FSO analysis:

- minimal changes in several files
- new Timing Rec from Magnus

Classes/00_signals/Signal.m

@@ -971,8 +971,8 @@ classdef Signal
                 maxA = max(sig(100:end-100))*1.3;
                 minA = min(sig(100:end-100))*1.3;
 
-                % maxA = 0.12;
+                maxA = 0.12;
-                % minA = -0.08;
+                minA = -0.08;
 
                 difference= maxA-minA;
 
@@ -993,7 +993,9 @@ classdef Signal
                 % beautify
                 cm=flip(cbrewer2("RdYlBu",4096));
                 % cm=flip(cbrewer2("RdBu",4096));
+                % cm=flip(cbrewer2("Blues",4096));
                 cm(1,:) = [1,1,1]; % set zeros to white => clean background
+
                 colormap(cm);
                 % colormap('turbo');
                 % ax.CLim =  [0   50];

Classes/04_DSP/Timing_Recovery.m

@@ -9,7 +9,7 @@ classdef Timing_Recovery < handle
     end
 
     methods(Access=public)
-        function obj = FFE(options)
+        function obj = Timing_Recovery(options)
             arguments(Input)
 
                 options.timing_error_detector = 'Gardner';
@@ -25,12 +25,11 @@ classdef Timing_Recovery < handle
                 obj.(fn{n}) = options.(fn{n});
             end
 
-            obj.e = zeros(obj.order,1);
+ 
-            obj.error = 0;
 
         end
 
-        function data_out = process(obj, data_in)
+        function [data_out,timing_error] = process(obj, data_in)
 
             timing_synchronization = comm.SymbolSynchronizer( ...
                 "TimingErrorDetector", obj.timing_error_detector, ...
@@ -39,7 +38,8 @@ classdef Timing_Recovery < handle
                 "NormalizedLoopBandwidth", obj.normalized_loop_bandwidth, ...
                 "DetectorGain",        obj.detector_gain);
 
-            data_out.signal = timing_synchronization(data_in.signal);
+            data_out = data_in;
+            [data_out.signal,timing_error] = timing_synchronization(data_in.signal);
 
         end
     end

Functions/EQ_structures/dsp_runid.m

@@ -95,7 +95,7 @@ try
     adaption= 1;
     use_dd_mode = 1;
 
-    use_ffe         = 0;
+    use_ffe         = 1;
     use_dfe         = 0;
     use_vnle_mlse   = 0;
     use_dbtgt       = 0;
@@ -149,6 +149,9 @@ try
         % Scpe_sig.spectrum("fignum",22233,"normalizeTo0dB",0,"displayname",'Rx');
         % Scpe_sig.eye(fsym,M,"fignum",1024);
 
+        Scpe_sig.signal = Scpe_sig.signal(1:2*Symbols.length);
+        Scpe_sig.signal = real(Scpe_sig.signal);
+
         if duob_mode ~= db_mode.db_encoded
 
             if use_ffe
@@ -246,7 +249,7 @@ try
                 %ML-based MLSE (L=2)
                 mu_ml = 0.01; training_epochs = 100;
                 ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
-                    "len_tr",length(Scpe_sig),"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
+                    "len_tr",length(Scpe_sig)/4,"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
                     "traceback_depth",128,"L",1,"delta",4,"adaptive_mu",0);
 
                 [ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Scpe_sig, Symbols, Tx_bits,"precode_mode",duob_mode);

Functions/EQ_structures/ffe.m

@@ -157,7 +157,7 @@ function displayAnalysis(eq_noise, eq_signal_sd, rx_signal, eq_, tx_symbols, M,
 
     % Initialize figure handles
     % Corrected line - added tx_symbols as second positional argument
-    showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 100);
+    % showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 100);
 
     warning off
     showLevelScatter(eq_signal_sd, tx_symbols, "fignum", 101);
@@ -174,19 +174,20 @@ function displayAnalysis(eq_noise, eq_signal_sd, rx_signal, eq_, tx_symbols, M,
     end
 
     try
-        figure(339);
+        figure(339);hold on
-        showEQfilter(eq_.e_tr, eq_signal_sd.fs.*2,"displayname",'training','fignum',339);
+        showEQfilter(eq_.e, eq_signal_sd.fs.*2,"displayname",'training','fignum',339);
-        showEQfilter(eq_.e, eq_signal_sd.fs.*2,"displayname",'dec. directed','fignum',339);
+        % showEQfilter(eq_.e, eq_signal_sd.fs.*2,"displayname",'dec. directed','fignum',339);
         legend on
     end
 
