Changes from mwork PC.
PDP 2025 MPI analysis new focus on database and SQL
This commit is contained in:
Silas Oettinghaus
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% basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
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% db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
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if 1
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uloops = struct;
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uloops.precomp = [0,1];
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uloops.db_precode = [0,1];
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uloops.bitrate = [224,336,360,390,420,448].*1e9; %[300,330,360,390,420,450,480] [224,336,360,390,420,448] for MPI
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% uloops.laser_wavelength = [1293,1297.5,1302,1306.5,1310,1313.4,1318,1322.7,1327.4];
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uloops.laser_wavelength = [1310];
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uloops.M = [4,6,8];
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uloops.link_length = [1]; % 1,2,3,5,6,8,10
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wh = DataStorage(uloops);
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wh.addStorage("ber");
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% wh = submit_simulations(wh,"parallel",0,"simulation_mode",0);
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wh = submit_handle(@dsp_mpi,wh,"parallel",1);
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end
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a = wh_mpi_112gbd.getStoValue('ber',uloops.precomp, uloops.db_precode, uloops.bitrate(1) , uloops.laser_wavelength, uloops.M, uloops.link_length);
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%VNLE standalone
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try
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ber_vnle = cellfun(@(x) x.vnle_dfe_package{1,1}.ber_vnle, a);
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end
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%MLSE
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try
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ber_values_mlse = cellfun(@(s) cellfun(@(pkg) pkg.ber_mlse, s.vnle_pf_package, 'UniformOutput', false), a, 'UniformOutput', false);
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ber_values_mlse = cell2mat(ber_values_mlse{1});
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end
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%DB
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try
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ber_values_db = cellfun(@(s) cellfun(@(pkg) pkg.ber, s.dbtgt_package, 'UniformOutput', false), a, 'UniformOutput', false);
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ber_values_db = cell2mat(ber_values_db{1});
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end
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xax = [0
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3
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6
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12
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15
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21
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24
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27
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30
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45];
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cols = cbrewer2('Set1',8);
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% Compute min, max, and mean for PAM 4 MLSE
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min_mlse = min(ber_values_mlse, [], 2);
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max_mlse = max(ber_values_mlse, [], 2);
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mean_mlse = mean(ber_values_mlse, 2);
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err_lower_mlse = mean_mlse - min_mlse;
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err_upper_mlse = max_mlse - mean_mlse;
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err_mlse = [err_lower_mlse, err_upper_mlse];
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% Compute min, max, and mean for PAM 4 DB tgt.
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min_db = min(ber_values_db, [], 2);
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max_db = max(ber_values_db, [], 2);
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mean_db = mean(ber_values_db, 2);
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err_lower_db = mean_db - min_db;
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err_upper_db = max_db - mean_db;
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err_db = [err_lower_db, err_upper_db];
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figure(1)
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hold on
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title('MPI');
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% Plot the MLSE curve with bounded error using boundedline
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[hl_mlse, hp_mlse] = boundedline(xax, mean_mlse, err_mlse,'Color', cols(1,:));
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plot(xax,ber_values_mlse,'DisplayName','PAM 4 MLSE','Color',cols(1,:),'LineStyle','-','HandleVisibility','on','Marker','none','LineWidth',0.2);
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% Plot the DB tgt. curve with bounded error using boundedline
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[hl_db, hp_db] = boundedline(xax, mean_db, err_db, 'Color', cols(2,:));
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plot(xax,ber_values_db,'DisplayName','PAM 4 MLSE','Color',cols(2,:),'LineStyle','-','HandleVisibility','on','Marker','none','LineWidth',0.2);
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% Format the plot
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xticks(xax);
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set(gca, 'YScale', 'log');
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ylim([5e-5 0.4]);
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xlim([min(xax) max(xax)]);
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yline([4.85e-3, 2e-2], 'HandleVisibility', 'off');
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legend
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% beautifyBERplot()
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xlabel('Interference Attenuation');
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ylabel('BER');
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349
projects/HighSpeedExperiment_2024/auswertung MPI/dsp_mpi.m
Normal file
349
projects/HighSpeedExperiment_2024/auswertung MPI/dsp_mpi.m
Normal file
@@ -0,0 +1,349 @@
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function [output] = dsp_mpi(varargin)
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simulation_mode = 0;
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%%% Change folder
