ECOC Theroy stuff
This commit is contained in:
Silas Oettinghaus
@@ -1,34 +1,20 @@
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db = DBHandler("type","mysql","dataBase",'labor');
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fp = QueryFilter();
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% fp.where('Runs', 'run_id','EQUALS', 987);
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M = 4;
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fp.where('Runs', 'pam_level','EQUALS', M);
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fp.where('Runs', 'symbolrate','EQUALS', 112e9);
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fp.where('Runs', 'fiber_length','EQUALS', 0);
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fp.where('Runs', 'is_mpi','EQUALS', 1);
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fp.where('Runs', 'interference_path_length','EQUALS', 70);
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% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
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fp.where('Runs', 'sir','EQUALS',20);
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savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
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databasePath = 'C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
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database_name = 'ecoc2025_loops.db';
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db = DBHandler("type","mysql");
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% db = DBHandler("pathToDB", [databasePath, database_name],"type","sqlite");
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filterParams = db.tables;
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% filterParams.Configurations = struct('run_id', run_id);
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filterParams.Configurations = struct( ...
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'symbolrate', 112e9, ... %[224,336,360,390,420,448]
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'fiber_length', 0, ...
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'db_mode', '"no_db"', ...
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'interference_attenuation', 4, ...
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'interference_path_length', 300, ...
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'is_mpi', 1, ...
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'pam_level', 4, ...
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'wavelength', 1310, ...
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'precomp_amp', [], ...
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'signal_attenuation', [], ...
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'v_awg', [], ...
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'v_bias', [] ...
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);
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selectedFields = {'Runs.run_id','Runs.tx_bits_path','Runs.tx_signal_path','Runs.tx_symbols_path','Runs.rx_sync_path','Runs.rx_raw_path',...
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'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
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'Configurations.interference_attenuation', 'Measurements.power_mpi_interference'};
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[dataTable,sql_query] = db.queryDB(filterParams, selectedFields);
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[dataTable,sql_query] = db.queryDB(fp, db.getTableFieldNames('Runs'));
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[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
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@@ -55,7 +41,7 @@ for i = 1:size(dataTable,1)
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Scpe_sig_raw = load([savePath, char(dataTable_.rx_raw_path(1))]);
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Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
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% Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
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Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
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Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",Scpe_sig_raw.fs,"fs_out",fsym);
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@@ -1,49 +1,57 @@
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% basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
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% database = DBHandler("pathToDB",[basePath,'silas_labor.db']);
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database = DBHandler("type",'mysql','dataBase','labor');
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filterParams = database.tables;
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filterParams.Runs.loop_id = 209;
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% filterParams.Configurations = struct( ...
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% 'symbolrate', 112e9, ... %[224,336,360,390,420,448]
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% 'fiber_length', 0, ...
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% 'db_mode', '"no_db"', ...
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% 'interference_attenuation', [], ...
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% 'interference_path_length', 1000, ...
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% 'is_mpi', 1, ...
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% 'pam_level', 4, ...
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% 'wavelength', 1310, ...
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% 'precomp_amp', [], ...
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% 'signal_attenuation', [], ...
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% 'v_awg', [], ...
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% 'v_bias', [] ...
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% );
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% dsp_options.database_type = 'mysql';
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% dsp_options.dataBase = 'labor';
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% dsp_options.storage_path = 'Z:\2025\ECOC Silas\ecoc_2025\';
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% database = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
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% filterParams = database.tables;
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% filterParams.Runs.loop_id = 209;
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% % filterParams.Configurations = struct( ...
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% % 'symbolrate', 112e9, ... %[224,336,360,390,420,448]
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% % 'fiber_length', 0, ...
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% % 'db_mode', '"no_db"', ...
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% % 'interference_attenuation', [], ...
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% % 'interference_path_length', 1000, ...
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% % 'is_mpi', 1, ...
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% % 'pam_level', 4, ...
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% % 'wavelength', 1310, ...
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% % 'precomp_amp', [], ...
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% % 'signal_attenuation', [], ...
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% % 'v_awg', [], ...
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% % 'v_bias', [] ...
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% % );
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%
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% % if 1
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% % % filterParams.EqualizerParameters.dc_buffer_len = 1;
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% % filterParams.EqualizerParameters.ffe_buffer_len = 1;
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% % filterParams.EqualizerParameters.smoothing_buffer_len = 4096;
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% % filterParams.EqualizerParameters.smoothing_buffer_update = 224;
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% % filterParams.EqualizerParameters.DCmu = 0;
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% % end
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% a = database.getTableFieldNames('Runs');
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% b = database.getTableFieldNames('Results');
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% c = database.getTableFieldNames('EqualizerParameters');
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% d = [a;b;c];
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%
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% [dataTable,~] = database.queryDB(filterParams, d);
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%
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% 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'...
