
S_mat = NaN(Kmax, N);
for l = 1:N
k = numel(S_cell{i}{l});
if k > 0
S_mat(1:k, l) = S_cell{i}{l};
end
end
% --- Violin plot over wavelengths (columns) ---
cols=linspecer(3);
catLabels = arrayfun(@(nm) sprintf('%d nm', nm), wavelengthplan, 'UniformOutput', false);
vs = violinplot(S_mat, catLabels, ...
'ViolinColor', cols(i,:), ...
'ViolinAlpha', 0.10, ...
'MarkerSize', 20, ...
'ShowMedian', true, ...
'EdgeColor', cols(i,:), ...
'ShowWhiskers', false, ...
'ShowData', true, ...
'ShowBox', false, ...
'Bandwidth', 0.05);
ylim([floor(min(S_mat,[],'all')), ceil(max(S_mat,[],'all'))])
ylim([-8 0]);
ylabel('ROP at FEC crossing');
title(sprintf('RROP to cross BER %.2e', fec));
grid on; box on;
end
%% ================= helper =================
function plotBandMeanBL(x, Y, color, techLabel, qLow, qHigh, lineSpec, showLegend)
% Y: (nPoints x nRealizations)
% Remove realizations that are entirely zero (like removeZeros behavior)
badCols = all(Y == 0, 1);
Y(:, badCols) = [];
Y(Y==0) = 1e-8;
% Stats across realizations
mu = mean(Y, 2, 'omitnan'); % mean line
lo = quantile(Y, qLow, 2); % lower bound
hi = quantile(Y, qHigh, 2); % upper bound
% Convert to asymmetric distances required by boundedline:
% b(:,1) = distance to lower side; b(:,2) = distance to upper side
b = [mu - lo, hi - mu];
% Call boundedline with alpha shading
[hl, hp] = boundedline(x(:), mu(:), b, lineSpec, 'alpha', ...
'transparency', 0.18);
% Color styling
set(hl, 'Color', color, 'LineWidth', 1.4, 'MarkerSize', 4);
set(hp, 'FaceColor', color, 'HandleVisibility','off'); % patch hidden in legend
% Single legend entry per technique (use first wavelength only)
if showLegend
set(hl, 'DisplayName', techLabel);
else
set(hl, 'HandleVisibility','off');
end
% Optional: outline the bounds if outlinebounds is available
if exist('outlinebounds','file') == 2
ho = outlinebounds(hl, hp);
set(ho, 'linestyle', ':', 'color', color, 'linewidth', 1, ...
'HandleVisibility','off');
end
end
function [S, noCrossingMask, Y_keep] = fecCrossings(rop, cells12xR, fec)
% cells12xR: 12xR cell array (one wavelength + scheme slice)
% each cell must be a struct with .metrics.BER
% rop: 12x1 numeric vector of ROP points
% fec: scalar FEC threshold (e.g., 3.8e-3)
%
% Outputs:
% S 1xK vector of crossing ROP per kept realization (NaN if none)
% noCrossingMask 1xK logical mask: true if no crossing for that realization
% Y_keep 12xK numeric BER matrix used for the crossing detection
% 1) keep only complete realization columns
Y = extractCompleteBER(cells12xR); % -> 12 x K
if isempty(Y)
S = [];
noCrossingMask = [];
Y_keep = Y;
return;
end
% 2) optionally drop realizations with mean BER > 0.1
ok = mean(Y,1,'omitnan') <= 0.1;
Y = Y(:, ok);
if isempty(Y)
S = [];
noCrossingMask = [];
Y_keep = Y;
return;
end
% 3) find crossings per realization
nR = size(Y,2);
S = nan(1,nR);
noCrossingMask = true(1,nR);
rop = rop(:); % ensure column
for j = 1:nR
y = Y(:,j);
% sign change from >fec to <=fec (first time it drops below FEC)
above = (y > fec);
idx = find(above(1:end-1) & ~above(2:end), 1, 'first');
if ~isempty(idx)
% linear interpolation between (x1,y1) and (x2,y2)
x1 = rop(idx); y1 = y(idx);
x2 = rop(idx+1); y2 = y(idx+1);
if isfinite(y1) && isfinite(y2) && y2 ~= y1
t = (fec - y1) / (y2 - y1);
S(j) = x1 + t*(x2 - x1);
noCrossingMask(j) = false;
end
end
end
Y_keep = Y;
end
function Y = extractCompleteBER(cellSlice)
% cellSlice: 12xR cell array; each cell should be a struct with .metrics.BER
% Keep only those realization columns where ALL 12 ROP entries are valid.