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curFolder = pwd;
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funcFolder=fileparts(mfilename('fullpath'));
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if ~isempty(funcFolder)
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cd(funcFolder);
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end
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%%% Run parameters
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% TX
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M = 4;
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fsym = 180e9;
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apply_pulsef = 1;
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fdac = 256e9;
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fadc = 256e9;
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random_key = 1;
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interference_attenuation = 0;
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is_mpi = 1;
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precomp = 0;
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db_precode = 0;
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db_encode = 0;
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rcalpha = 0.05;
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kover = 16;
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vbias_rel = 0.5;
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u_pi = 2.9;
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vbias = -vbias_rel*u_pi;
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laser_wavelength = 1293;
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laser_linewidth = 0;
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tx_bw_nyquist = 0.8;
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% Channel
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link_length = 1;
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% RX
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rop = -5;
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rx_bw_nyquist = 0.8;
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vnle_order1 = 50;
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vnle_order2 = 5;
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vnle_order3 = 5;
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vnle_order=[vnle_order1,vnle_order2,vnle_order3];
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dfe_order = [0 0 0];
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pf_ncoeffs = 1;
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alpha = 0;
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len_tr = 4096*2;
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mu_ffe1 = 0.0001;
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mu_ffe2 = 0.0008;
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mu_ffe3 = 0.001;
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mu_dc = 0.005;
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mu_dc = 0;
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mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
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mu_dfe = 0.0004;
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dfe_ = sum(dfe_order)>0;
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doub_mode = db_mode.no_db;
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%%% change specific parameter if given in varargin
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% Parse optional input arguments
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if ~isempty(varargin)
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var_s = varargin{1};
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if isstruct(var_s)
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fields = fieldnames(var_s);
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for i = 1:numel(fields)
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if isnumeric(fields{i})
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eval([fields{i}, ' = ', num2str( var_s.(fields{i}) ), ';']);
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fprintf("%s <-- %.2f \n", fields{i}, var_s.(fields{i}));
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else
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eval([fields{i}, ' = ', 'var_s.(fields{',num2str(i),'})' , ';']);
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end
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end
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else
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error('Optional variables should be passed as a struct.');
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end
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end
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if doub_mode ~= db_mode.db_encoded
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if precomp == 0 && db_precode == 1
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doub_mode = db_mode.db_precoded;
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db_precode = 1; % preceded data (in my measurement set, this corresponds to low precomp too!)
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discard_precode = 0; %
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emulate_precode = 0;
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legendentry = 'low precomp; precoded';
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disp('low precomp; precoded')
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elseif precomp == 1 && db_precode == 1
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doub_mode = db_mode.db_emulate;
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db_precode = 0; % preceded data (in my measurement set, this corresponds to low precomp too!)
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discard_precode = 0; %
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emulate_precode = 1;
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legendentry = 'high precomp; precoded';
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disp('high precomp; precoded')
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elseif precomp == 0 && db_precode == 0
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doub_mode = db_mode.db_discard;
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db_precode = 1; % preceded data (in my measurement set, this corresponds to low precomp too!)
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discard_precode = 1; %
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emulate_precode = 0;
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legendentry = 'no precomp; not precoded';
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disp('no precomp; not precoded')
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elseif precomp == 1 && db_precode == 0
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doub_mode = db_mode.no_db;
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db_precode = 0; % preceded data (in my measurement set, this corresponds to low precomp too!)