-    try
+    % try
-        figure(240); hold on; plot(pow2db(movmean(eq_.debug_struct.error_tr',100)));ylim([-30,3]);title('error training');
+    %     figure(240); hold on; plot(pow2db(movmean(eq_.debug_struct.error_tr',100)));ylim([-30,3]);title('error training');
+    % 
+    %     figure(241); hold on; plot(pow2db(movmean(eq_.debug_struct.update_tr',100)));title('update step training');
+    % 
+    %     figure(242); hold on; plot(pow2db(movmean(eq_.debug_struct.update',1000)));title('update step dd');
+    % end
 
-        figure(241); hold on; plot(pow2db(movmean(eq_.debug_struct.update_tr',100)));title('update step training');
+    eq_signal_sd.eye(eq_signal_sd.fs,M,"displayname",'Eye','fignum',105);
-    
-        figure(242); hold on; plot(pow2db(movmean(eq_.debug_struct.update',1000)));title('update step dd');
-    end
-    % eq_signal_sd.eye(eq_signal_sd.fs,M,"displayname",'Eye','fignum',105);
 
 end

Functions/EQ_structures/ml_mlse.m

@@ -45,8 +45,7 @@ end
 
 ml_mlse_results.config = Equalizerstruct();
 
-eq_small = strip_eq(eq_, 10);
+eq_ = strip_eq(eq_, 10);
-json_str = jsonencode(eq_small);
 
 ml_mlse_results.config.eq = jsonencode(eq_);
 ml_mlse_results.config.equalizer_structure = int32(equalizer_structure.ml_mlse);
@@ -135,6 +134,9 @@ function eq_out = strip_eq(eq_, max_elems)
                 eq_.(props{i}) = [];
             end
         end
+        if issparse(val)
+            eq_.(props{i}) = find(eq_.(props{i}));
+        end
     end
     eq_out = eq_;
 end

Functions/EQ_structures/vnle_postfilter_mlse.m

@@ -283,7 +283,7 @@ if ~isempty(postFFE)
     showEQcoefficients('n1', postFFE.e, "displayname", 'Coefficients', 'fignum', 338);
 end
 
-showEQcoefficients('n1', eq_.e,'n2', eq_.e2,'n3', eq_.e3, "displayname", 'Coefficients', 'fignum', 339);
+% showEQcoefficients('n1', eq_.e1,'n2', eq_.e2,'n3', eq_.e3, "displayname", 'Coefficients', 'fignum', 339);
 showEQfilter(eq_.e, eq_signal_sd.fs.*2);
 
 figure(340); clf;

projects/FSO_transmission/first_analysis.m

@@ -1,15 +1,15 @@
 
-base = "C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC";
+base = "C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC";
 mode = 0; %0 oder 1
 M = 2;
 
 all_files = dir(fullfile(base, "**/*.mat"));
 
 if M == 2
-    tx_data = load("C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\14G_PAM2\tx_info\tx_info_PAM2_14Gbd0.75RRC.mat");
+    tx_data = load("C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\14G_PAM2\tx_info\tx_info_PAM2_14Gbd0.75RRC.mat");
     filename = fullfile(base, "14G_PAM2\M=2_Rs=1.4e10_Fs=8e10_I=265mA_RoP=46.3mW_L=31m_PS=RRC_rolloff=0.75_Mode=Rise.mat");
 elseif M == 4
-    tx_data = load("C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\6G_PAM4\tx_info\tx_info_PAM4_6Gbd0.6RRC.mat");
+    tx_data = load("C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\6G_PAM4\tx_info\tx_info_PAM4_6Gbd0.6RRC.mat");
     filename = fullfile(base, "6G_PAM4\M=4_Rs=6e9_Fs=8e10_I=255mA_RoP=42.3mW_L=31m_PS=RRC_rolloff=0.6_Mode=Rise.mat");
 end
 
@@ -53,10 +53,14 @@ Bits_ = PM.demap(Symbols);
 assert(ber == 0);
 