if isempty(cellSlice), Y = []; return; end
nR = size(cellSlice,2);
keep = false(1,nR);
for r = 1:nR
col = cellSlice(:,r);
keep(r) = all(cellfun(@(c) ~isempty(c) , col));
end
if ~any(keep), Y = []; return; end
Y = cellfun(@(c) c.metrics.BER, cellSlice(:,keep), 'UniformOutput', true);
end
function Y = extractCompleteAlphas(cellSlice)
% cellSlice: 12xR cell array; each cell should be a struct with .metrics.BER
% Keep only those realization columns where ALL 12 ROP entries are valid.
if isempty(cellSlice), Y = []; return; end
nR = size(cellSlice,2);
keep = false(1,nR);
for r = 1:nR
col = cellSlice(:,r);
keep(r) = all(cellfun(@(c) ~isempty(c) , col));
end
if ~any(keep), Y = []; return; end
Y = cellfun(@(c) c.metrics.Alpha, cellSlice(:,keep), 'UniformOutput', true);
end

122
projects/WDM/WDM_model.m
View File

@@ -2,23 +2,22 @@
% TX % TX
% --- FIRST LINE: evaluate settings located beside this script --- % --- FIRST LINE: evaluate settings located beside this script ---
run(fullfile(fileparts(mfilename('fullpath')),'WDM_settings.m')); run(fullfile(fileparts(mfilename('fullpath')),'WDM_settings.m'));
s = struct;
s.num_realiz = 1;
% s.wavelengthplan = calcWavelengthPlan(16,400e9,1310);
s.wavelengthplan = [1295,1305,1315,1325];
s.link_length = 2;
s.pmd = 0.0;
s.gamma = 0.00;
num_realiz = 50; s.M = 4;
% wavelengthplan = calcWavelengthPlan(16,400e9,1310); m = floor(log2(s.M)*10)/10;
wavelengthplan = [1295,1305,1315,1325];
link_length = 2;
pmd = 0.1;
gamma = 0.0023;
M = 4;
m = floor(log2(M)*10)/10;
fsym = 224e9; fsym = 224e9;
fdac = 2*fsym; fdac = 2*fsym;
fadc = 2*fsym; fadc = 2*fsym;
random_key = 2; s.random_key = 100;
% Laser / Modulator % Laser / s.Modulator
vbias_rel = 0.5; vbias_rel = 0.5;
u_pi = 3.2; u_pi = 3.2;
vbias = -vbias_rel*u_pi; vbias = -vbias_rel*u_pi;
@@ -48,8 +47,8 @@ apply_pulsef = 0;
rcalpha = 0.05; rcalpha = 0.05;
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha); Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
N = numel(wavelengthplan); N = numel(s.wavelengthplan);
f_plan = physconst('lightspeed')./(wavelengthplan.*1e-9); f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9);
margin = 25e12; % some THz left and right margin = 25e12; % some THz left and right
f_span = (max(f_plan)+margin)-(min(f_plan)-margin); f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
f_nyq = f_span/2; f_nyq = f_span/2;
@@ -58,38 +57,38 @@ upsample_required = f_nyq./(fdac*kover/2);
upsample_pow = 2^nextpow2(upsample_required); upsample_pow = 2^nextpow2(upsample_required);
upsample_ceil = ceil(upsample_required); upsample_ceil = ceil(upsample_required);
f_opt = fdac*kover*upsample_pow; s.f_opt = fdac*kover*upsample_pow;
f_opt_nyq = f_opt/2; s.f_opt_nyq = s.f_opt/2;
signal_cell = {}; signal_cell = {};
Symbols = {}; Symbols = {};
Tx_bits = {}; Tx_bits = {};
rop = -8.25:0.75:0; s.rop = -6:0.75:-0.75;
output_ffe = cell(length(wavelengthplan),length(rop),num_realiz); output_ffe = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
output_vnle = cell(length(wavelengthplan),length(rop),num_realiz); output_vnle = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
output_mlse = cell(length(wavelengthplan),length(rop),num_realiz); output_mlse = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
output_dbt = cell(length(wavelengthplan),length(rop),num_realiz); output_dbt = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
for realiz = 1:num_realiz for realiz = 1:s.num_realiz
parfor l = 1:N parfor l = 1:N
[Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource(... [Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource(...