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discard_precode = 0; %
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emulate_precode = 0;
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legendentry = 'high precomp; not precoded';
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disp('high precomp; not precoded')
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end
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else
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end
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fsym_ = floor( bitrate*1e-9./log2(M) ).*1e9;
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if fsym_ ~= fsym
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fsym = fsym_;
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% 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);
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end
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f_nyquist = fsym/2;
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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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useGui = 0;
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% db = DBHandler("pathToDB",[basePath,'silas_labor.db']);
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filterParams = database.tables;
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% filterParams.Runs.run_id = 2958; % no db
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% filterParams.Runs.run_id = 2937; % no db
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filterParams.Configurations = struct( ...
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'bitrate', bitrate, ...
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'db_mode', db_precode+db_encode, ...
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'fiber_length', link_length, ...
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'interference_attenuation', [], ...
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'interference_path_length', [], ...
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'is_mpi', is_mpi, ...
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'pam_level', M, ...
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'precomp_amp', [], ...
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'rop_attenuation', 0, ...
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'symbolrate', [], ...
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'v_awg', [], ...
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'v_bias', [], ...
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'wavelength', laser_wavelength ...
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);
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selectedFields = {'Runs.run_id','Runs.tx_bits_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'};
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[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
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[~, uniqueIdx] = unique(dataTable.run_id); % Get unique run_id indices
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dataTable = dataTable(uniqueIdx,:); % Extract unique configurations for each run_id
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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))));
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output = struct();
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vnle_pf_package = {};
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vnle_dfe_package = {};
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dbtgt_package = {};
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disp(num2str(bitrate))
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for iatt = 1:numel(dataTable.interference_attenuation)
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current_run_id = dataTable.run_id(iatt);
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Tx_bits = load([basePath, char(dataTable.tx_bits_path(iatt))]);
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Tx_bits = Tx_bits.Bits;
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Symbols_mapped = PAMmapper(M,0).map(Tx_bits);
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Symbols_mapped.fs = fsym;
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Symbols = load([basePath, char(dataTable.tx_symbols_path(iatt))]);
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Symbols = Symbols.Symbols;
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Scpe_load = load([basePath, char(dataTable.rx_sync_path(iatt))]);
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Scpe_cell = Scpe_load.S;
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[~,~,found]=Scpe_cell{2}.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",1);
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if ~found
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Raw_signal = load([basePath, char(dataTable.rx_raw_path(1))]);
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Raw_signal = Raw_signal.Scpe_sig_raw;
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[~,Scpe_cell,found] =Raw_signal.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
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end
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if ~found
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if length(Symbols_mapped.signal) == sum(Symbols_mapped.signal == Symbols.signal)
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warning('Could not synchronize the received signal with the stored symbols!')
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else
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[~,Scpe_cell,found] =Raw_signal.tsynch("reference",Symbols_mapped,"fs_ref",fsym,"debug_plots",0);
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end
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if ~found
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warning('Could not synchronize the received signal with the stored symbols!')