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% 'Runs.db_mode' 'Runs.rop_attenuation' 'Runs.is_mpi' 'Runs.interference_attenuation' 'Runs.interference_path_length' 'Runs.signal_attenuation' ...
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% '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' ...
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% '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'};
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% if 1
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% % filterParams.EqualizerParameters.dc_buffer_len = 1;
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% filterParams.EqualizerParameters.ffe_buffer_len = 1;
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% filterParams.EqualizerParameters.smoothing_buffer_len = 4096;
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% filterParams.EqualizerParameters.smoothing_buffer_update = 224;
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% filterParams.EqualizerParameters.DCmu = 0;
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% end
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a = database.getTableFieldNames('Runs');
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b = database.getTableFieldNames('Results');
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c = database.getTableFieldNames('EqualizerParameters');
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d = [a;b;c];
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db = DBHandler("type","mysql","dataBase",'labor');
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[dataTable,~] = database.queryDB(filterParams, d);
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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'...
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'Runs.db_mode' 'Runs.rop_attenuation' 'Runs.is_mpi' 'Runs.interference_attenuation' 'Runs.interference_path_length' 'Runs.signal_attenuation' ...
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'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' ...
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'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'};
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[dataTable_raw,sql_query] = database.queryDB(filterParams, selectedFields);
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fp = QueryFilter();
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% fp.where('Runs', 'loop_id','EQUALS', 209);
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% fp.where('Runs', 'sir','EQUALS', 21);
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fp.where('Runs', 'pam_level','EQUALS', 4);
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fn = [db.getTableFieldNames('Runs');db.getTableFieldNames('Results')];
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[dataTable,sql_query] = db.queryDB(fp,fn);
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%%
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dataTable_clean = dataTable_raw;
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dataTable_clean = dataTable;
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dataTable_clean.SIR = -7 - round(dataTable_clean.power_mpi_interference);
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dataTable_clean.NGMI = dataTable_clean.GMI ./ log2(dataTable_clean.pam_level);
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dataTable_clean = cleanUpTable(dataTable_clean);
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@@ -1,13 +1,13 @@
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% 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"
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%% Parameters
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df = 1e6;%150e3; % Laser linewidth [Hz]
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df = 1e6; % Laser linewidth [Hz]
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SIR_dB = 20; % Interference attenuation [dB]
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alpha = 10^(-SIR_dB/20); % Interference attenuation [linear]
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n_fiber = 1.467; % Refractive index
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c = physconst('lightspeed'); % [m/s]
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L = linspace(0,400,40); % Interference delay [m]
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L = linspace(0,250,50); % Interference delay [m]
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tau = n_fiber./c.*L; % Interference time (= tau) [s]
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tau_c = 1/(pi*df); % laser coherence time [s]
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@@ -22,7 +22,7 @@ N = round(Tsim*fs); % number of samples for each realization
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max_delay_samples = round(max(tau)*fs); % largest delay that is evaluated (based on max. Interference delay)
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phase_noise_std = sqrt(2*pi*df/fs); % standard dev. phase noise
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num_realizations = 10; % number of parallel runs
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num_realizations = 50; % number of parallel runs
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monte_carlo_variance = zeros(num_realizations, length(L));
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parfor r = 1:num_realizations
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@@ -48,7 +48,9 @@ avg_of_mc_variances = mean(monte_carlo_variance, 1);
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std_of_mc_variances = std(monte_carlo_variance, 0, 1);
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%% Analytic variance
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analytic_variance = 2*alpha^2 * (1 - exp(-2*pi*df.*tau)).^2;
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L_ = linspace(0,250,500); % Interference delay [m]
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tau_ = n_fiber./c.*L_;
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analytic_variance = 2*alpha^2 * (1 - exp(-2*pi*df.*tau_)).^2;
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%% Plot
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cols = [0.3467 0.5360 0.6907
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@@ -62,20 +64,21 @@ hold on;