 %% For comparison, apply pulsef on Tx Symbols
-Pform = Pulseformer("fsym",fsym,"fdac",fs,"pulse","rc","pulselength",16,"alpha",rolloff);
+Pform = Pulseformer("fsym",fsym,"fdac",fs,"pulse","rrc","pulselength",16,"alpha",rolloff);
-Digi_sig_compare = Pform.process(Symbols);
+Digi_sig_tx_compare = Pform.process(Symbols);
-MF = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
+
-Rx_sig_compare = MF.process(Digi_sig_compare);
+Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
+Digi_sync = Pform.process(Symbols);
+
+MF = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
+Rx_sig_compare = MF.process(Digi_sig_tx_compare);
 
 %%
 
@@ -68,53 +72,47 @@ scoperead_volts = (traceData.RawData - traceData.YReference) * traceData.YIncrem
 demystified = isequal(traceData.YData,scoperead_volts);
 assert(demystified);
 
+timesig_compare = [0:1:datas.tr.lastData(1).trace.ch3.Points-1] ./ fs;
+timesig = datas.tr.lastData(1).trace.ch3.XData;
+assert(isequal(traceData.YData,scoperead_volts));
+
 Scope_sig = Electricalsignal(traceData.YData,"fs",fs);
 
-Scope_sig.plot("displayname",'raw','fignum',100);
+%%
-Scope_sig.spectrum("displayname",'raw','fignum',101)
-
 % 1) matched filter
-% pulse is symmetric, hence we can use pulsef firectly as matched filter.
+% pulse is symmetric, hence we can use pulsef directly as matched filter.
 % It feels off (bit I think correct) that the fsym is now the output freq.!!
 % -> output 2 sps to omit timing recovery!?
-Pform = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1);
+apply_matched_filter = 0;
-Rx_matched = Pform.process(Scope_sig);
+k = 4;
-Rx_matched.spectrum("displayname",'Signal after matched filter','fignum',1);
+if apply_matched_filter
     
+    Pform = Pulseformer("fsym",fsym,"fdac",k*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1);
+    Rx_matched = Pform.process(Scope_sig);
+else
 
-%%
+    Rx_matched = Filter('filtdegree',4,"f_cutoff",fsym*0.5,"fs",Scope_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scope_sig);
+    Rx_matched = Rx_matched.resample("fs_out",k*fsym);
+end
+Rx_matched.spectrum();
 
-sys = comm.SymbolSynchronizer('TimingErrorDetector', 'Gardner (non-data-aided)', ...
+%% Timing Rec
-                              'SamplesPerSymbol', 2, ...
+apply_timing_rec = 1;
-                              'DampingFactor', 0.7, ...
+if apply_timing_rec
-                              'NormalizedLoopBandwidth', 0.01);
+    [Rx_symbolsync, timing_error] = Timing_Recovery("timing_error_detector",'Gardner (non-data-aided)','sps',k,'damping_factor',0.1,'normalized_loop_bandwidth',0.1,'detector_gain',2.7).process(Rx_matched);
-Rx_symbolsync = Rx_matched;
+    figure();plot(timing_error);
-[Rx_symbolsync.signal, timing_error] = sys(Rx_matched.signal);
+    Rx_symbolsync.fs = fsym;
-
+    % Tsynch
-plot(timing_error); % If this is a ramp, you have drift!
+    [~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_symbolsync.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
-
+    Rx_synced = Rx_synced_cell{1};
-%% timing sync -> at this point we still have no symbol timing recovery, we
+    sps = 1;
-% % try to do this with 2sps EQ!
+else
-
+    % Tsynch
-[~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_symbolsync.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 1);
+    [~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_matched.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
-
+    Rx_synced = Rx_synced_cell{1};
-
+    Rx_synced = Rx_synced.resample("fs_out",2*fsym);
-%% not working.. 
+    sps = 2;
-Rx_synced = Rx_synced_cell{1};
+end
-len_tr  = 4096*2;
-mu_ffe1 = 0.0001;
-mu_ffe2 = 0.0008;
-mu_ffe3 = 0.001;
-mu_dc = 0.005;
-mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
-mu_dfe = 0.0004;
-duob_mode = db_mode.no_db;
-
-Rx_synced.plot("displayname",'RX: Matched+Sync+2sps','fignum',2);
-
-Digi_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped','fignum',1,'normalizeTo0dB',0);
-Rx_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped + matched filtered ','fignum',1,'normalizeTo0dB',0);
-Rx_synced.normalize("mode","rms").spectrum("displayname",'RX: matched filtered + synced','fignum',1,'normalizeTo0dB',0);
 
 if M == 2
     ber_in_paper = 10^(-2.6); %fig 3a) 4 Gb/s MWIR FSO Transmission using Directly Modulated QCL and an Uncooled UTC-PD at Room-Temperature
@@ -122,50 +120,121 @@ elseif M == 4
     ber_in_paper = 10^(-2.5);
 end
 