"fsym",fsym,"M",M,"order",18,"useprbs",0,... "fsym",fsym,"M",s.M,"order",18,"useprbs",0,...
"fs_out",fdac,... "fs_out",fdac,...
"applyclipping",0,"clipfactor",1.5,... "applyclipping",0,"clipfactor",1.5,...
"applypulseform",apply_pulsef,"pulseformer",Pform,... "applypulseform",apply_pulsef,"pulseformer",Pform,...
"randkey",random_key+l+realiz,... "randkey",s.random_key+l+realiz,...
"db_precode",db_precode,"db_encode",db_encode,... "db_precode",db_precode,"db_encode",db_encode,...
"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode).process(); "mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode).process();
% Digi_sig.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0); % Digi_sig.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
Lp_awg = Filter('filtdegree',3,"f_cutoff",100e9,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true); Lp_awg = Filter('filtdegree',3,"f_cutoff",100e9,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true);
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",0,"H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig); El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",0,"H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig);
% El_sig = M8199B("kover",kover).process(Digi_sig); % El_sig = s.M8199B("kover",kover).process(Digi_sig);
% El_sig.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0); % El_sig.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
%%%%% Electrical Driver Amplifier %%%%%% %%%%% Electrical Driver Amplifier %%%%%%
@@ -97,10 +96,10 @@ for realiz = 1:num_realiz
% El_sig = El_sig.setPower(1,"dBm"); % El_sig = El_sig.setPower(1,"dBm");
% figure;histogram(El_sig.signal); % figure;histogram(El_sig.signal);
%%%%% MODULATE E/O CONVERSION %%%%% %%%%% s.MODULATE E/O CONVERSION %%%%%
Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",wavelengthplan(l),"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+l+realiz).process(El_sig); Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",s.wavelengthplan(l),"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",s.random_key+l+realiz).process(El_sig);
signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",30).process(Eml_out); signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",100).process(Eml_out);
end end
Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover,... Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover,...
@@ -119,12 +118,12 @@ for realiz = 1:num_realiz
zdw = 1310; zdw = 1310;
D_local = 0; %if ~=0, simulation uses "segmented fiber with d+,d-) D_local = 0; %if ~=0, simulation uses "segmented fiber with d+,d-)
randomize_D = true; randomize_D = true;
Dvec = getDispersionVector(nSegments, D_local, zdw, randomize_D, random_key+realiz); Dvec = getDispersionVector(nSegments, D_local, zdw, randomize_D, s.random_key+realiz);
for s = 1:nSegments for seg = 1:nSegments
Opt_sig_wdm_fib = DP_Fiber("L",link_length/nSegments,"D",Dvec(s),"Dpmd",pmd,"Ds",0.07,... Opt_sig_wdm_fib = DP_Fiber("L",s.link_length/nSegments,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07,...