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plot(L, avg_of_mc_variances, 'LineWidth',2, 'DisplayName','Simulation','Color',cols(1,:),'LineStyle','-');
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errorbar(L, avg_of_mc_variances,std_of_mc_variances, 'LineWidth',0.7,'LineStyle','none', 'DisplayName','Simulation','Color',cols(1,:),'HandleVisibility','off');
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plot(L, analytic_variance, 'LineWidth',2, 'DisplayName','Analytic','Color',cols(2,:),'LineStyle','-');
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plot(L_, analytic_variance, 'LineWidth',2, 'DisplayName','Analytic','Color',cols(2,:),'LineStyle','-');
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xticks(coherence_length_multiples.*L_c);
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xticklabels(round(coherence_length_multiples.*L_c));
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xticklabels(round(coherence_length_multiples.*L_c,1));
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norm_to_coherence_len = 1;
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if norm_to_coherence_len
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xticklabels(coherence_length_multiples);
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xlabel('$\tau_c$', 'FontSize',12);
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xlabel('$n \cdot L_c$', 'FontSize',12);
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else
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xlabel('Interference Delay [m]', 'FontSize',12);
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end
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xline(L_c.*coherence_length_multiples, 'LineWidth',1.5, 'DisplayName','Coh. Length','HandleVisibility','off','Color',[0.7,0.7,0.7],'LineStyle','-');
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xlim([0,L(end)]);
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yline(var_sat, '-.k','LineWidth',1.5, 'DisplayName','Saturation: 2$\alpha ^2$');
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xlabel('Interference Delay [m]', 'FontSize',12);
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grid on;
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ylabel('Intensity Variance', 'FontSize',12);
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title(sprintf('MPI Variance; %d MHz; SIR: %d dB',df.*1e-6,SIR_dB), 'FontSize',14);
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@@ -1,32 +1,32 @@
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%% Parameters
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df = linspace(100e3,50e6,10000); % Laser FWHM linewidth [Hz]
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df = linspace(1,50e6,10000); % Laser FWHM linewidth [Hz]
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n_fiber = 1.467; % Fiber group index
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c = 3e8; % Speed of light [m/s]
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% Compute coherence length (1/e of mean-fringe decay)
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tau_c = 1./(pi*df);
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L_c = (c/n_fiber) .* tau_c; % Coherence length [m]
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L_c = (c.* tau_c/n_fiber) ; % Coherence length [m]
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%% Plot
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figure('Color','w');
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loglog(df/1e6, L_c, 'LineWidth',2,'LineStyle','-'); % linewidth in MHz
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xticks([0.1, 1, 10, 50]);
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yticks([1, 10, 100, 1000]);
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yticklabels({'1','10','100','1000'})
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% xticks([0.1, 1, 10, 50]);
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% yticks([1, 10, 100, 1000]);
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% yticklabels({'1','10','100','1000'})
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grid on; box on;
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xlabel('Laser linewidth [MHz]','FontSize',12,'Interpreter','none');
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ylabel('Coherence length [m]','FontSize',12,'Interpreter','none');
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title('Coherence Length vs. Laser Linewidth','FontSize',14,'Interpreter','none');
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xlabel('Laser linewidth [MHz]','FontSize',12,'Interpreter','latex');
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ylabel('Coherence length [m]','FontSize',12,'Interpreter','latex');
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title('Coherence Length vs. Laser Linewidth','FontSize',14,'Interpreter','latex');
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%% Annotate some key points
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hold on;
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freqs = [150e3, 1e6, 10e6, 50e6]; % [Hz]
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for f = freqs
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x = f/1e6;
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y = (c/n_fiber) * (1/(pi*f));
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scatter(x,y,'Marker','x','LineWidth',1,'MarkerEdgeColor','black');
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text(x*1.1,y, sprintf('%.2f MHz', f/1e6), ...
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'FontSize',10,'HorizontalAlignment','left');
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end
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% hold on;
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% freqs = [150e3, 1e6, 10e6, 50e6]; % [Hz]
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% for f = freqs
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% x = f/1e6;
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% y = (c/n_fiber) * (1/(pi*f));
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% scatter(x,y,'Marker','x','LineWidth',1,'MarkerEdgeColor','black');
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%
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% text(x*1.1,y, sprintf('%.2f MHz', f/1e6), ...