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end
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end
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%
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% Raw_signal = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.55,"fs",Raw_signal.fs,"filterType",filtertypes.gaussian,"active",true).process(Raw_signal);
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%
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% Scpe_cell{1}.eye(fsym,M,"displayname",'eye','fignum',227);
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%
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% Raw_signal.spectrum("normalizeTo0dB",0,"fignum",11,"fft_length",2^12);
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% Raw_signal.move_it_spectrum("fignum",334);
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% Raw_signal.move_it_spectrum("fignum",334);
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fsym = Symbols.fs;
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if db_precode
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Symbols_precoded = Symbols;
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end
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proc_occ = min(15,length(Scpe_cell));
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for occ = 1:proc_occ
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Scpe_sig = Scpe_cell{occ};
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%%%%%% Sample to 2x fsym %%%%%%
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Scpe_sig = Scpe_sig.resample("fs_out",2*fsym);
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%%%%%% Sync Rx signal with reference %%%%%%
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[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
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Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.5,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
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Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
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%
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% Pform = Pulseformer("fsym",Scpe_sig.fs,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha,"matched",0);
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%
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% Scpe_sig_matched = Pform.process(Scpe_sig);
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%
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% Scpe_sig.spectrum("normalizeTo0dB",0,"fignum",336,"displayname","scope ");
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% Scpe_sig_matched.spectrum("normalizeTo0dB",0,"fignum",336,"displayname","matched");
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%%% EQUALIZING
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% 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);
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% 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);
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% 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);
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mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
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vnle_order=[vnle_order1,vnle_order2,vnle_order3];
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% %%%%% VNLE + DFE %%%%
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if 0
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eq_vnle_dfe = EQ("Ne",vnle_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
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eq_2 = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
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[result] = vnle(eq_vnle_dfe,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",doub_mode,"showAnalysis",1,"postFFE",[]);
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vnle_dfe_package{iatt,occ} = result;
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end
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%%%%% VNLE + PF + MLSE %%%%
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if 1
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try
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% len_tr = length(Symbols)-1000;
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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);
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% eq_vnle_ = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0,"order",vnle_order,"sps",2,"decide",0);
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pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
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mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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eq_2 = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
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[result] = vnle_postfilter_mlse(eq_vnle_,pf_,mlse_,M,Scpe_sig,Symbols,Tx_bits,"precode_mode",doub_mode,'showAnalysis',0,"postFFE",[]);
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vnle_pf_package{iatt,occ} = result;
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database.addProcessingResult(current_run_id,result.resultsMLSE, result.equalizerConfigMLSE);
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database.addProcessingResult(current_run_id,result.resultsVNLE, result.equalizerConfigVNLE);
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catch
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warning(['VNLE+MLSE fail: run id: ', num2str(current_run_id)],' occ:', num2str(occ), ' iatten: ',num2str(iatt))
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end
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end
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%%%%% Duobinary Targeting %%%%
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if 1
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try
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mlse_db = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
|
||||
eq_db = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
eq_2 = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0);
|
||||
|
||||
[result] = duobinary_target(eq_db, mlse_db, M, Scpe_sig, Symbols, Tx_bits, "precode_mode", doub_mode,'showAnalysis',0,"postFFE",[]);
|
||||
dbtgt_package{iatt,occ} = result;
|
||||
|
||||
database.addProcessingResult(current_run_id,result.resultsDBtgt, result.equalizerConfigDBtgt);
|
||||
|
||||
catch
|
||||
warning(['VNLE DB+MLSE fail: run id: ', num2str(current_run_id)],' occ:', num2str(occ), ' iatten: ',num2str(iatt))
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
%%%%%% %db signaling => db encoded %%%%%
|
||||
if 0
|
||||
mlse_db_enc = MLSE_viterbi("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
|
||||
eq_db_enc = EQ("Ne",vnle_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
[result] = duobinary_signaling(eq_db_enc, mlse_db_enc,M, Scpe_sig ,Symbols, Tx_bits);
|
||||
dbenc_package{iatt,occ} = result;
|
||||
end
|
||||
|
||||
|
||||
% autoArrangeFigures;
|
||||
disp('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ')
|
||||
fprintf('\n')
|
||||
|
||||
|
||||
end
|
||||
|
||||
|
||||
|
||||
if ~isempty(curFolder)
|
||||
cd(curFolder);
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
output.dataTable = dataTable;
|
||||
output.vnle_dfe_package = vnle_dfe_package;
|
||||
output.vnle_pf_package = vnle_pf_package;
|
||||
output.dbtgt_package = dbtgt_package;
|
||||
@@ -0,0 +1,91 @@
|
||||
|
||||
|
||||
basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
|
||||
database = DBHandler("pathToDB",[basePath,'silas_labor.db']);
|
||||
|
||||
filterParams = database.tables;
|
||||
filterParams.Configurations = struct( ...
|
||||
'bitrate', 336e9, ...
|
||||
'db_mode', 0, ...
|
||||
'fiber_length', 1, ...
|
||||
'interference_attenuation', [], ...