"beat_len",10,"corr_len",100,"dz",1,"manakov",0,... "beat_len",10,"corr_len",100,"dz",1,"manakov",0,...
"gamma",gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01,... "gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01,...
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1).process(Opt_sig_wdm_fib); "SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1).process(Opt_sig_wdm_fib);
end end
@@ -133,23 +132,23 @@ for realiz = 1:num_realiz
% Opt_sig_wdm_fib.move_it_spectrum("fignum",100212,"displayname",'bla'); % Opt_sig_wdm_fib.move_it_spectrum("fignum",100212,"displayname",'bla');
% 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) % Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",s.link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"s.gamma",0,"Dslope",0.07).process(Opt_sig)
parfor ri = 1:length(rop) for ri = 1:length(s.rop)
%%%%%% ROP %%%%%% %%%%%% ROP %%%%%%
Opt_sig_wdm_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop(ri)+10*log10(N)).process(Opt_sig_wdm_fib); Opt_sig_wdm_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",s.rop(ri)+10*log10(N)).process(Opt_sig_wdm_fib);
Opt_sig_wdm_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1,"fs_out",Opt_sig_wdm_rx.fs/upsample_pow,"fs_in",Opt_sig_wdm_rx.fs,"lambda_center",1310).process(Opt_sig_wdm_rx); Opt_sig_wdm_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1,"fs_out",Opt_sig_wdm_rx.fs/upsample_pow,"fs_in",Opt_sig_wdm_rx.fs,"lambda_center",1310).process(Opt_sig_wdm_rx);
PD_cell = {}; PD_cell = {};
for l = 1:N parfor l = 1:N
%%%%%% PD Square Law %%%%%% %%%%%% PD Square Law %%%%%%
assert(fdac*kover==Opt_sig_wdm_demux{l}.fs,'Sampling Frequencies do not match! Check previous steps'); assert(fdac*kover==Opt_sig_wdm_demux{l}.fs,'Sampling Frequencies do not match! Check previous steps');
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",random_key+l+realiz).process(Opt_sig_wdm_demux{l}); PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",s.random_key+l+realiz).process(Opt_sig_wdm_demux{l});
PD_sig.spectrum("fignum",222,"displayname",'bla','normalizeTo0dB',1); % PD_sig.spectrum("fignum",222,"displayname",'bla','normalizeTo0dB',1);
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%% %%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
rx_bwl = 100e9; rx_bwl = 100e9;
@@ -176,7 +175,7 @@ for realiz = 1:num_realiz
% FFE % FFE
ffe_order = [50, 0, 0]; ffe_order = [50, 0, 0];
eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0); eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
ffe_results = ffe(eq_ffe,M,Rx_sig,Symbols{l},Tx_bits{l},... ffe_results = ffe(eq_ffe,s.M,Rx_sig,Symbols{l},Tx_bits{l},...
"precode_mode",duob_mode,... "precode_mode",duob_mode,...
'showAnalysis',0,... 'showAnalysis',0,...
"postFFE",[],... "postFFE",[],...
@@ -194,12 +193,12 @@ for realiz = 1:num_realiz
useviterbi = 0; useviterbi = 0;
if useviterbi if useviterbi
mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels); mlse_ = MLSE_viterbi("duobinary_output",0,'M',s.M,'trellis_states',PAMmapper(s.M,0).levels);
else else
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels); mlse_ = MLSE("duobinary_output",0,'M',s.M,'trellis_states',PAMmapper(s.M,0).levels);
end end
[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig, Symbols{l},Tx_bits{l}, ... [vnle_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, s.M, Rx_sig, Symbols{l},Tx_bits{l}, ...
"precode_mode", duob_mode,... "precode_mode", duob_mode,...
'showAnalysis', 0, ... 'showAnalysis', 0, ...