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% 'FontSize',10,'HorizontalAlignment','left');
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%
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% end
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@@ -5,7 +5,7 @@ db = DBHandler("dataBase", [dataBase], "type", database_type);
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fp = QueryFilter();
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% fp.where('Runs', 'run_id','EQUALS', 987);
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M = 8;
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M = 4;
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fp.where('Runs', 'pam_level','EQUALS', M);
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% fp.where('Runs', 'bitrate','LESS_THAN', 310e9);
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fp.where('Runs', 'fiber_length','EQUALS', 2);
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@@ -13,7 +13,7 @@ fp.where('Runs', 'is_mpi','EQUALS', 0);
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% fp.where('Runs', 'interference_path_length','EQUALS', 1000);
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% fp.where('Runs', 'loop_id','GREATER_THAN', 11);
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% fp.where('Runs', 'sir','EQUALS',18);
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fp.where('Runs', 'wavelength','EQUALS', 1293);
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fp.where('Runs', 'wavelength','EQUALS', 1310);
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% fp.where('Runs', 'db_mode','EQUALS', 0);
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fp.where('Runs', 'rop_attenuation','EQUALS', 0);
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@@ -1,6 +1,6 @@
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%%% Run parameters
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% TX
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M = 6;
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M = 4;
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m = floor(log2(M)*10)/10;
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fsym = 224e9;
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@@ -48,7 +48,7 @@ cols = linspecer(6);
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rop = [-6];
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bwl = [0.5:0.1:1.5];
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fsym = [120:8:256].*1e9;
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fsym =150e9;
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fsym =210e9;
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ber_vnle = [];
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ber_mlse = [];
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251
projects/WDM/WDM_auswertung.m
Normal file
251
projects/WDM/WDM_auswertung.m
Normal file
@@ -0,0 +1,251 @@
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figure(); hold on;
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cols = cbrewer2('set2',N); % one color per wavelength (Ch)
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try
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rop = res.settings.rop; % 12 points
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wavelengthplan = res.settings.wavelengthplan;
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catch
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wavelengthplan = [1295,1305,1315,1325];
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% wavelengthplan = calcWavelengthPlan(16,400e9,1310);
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rop = -8.25:0.75:0;
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end
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N = length(wavelengthplan);
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fec = 2.2e-4;
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fec = 3.8e-3;
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Sffe = cell(1,N);
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Svnle = cell(1,N);
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Smlse = cell(1,N);
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Sdbt = cell(1,N);
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% Choose your quantile band. For your old style, use 0.04/0.99:
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qLow = 0.0; % lower quantile (e.g., 0.04 for old script)
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qHigh = 1; % upper quantile (e.g., 0.99 for old script)
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cols = linspecer(N); % one color per wavelength (Ch)
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cols = cbrewer2('set1',N);
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for l = 1:N
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% Slice 12x50 cell arrays
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ffe_cells = reshape(squeeze(res.ffe(l,:,:)),length(rop),[]);
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vnle_cells = reshape(squeeze(res.vnle(l,:,:)),length(rop),[]);
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mlse_cells = reshape(squeeze(res.mlse(l,:,:)),length(rop),[]);
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dbt_cells = reshape(squeeze(res.dbt(l,:,:)),length(rop),[]);
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[Sffe{l}, noX_ffe] = fecCrossings(rop, ffe_cells, fec);
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[Svnle{l}, noX_ffe] = fecCrossings(rop, vnle_cells, fec);
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[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
|
||||
|
||||
@@ -2,23 +2,22 @@
|
||||
% TX
|
||||
% --- FIRST LINE: evaluate settings located beside this script ---
|
||||
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;
|
||||
% wavelengthplan = calcWavelengthPlan(16,400e9,1310);
|
||||
wavelengthplan = [1295,1305,1315,1325];
|
||||
link_length = 2;
|
||||
pmd = 0.1;
|
||||
gamma = 0.0023;
|
||||
|
||||
|
||||
M = 4;
|
||||
m = floor(log2(M)*10)/10;
|
||||
s.M = 4;
|
||||
m = floor(log2(s.M)*10)/10;
|
||||
fsym = 224e9;
|
||||
fdac = 2*fsym;
|
||||
fadc = 2*fsym;
|
||||
random_key = 2;
|
||||
s.random_key = 100;
|
||||
|
||||
% Laser / Modulator
|
||||
% Laser / s.Modulator
|
||||
vbias_rel = 0.5;
|
||||
u_pi = 3.2;
|
||||
vbias = -vbias_rel*u_pi;
|
||||
@@ -48,8 +47,8 @@ apply_pulsef = 0;
|
||||
rcalpha = 0.05;
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
N = numel(wavelengthplan);
|
||||
f_plan = physconst('lightspeed')./(wavelengthplan.*1e-9);
|
||||
N = numel(s.wavelengthplan);
|
||||
f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9);
|
||||
margin = 25e12; % some THz left and right
|
||||
f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
|
||||
f_nyq = f_span/2;
|
||||
@@ -58,38 +57,38 @@ upsample_required = f_nyq./(fdac*kover/2);
|
||||
upsample_pow = 2^nextpow2(upsample_required);
|
||||
upsample_ceil = ceil(upsample_required);
|
||||
|
||||
f_opt = fdac*kover*upsample_pow;
|
||||
f_opt_nyq = f_opt/2;
|
||||
s.f_opt = fdac*kover*upsample_pow;
|
||||
s.f_opt_nyq = s.f_opt/2;
|
||||
|
||||
signal_cell = {};
|
||||
Symbols = {};
|
||||
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_vnle = cell(length(wavelengthplan),length(rop),num_realiz);
|
||||
output_mlse = cell(length(wavelengthplan),length(rop),num_realiz);
|
||||
output_dbt = cell(length(wavelengthplan),length(rop),num_realiz);
|
||||
output_ffe = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
|
||||
output_vnle = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
|
||||
output_mlse = cell(length(s.wavelengthplan),length(s.rop),s.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
|
||||
|
||||
[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,...