-%% -------------------- FFE --------------------
+
+%%
+
+Rx_synced = Rx_synced_cell{1};
+
+
+% -------------------- FFE --------------------
 % requires some more digging what is going on :-)
-eq_ffe      = EQ("Ne",[50, 5, 5],"Nb",[2,0,0], ...
+eq_ffe      = EQ("Ne",[50, 1, 1],"Nb",[2,0,0], ...
-    "training_length",len_tr,"training_loops",5,"dd_loops",5, ...
+    "training_length",512,"training_loops",5,"dd_loops",5, ...
-    "K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
+    "K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
     "FFEmu",0,"plotfinal",0,"ideal_dfe",1);
 
-ffe_results = ffe(eq_ffe,M,Rx_synced,Symbols,Bits, ...
+vars = logspace(-4,-3,36);
-    "precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
-    "eth_style_symbol_mapping",mapping_style);
 
-% ffe_results.metrics.print
+parfor i = 1:numel(vars)
-fprintf('My EQ: %.1e \n',ffe_results.metrics.BER);
+
+    len_tr  = 4096;
+    mu_ffe1 = 0.01;% mus(i);%0.0001;
+    mu_ffe2 = 0.0008;
+    mu_ffe3 = 0.001;
+    mu_dc = 0.005;
+    mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
+    mu_dfe = vars(i);
+    duob_mode = db_mode.no_db;
+
+    % requires some more digging what is going on :-)
+    eq_ffe_1      = EQ("Ne",[150, 1, 0],"Nb",[50,0,0], ...
+        "training_length",len_tr,"training_loops",5,"dd_loops",5, ...
+        "K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
+        "FFEmu",0,"plotfinal",0,"ideal_dfe",0);
+
+    eq_ffe_2 = FFE("epochs_tr",1,"epochs_dd",vars(i),"len_tr",4096,"mu_dd",vars(i),"mu_tr",vars(i),"order",999,"sps",1,"decide",0, "adaption",adaption_method.nlms,"dd_mode",0);
+    % eq_ffe_2 = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"ffe_mu_dd",1e-5,"dfe_mu_dd",mus(i),"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",50,"dfe_order",10,"sps",1,"decide",1);
+
+
+    ffe_results = ffe(eq_ffe_1,M,Rx_synced,Symbols,Bits, ...
+        "precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
+        "eth_style_symbol_mapping",mapping_style);
+
+    ffe_results.metrics.BER
+    bers(i) = ffe_results.metrics.BER;
+end
+
+figure();
+plot(vars,bers);
+yline(ber_in_paper)
+beautifyBERplot();
+% 
 fprintf('Paper: %.1e \n \n',ber_in_paper);
+ffe_results.metrics.print("description",'FFE');
+fprintf('FFE: %.1e \n',ffe_results.metrics.BER);
+
 
 %% -------------------- VNLE + MLSE --------------------
+len_tr  = 4096;
+mu_ffe1 = 0.0001;% mus(i);%0.0001;
+mu_ffe2 = 0.0008;
+mu_ffe3 = 0.001;
+mu_dc = 0.005;
+mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
+mu_dfe = 0.0004;
+duob_mode = db_mode.no_db;
 