"postFFE", [],... "postFFE", [],...
@@ -209,18 +208,17 @@ for realiz = 1:num_realiz
output_mlse{l,ri,realiz} = mlse_results; output_mlse{l,ri,realiz} = mlse_results;
% DB tgt. % DB tgt.
useviterbi = 0; useviterbi = 0;
if useviterbi if useviterbi
mlse_db_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels); mlse_db_ = MLSE_viterbi("duobinary_output",0,'M',s.M,'trellis_states',PAMmapper(s.M,0).levels);
else else
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels); mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",s.M,"trellis_states",PAMmapper(s.M,0).levels);
end end
ffe_order = [50, 5, 5]; ffe_order = [50, 5, 5];
eq_ = 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_ = 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);
dbt_results = duobinary_target(eq_, mlse_db_, M, Rx_sig, Symbols{l},Tx_bits{l}, ... dbt_results = duobinary_target(eq_, mlse_db_, s.M, Rx_sig, Symbols{l},Tx_bits{l}, ...
"precode_mode", duob_mode, ... "precode_mode", duob_mode, ...
'showAnalysis', 0,... 'showAnalysis', 0,...
"postFFE", []); "postFFE", []);
@@ -232,6 +230,7 @@ for realiz = 1:num_realiz
end end
res = struct(); res = struct();
res.settings = s;
res.ffe = output_ffe; res.ffe = output_ffe;
res.vnle = output_vnle; res.vnle = output_vnle;
res.mlse = output_mlse; res.mlse = output_mlse;
@@ -240,38 +239,13 @@ for realiz = 1:num_realiz
% Save results % Save results
save(fullfile(output_root, fname), 'res', '-v7.3'); save(fullfile(output_root, fname), 'res', '-v7.3');
fprintf('Saved results to: %s\n', fullfile(output_root, fname)); fprintf('Saved results to: %s\n', fullfile(output_root, fname));
disp(datetime('now','TimeZone','local','Format','yyyyMs.Mdd_HHmmss'));
end end
figure();hold on;
cols = linspecer(N);
for l = 1:N
% plot(rop,mean(squeeze(ber_vnle(l,:,:)),2,'omitnan'),'Marker','*','DisplayName',sprintf('Ch: %d',wavelengthplan(l)))
% plot(rop,cellfun(@(c) c.metrics.BER, output_ffe(l,:), 'UniformOutput', true),'Marker','*','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle',':');
plot(rop,cellfun(@(c) c.metrics.BER, res.vnle(l,:), 'UniformOutput', true),'Marker','x','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle','--')
plot(rop,cellfun(@(c) c.metrics.BER, res.mlse(l,:), 'UniformOutput', true),'Marker','o','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle','-')
end
yline([3.8e-3,2.2e-4],'HandleVisibility','off');
ylabel('BER');
xlabel('ROP')
title('BER vs. ROP');
set(gca, 'XScale', 'linear', ...
'YScale', 'log', ...
'TickLabelInterpreter', 'latex', ...
'FontSize', 11);
xlim([min(rop) max(rop)])
ylim([1e-5 0.3])
% --- save as PNG ---
outname = fullfile(output_root, 'BER_vs_ROP.png'); % saves to current folder
print(gcf, outname, '-dpng', '-r300'); % 300 dpi
fprintf('Saved figure to %s\n', outname);
function dispersion_vector = getDispersionVector(N, D, ref_zdw, randomize_ZDW, randomkey) function dispersion_vector = getDispersionVector(N, D, ref_zdw, randomize_ZDW, randomkey)
% MATLAB version of the Python generator shown above. % s.MATLAB version of the Python generator shown above.
% Returns an N×1 vector (ps/(nm·km)). % Returns an N×1 vector (ps/(nm·km)).
% %
% D is the nominal dispersion magnitude. For D>0 the link is segmented with % D is the nominal dispersion magnitude. For D>0 the link is segmented with