|
||||
"applyclipping",0,"clipfactor",1.5,...
|
||||
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||
"randkey",random_key+l+realiz,...
|
||||
"randkey",s.random_key+l+realiz,...
|
||||
"db_precode",db_precode,"db_encode",db_encode,...
|
||||
"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);
|
||||
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 = 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);
|
||||
|
||||
%%%%% Electrical Driver Amplifier %%%%%%
|
||||
@@ -97,10 +96,10 @@ for realiz = 1:num_realiz
|
||||
% El_sig = El_sig.setPower(1,"dBm");
|
||||
% figure;histogram(El_sig.signal);
|
||||
|
||||
%%%%% 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);
|
||||
%%%%% s.MODULATE E/O CONVERSION %%%%%
|
||||
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
|
||||
|
||||
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;
|
||||
D_local = 0; %if ~=0, simulation uses "segmented fiber with d+,d-)
|
||||
randomize_D = true;
|
||||
Dvec = getDispersionVector(nSegments, D_local, zdw, randomize_D, random_key+realiz);
|
||||
for s = 1:nSegments
|
||||
Dvec = getDispersionVector(nSegments, D_local, zdw, randomize_D, s.random_key+realiz);
|
||||
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,...
|
||||
"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);
|
||||
|
||||
end
|
||||
@@ -133,23 +132,23 @@ for realiz = 1:num_realiz
|
||||
|
||||
% 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 %%%%%%
|
||||
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);
|
||||
|
||||
PD_cell = {};
|
||||
for l = 1:N
|
||||
parfor l = 1:N
|
||||
|
||||
%%%%%% PD Square Law %%%%%%
|
||||
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 %%%%%%
|
||||
rx_bwl = 100e9;
|
||||
@@ -176,7 +175,7 @@ for realiz = 1:num_realiz
|
||||
% FFE
|
||||
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);
|
||||
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,...
|
||||
'showAnalysis',0,...
|
||||
"postFFE",[],...
|
||||
@@ -194,12 +193,12 @@ for realiz = 1:num_realiz
|
||||
|
||||
useviterbi = 0;
|
||||
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
|
||||
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
|
||||
|
||||
[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,...
|
||||
'showAnalysis', 0, ...
|
||||
"postFFE", [],...
|
||||
@@ -209,18 +208,17 @@ for realiz = 1:num_realiz
|
||||
output_mlse{l,ri,realiz} = mlse_results;
|
||||
|
||||
|
||||
|
||||
% DB tgt.
|
||||
useviterbi = 0;
|
||||
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
|
||||
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
|
||||
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);
|
||||
|
||||
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, ...
|
||||
'showAnalysis', 0,...
|
||||
"postFFE", []);
|
||||
@@ -232,6 +230,7 @@ for realiz = 1:num_realiz
|
||||
end
|
||||
|
||||
res = struct();
|
||||
res.settings = s;
|
||||
res.ffe = output_ffe;
|
||||
res.vnle = output_vnle;
|
||||
res.mlse = output_mlse;
|
||||
@@ -240,38 +239,13 @@ for realiz = 1:num_realiz
|
||||
% Save results
|
||||
save(fullfile(output_root, fname), 'res', '-v7.3');
|
||||
fprintf('Saved results to: %s\n', fullfile(output_root, fname));
|
||||
disp(datetime('now','TimeZone','local','Format','yyyyMs.Mdd_HHmmss'));
|
||||
|
||||
|
||||
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)
|
||||
% 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)).
|
||||
%
|
||||
% D is the nominal dispersion magnitude. For D>0 the link is segmented with
|
||||
|
||||
Reference in New Issue
Block a user