|
||||
'interference_path_length', [], ...
|
||||
'is_mpi', 1, ...
|
||||
'pam_level', 4, ...
|
||||
'rop_attenuation', 0 ...
|
||||
);
|
||||
|
||||
filterParams.EqualizerParameters.diff_precode = int32(db_mode.no_db);
|
||||
filterParams.EqualizerParameters.equalizer_structure = int32(equalizer_structure.vnle);
|
||||
|
||||
selectedFields = {'Configurations.run_id' 'Runs.rx_raw_path' 'Configurations.bitrate' 'Configurations.symbolrate' 'Configurations.pam_level' 'Configurations.db_mode' 'Configurations.rop_attenuation' 'Configurations.is_mpi' 'Configurations.interference_attenuation' 'EqualizerParameters.equalizer_structure' 'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'Measurements.power_pd_in' 'Measurements.power_mpi_interference' 'Measurements.power_mpi_signal' 'Results.BER' 'Results.SNR' 'Results.GMI' 'Results.Alpha'};
|
||||
|
||||
[dataTable,sql_query] = database.queryDB(filterParams, selectedFields);
|
||||
|
||||
fixedVars = {'run_id','eq_id','bitrate'};
|
||||
resultTable = groupIt(fixedVars,dataTable);
|
||||
|
||||
|
||||
|
||||
% Create a new figure
|
||||
figure(1);
|
||||
hold on
|
||||
unique_rates = unique(resultTable.bitrate);
|
||||
for i = 1:numel(unique_rates)
|
||||
% Plot BER vs. interference_attenuation
|
||||
plot(resultTable.power_mpi_signal(resultTable.bitrate==unique_rates(i),:)-resultTable.power_mpi_interference(resultTable.bitrate==unique_rates(i),:), resultTable.BER(resultTable.bitrate==unique_rates(i),:), 'o-', 'LineWidth', 1.5);
|
||||
end
|
||||
|
||||
% Label the axes and add a title
|
||||
xlabel('Interference Attenuation');
|
||||
ylabel('BER');
|
||||
title('BER vs. Interference Attenuation');
|
||||
|
||||
% Enable grid for better readability
|
||||
grid on;
|
||||
|
||||
beautifyBERplot;
|
||||
|
||||
|
||||
|
||||
|
||||
function resultTable = groupIt(fixedVars,dataTable)
|
||||
|
||||
% Group by run_id and eq_id (adjust grouping keys as needed)
|
||||
|
||||
[G, groupKeys] = findgroups(dataTable(:, fixedVars));
|
||||
|
||||
% Preallocate a cell array for aggregated data.
|
||||
varNames = dataTable.Properties.VariableNames;
|
||||
nVars = numel(varNames);
|
||||
aggData = cell(height(groupKeys), nVars);
|
||||
groupCount = zeros(height(groupKeys), 1); % To store the size of each group
|
||||
|
||||
% Loop over each group.
|
||||
for i = 1:height(groupKeys)
|
||||
idx = (G == i); % Logical index for group i
|
||||
groupCount(i) = sum(idx); % Count number of rows in this group
|
||||
% For each variable in the table:
|
||||
for j = 1:nVars
|
||||
colData = dataTable.(varNames{j});
|
||||
if isnumeric(colData)
|
||||
% For numeric data, compute the mean.
|
||||
aggData{i, j} = mean(colData(idx));
|
||||
else
|
||||
% For non-numeric data, take the first entry.
|
||||
if iscell(colData)
|
||||
aggData{i, j} = colData{find(idx, 1)};
|
||||
else
|
||||
aggData{i, j} = colData(find(idx, 1));
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
% Convert the aggregated cell array into a table.
|
||||
resultTable = cell2table(aggData, 'VariableNames', varNames);
|
||||
|
||||
% Append the group count as a new column.
|
||||
resultTable.nRows = groupCount;
|
||||
|
||||
end
|
||||
Reference in New Issue
Block a user