-pf_ncoeffs  = 1;
+pf_ncoeffs  = 4;
-eq_v        = EQ("Ne",[100, 5, 5],"Nb",[0, 0, 0], ...
+
+vars = 1:7;
+bers = zeros(size(vars));
+parfor i = 1:numel(vars)
+    
+    eqv  = EQ("Ne",[200, 1, 0],"Nb",[2, 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, ...
+    "K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
     "FFEmu",0,"plotfinal",0,"ideal_dfe",1);
-pf_         = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
+    pf_ncoeffs  = vars(i);
-mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0,"eth_style",mapping_style).levels);
+    pf      = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
-
+    mlse_   = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0,"eth_style",mapping_style).levels);
-[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Rx_synced, Symbols, Bits, ...
+    [vnle_results, mlse_results] = vnle_postfilter_mlse(eqv, pf, mlse_, M, Rx_synced, Symbols, Bits, ...
-    "precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
+        "precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
-
+    fprintf('Paper: %.1e \n \n',ber_in_paper);
-mlse_results.metrics.print("description",'MLSE')
+    vnle_results.metrics.print("description",'VNLE');
-fprintf('My EQ: %.1e \n',mlse_results.metrics.BER);
+    mlse_results.metrics.print("description",'MLSE');
-fprintf('Paper: %.1e \n \n',ber_in_paper);
+    bers(i) = mlse_results.metrics.BER;
+end
+%%
+figure();hold on
+plot(vars,bers_ffe,'DisplayName','FFE [200,0,0] + PF + MLSE');
+plot(vars,bers_vnle,'DisplayName','VNLE [200,1,0] + PF + MLSE');
+plot(vars,bers_vnledfe,'DisplayName','VNLE [200,1,0] + DFE [2] + PF + MLSE');
+plot(vars,bers_vnledfe_ideal,'DisplayName','VNLE [200,1,0] + ideal DFE [2] + PF + MLSE');
+yline(ber_in_paper);
+yline([2e-2, 4.85e-3, 3.8e-3, 2,2e-4],'LineWidth',2,'Color',[0.8,0.8,0.8],'LineStyle',':','HandleVisibility','off');
+ylim([1e-5,0.1]);
+beautifyBERplot();
 
 %% -------------------- DB target --------------------
 mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);
 
 eq_       = EQ("Ne",[50, 5, 5],"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",1);
+    "K",sps,"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_synced, Symbols, Bits, ...
     "precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", [],"eth_style_symbol_mapping",mapping_style);
 
-dbt_results.metrics.print("description",'Duobinary');
-mlse_results.metrics.print
-fprintf('My EQ: %.1e \n',dbt_results.metrics.BER);
 fprintf('Paper: %.1e \n \n',ber_in_paper);
+dbt_results.metrics.print("description",'Duobinary');
 
 
+%%
+
+%ML-based MLSE (L=2)
+mu_ml = 0.01; training_epochs = 100;
+ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
+    "len_tr",len_tr,"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",1, ...
+    "traceback_depth",128,"L",3,"delta",4,"adaptive_mu",0);
+
+[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode);
+ml_mlse_results.metrics.print("description",'ML ');

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_EYES.m

@@ -2,7 +2,7 @@ dsp_options.storage_path    = 'Z:\2024\sioe_labor\';
 dsp_options.max_occurences = 1;
 database    = DBHandler("dataBase", 'labor_highspeed', "type",  'mysql' );
 
-rate = [300e9];
+
 cols = cbrewer2('BuPu',25);
 cols = [cols(end-10:2:end,:)];
 cols = cbrewer2('Set1',6);
@@ -12,10 +12,10 @@ fig=figure(fignum);clf;
 
 dbmode = 0;
 
-
 % 1 - PAM 4 with preemphasis
 fp = QueryFilter();
-M = 6;
+M = 8;
+rate = [360e9];
 fp.where('Runs', 'pam_level','EQUALS', M);
 fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
 fp.where('Runs', 'fiber_length','EQUALS', 2);
@@ -23,7 +23,7 @@ fp.where('Runs', 'wavelength','EQUALS', 1310);
 fp.where('Runs', 'db_mode','EQUALS', dbmode);
 fp.where('Runs', 'rop_attenuation','EQUAL', 0);
 
-[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
+[dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
 
 dataTable   = queryRunid(dataTable.run_id, database);
 fsym        = dataTable.symbolrate;
@@ -33,14 +33,36 @@ duob_mode   = db_mode(strrep(dataTable.db_mode,'"',''));
 % Load and Sync signal data from DB
 [Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
 
+Scpe_sig_syncd = Scpe_cell{1};
+Scpe_sig_syncd.eye(fsym,M,"fignum",rate.*1e-9*M+1,"displayname",' Eye of Signal');
+%%%%%% SNR CHEAT - Avges the measured signal occurences found after correlation in "tsynch" %%%%%%
+average_signals = 1;
+if average_signals
+    Scpe_sig_avg = Scpe_sig_syncd;
+    scope_mean = zeros(size(Scpe_cell{1}.signal));
+    for n=1:numel(Scpe_cell)
+        scope_mean = scope_mean + Scpe_cell{n}.signal;
+    end
+    scope_mean = scope_mean ./ n;
+    Scpe_sig_avg.signal = scope_mean;
+
+    Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1);
+    Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
+    Scpe_sig_avg = Scpe_sig_avg.*1.25;
+    Scpe_sig_avg.eye(fsym,M,"fignum",rate.*1e-9*M,"displayname",' Eye of AVG Signal');
+end
+
 % Preprocess signal
-Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
+Scpe_sig = preprocessSignal(Scpe_sig_avg, Symbols, fsym);
 
 Scpe_sig.eye(fsym,M,"fignum",M*10);
 
+
+
 %% === EXPORT TO TIKZ ===
-% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\eye_pam_',num2str(M),'.tikz'];
+
-% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\vnle_optimization.tikz'];
+% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G_submission\media\matlab2tikz\eye_pam_',num2str(M),'-2.tikz'];
+% % outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\vnle_optimization.tikz'];
 % matlab2tikz(outfile, ...
 %     'width','\fwidth', ...
 %     'height','\fheight', ...

projects/HighSpeedExperiment_2024/Auswertung_JLT/run_dsp_from_db.m

@@ -42,14 +42,14 @@ fp = QueryFilter();
 % fp.where('Runs', 'run_id','EQUALS', 2776);
 M = 6;
 fp.where('Runs', 'pam_level','EQUALS', M);     
-% fp.where('Runs', 'bitrate','EQUALS', 390e9);%360,390
+fp.where('Runs', 'bitrate','EQUALS', 360e9);%360,390
 % fp.where('Runs', 'symbolrate','EQUALS', 195e9);
-fp.where('Runs', 'fiber_length','EQUALS', 10);   
+fp.where('Runs', 'fiber_length','EQUALS', 2);   
 fp.where('Runs', 'is_mpi','EQUALS', 0);                
 % fp.where('Runs', 'interference_path_length','EQUALS', 1000);      
 % fp.where('Runs', 'loop_id','GREATER_THAN', 11);   
 % fp.where('Runs', 'sir','EQUALS',18);  
-% fp.where('Runs', 'wavelength','EQUALS', 1310);         
+fp.where('Runs', 'wavelength','EQUALS', 1310);         
 fp.where('Runs', 'db_mode','EQUALS', 0);              
 fp.where('Runs', 'rop_attenuation','EQUAL', 0);      
 % fp.where('Runs', 'power_pd_in','LESS_THAN', 7);    
@@ -70,7 +70,7 @@ wh.addStorage("mlmlse_package");
 
 %% === RUN IT ===
 
-[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "parallel", 'wh', wh, 'waitbar', true);
+[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "serial", 'wh', wh, 'waitbar', true);
 
 
 

projects/HighSpeedExperiment_2024/baudrate_evaluation.m

@@ -1,5 +1,5 @@
 
-filename = "C:\Users\sioe\Documents\High_Speed_Measurement_2024\baudrate_sweep_b2b\PAMX_b2b_baudrate20241024_210648_wh.mat";
+filename = "F:\2024\sioe\High Speed Messungen Oktober\baudrate_sweep_b2b\PAMX_b2b_baudrate20241024_210648_wh_final.mat";
 
 a = load(filename);
 wh = a.obj;

projects/IMDD_base_system/minimal_example.m

@@ -8,7 +8,7 @@ fdac            = 256e9;
 fadc            = 256e9;
 random_key      = 1;
 
-rcalpha         = 0.05;
+rcalpha         = 0.6;
 kover           = 16;
 
 duob_mode = db_mode.no_db;
@@ -25,7 +25,7 @@ tx_bw_nyquist   = 0.8;
 link_length     = 1;
 
 % RX
-rop = -8;
+rop = -9;
 rx_bw_nyquist = 0.8;
 
 vnle_order1 = 50;
@@ -51,7 +51,7 @@ mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
 mu_dfe = 0.0004;
 
 
-Pform       = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
+Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
 
 [Digi_sig,Symbols,Tx_bits] = PAMsource(...
     "fsym",fsym,"M",M,"order",18,"useprbs",0,...
@@ -65,7 +65,7 @@ Pform       = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",1
 %%%%% AWG
 El_sig = M8199A("kover",kover).process(Digi_sig);
 % El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",1).process(Digi_sig);
-El_sig.spectrum("displayname",'Digi Spectrum','fignum',100,'normalizeTo0dB',0);
+El_sig.spectrum("displayname",'Digi Spectrum','fignum',1,'normalizeTo0dB',1);
 % El_sig = El_sig.setPower(0,"dBm");
 
 %%%%% Electrical Driver Amplifier %%%%%%
@@ -101,32 +101,53 @@ Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
     "delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
     "samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
     "adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);
+%%
 
-%%%%%% Sample to 2x fsym %%%%%%
+% 1) matched filter
-Scpe_sig =  Scpe_sig.resample("fs_out",2*fsym);
+% pulse is symmetric, hence we can use pulsef firectly as matched filter.
-Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
+% It feels off (bit I think correct) that the fsym is now the output freq.!!
+% -> output 2 sps to omit timing recovery!?
+Pform = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha,"matched",1);
+Scpe_sig = Pform.process(Scpe_sig);
+Scpe_sig.spectrum("displayname",'Signal after matched filter','fignum',1,'normalizeTo0dB',1);
+% 
 
+% %%
+% %%%%%% Sample to 2x fsym %%%%%%
+% Scpe_sig =  Scpe_sig.resample("fs_out",2*fsym);
+% Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
+
+%%
 %%%%%% Sync Rx signal with reference %%%%%%
-[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
+[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",1);
 Scpe_sig.spectrum("displayname",'Opt Spectrum','fignum',11,'normalizeTo0dB',1);
 
-Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.5,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
+% Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.5,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
 
 Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
+Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
+
+%%
 
 % -------------------- FFE --------------------
 ffe_order   = [50, 0, 0];
-eq_ffe      = EQ("Ne",ffe_order,"Nb",[0,0,0], ...
+eq_      = EQ("Ne",ffe_order,"Nb",[2,0,0], ...
     "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);
 
-output.ffe_results = ffe(eq_ffe,M,Scpe_sig,Symbols,Tx_bits, ...
+% eq_ = FFE("epochs_tr",4,"epochs_dd",5,"len_tr",4096,"mu_dd",0.01,"mu_tr",0.01,"order",50,"sps",2,"decide",0, "adaption",adaption_method.nlms,"dd_mode",1);
-    "precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
+eq_ = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",512,"ffe_mu_dd",1e-4,"dfe_mu_dd",5e-4,"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",99,"dfe_order",99,"sps",2,"decide",0);
+
+
+output.ffe_results = ffe(eq_,M,Scpe_sig,Symbols,Tx_bits, ...
+    "precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
     "eth_style_symbol_mapping",0);
 
 output.ffe_results.metrics.print
 
+%%
+
 % -------------------- VNLE + MLSE --------------------
 pf_ncoeffs  = 1;
 ffe_order3  = [50, 5, 5];
@@ -139,8 +160,11 @@ pf_         = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
 mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
 
 [output.vnle_results, output.mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
-    "precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", [], "eth_style_symbol_mapping", 0);
+    "precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", 0);
 
+output.mlse_results.metrics.print
+
+%%
 
 % -------------------- DB target --------------------
 mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);

projects/Lab_analysis/data_analysis_and_plot_scripts/lambda_vs_spectrum.m

@@ -0,0 +1,88 @@
+wh_aeon = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Lab_analysis\aeon_soa_measurement_lambda_plaser_pump.mat");
+wh_aeon = wh_aeon.wh;
+wh_thor = load("C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\Lab_analysis\thorlabs_pdfa_measurement_lambda_plaser_pump.mat");
+wh_thor = wh_thor.wh;
+
+% Silas' custom "warehouse" datatype
+wh_aeon.showInfo;
+wh_thor.showInfo;
+
+%% PLOT OSA SPECTRA
+
+figure();hold on
+cols = linspecer(4);
+cols = cbrewer2('Paired',8);
+subplot(1,2,1);hold on;
+wh = wh_thor;
+ccnt = 1;
+
+pumps = wh.parameter.pump.values;
+plasers = wh.parameter.laserpower.values;
+scnt = 1;
+for p = 1:numel(plasers)
+    
+    for pmp = 1:numel(pumps)
+        subplot(numel(plasers),numel(pumps),scnt);hold on;
+        title(sprintf('Laser: %d dBm; Pump: %d %',plasers(p),pumps(pmp)),"Interpreter","tex")
+        for lambda = wh.parameter.lambda.values
+    
+            spectrum_osa = wh_thor.getStoValue('spectrum_osa',plasers(p),lambda,pumps(pmp));
+            wavelength_osa = wh_thor.getStoValue('wavelength_osa',plasers(p),lambda,pumps(pmp));
+            plot(wavelength_osa,spectrum_osa,'DisplayName',sprintf('P_{in}: %d dB ',plasers(p)),'Color',cols(ccnt,:));
+            
+
+            spectrum_osa = wh_aeon.getStoValue('spectrum_osa',plasers(p),lambda,pumps(pmp));
+            wavelength_osa = wh_aeon.getStoValue('wavelength_osa',plasers(p),lambda,pumps(pmp));
+            plot(wavelength_osa,spectrum_osa,'DisplayName',sprintf('P_{in}: %d dB ',plasers(p)),'Color',cols(ccnt+1,:));
+
+
+            ylim([-60, 20]);
+            beautifyBERplot("logscale",false,"setmarkers",0,"setcolors",0);
+        end
+        scnt = scnt +1;
+    end
+    ccnt = ccnt+2;
+end
+
+
+xlabel('Wavelength [nm]');
+ylabel('OSNR [dB]')
+
+
+%%
+
+figure();hold on
+cols = linspecer(4);
+subplot(1,2,1);hold on;
+wh = wh_thor;
+ccnt = 1;
+
+pumps = wh.parameter.pump.values;
+plasers = wh.parameter.laserpower.values;
+scnt = 1;
+for p = 1:numel(plasers)
+    
+    for pmp = 1:numel(pumps)
+        subplot(numel(plasers),numel(pumps),scnt);hold on;
+        
+
+        ase_noise = wh_thor.getStoValue('pase_osa',plasers(p),wh.parameter.lambda.values,pumps(pmp));
+        signal = wh_thor.getStoValue('psig_osa',plasers(p),wh.parameter.lambda.values,pumps(pmp));
+        plot(wh.parameter.lambda.values,ase_noise,'DisplayName',sprintf('P_{in}: %d dB ',plasers(p)),'Color',cols(ccnt,:));
+
+
+        ase_noise = wh_aeon.getStoValue('pase_osa',plasers(p),wh.parameter.lambda.values,pumps(pmp));
+        signal = wh_aeon.getStoValue('psig_osa',plasers(p),wh.parameter.lambda.values,pumps(pmp));
+        plot(wh.parameter.lambda.values,ase_noise,'DisplayName',sprintf('P_{in}: %d dB ',plasers(p)),'Color',cols(ccnt,:),'LineStyle','-');
+
+        ylim([-60, 20]);
+        beautifyBERplot("logscale",false,"setmarkers",0,"setcolors",0);
+
+        scnt = scnt +1;
+    end
+    ccnt = ccnt+1;
+end
+
+
+xlabel('Wavelength [nm]');
+ylabel('OSNR [dB]')

projects/WDM/WDM_auswertung.m

@@ -1,5 +1,5 @@
 
-base = "C:\Users\Silas\Nextcloud\Cluster";
+base = "C:\Users\Silas\Nextcloud4\Cluster";
 all_files = dir(fullfile(base, "**/*.mat"));
 
 schemes = ["co","pair","alt","seg"];
@@ -13,8 +13,8 @@ T = table('Size',[0 10], ...
 rx = "^WDM_(?<date>\d{8})_(?<time>\d{6})_n(?<node>\d+)_(?<jobid>\d+)_" + ...
      "(?<L>\d+)km_(?<Nch>\d+)ch_(?<df>\d+)ghz_(?<scheme>[a-z]+)_alpha(?<alpha>\d+(?:_\d+)?)\.mat$";
 
-for k = 1:numel(all_files)
+for i = 1:numel(all_files)
-    f = all_files(k);
+    f = all_files(i);
     folder = string(f.folder);
     file   = string(f.name);
 
@@ -44,6 +44,8 @@ for k = 1:numel(all_files)
 end
 
 %%
+
+
 idx = strcmp(T.scheme,"co") & ...
       T.alpha == 0.4 & ...
       T.date >= datetime(2026,1,9) & ...
@@ -51,13 +53,181 @@ idx = strcmp(T.scheme,"co") & ...
 
 T_sel = T(idx,:);
 
-i = 2;
+% ---- Load all res ----
-res = load(fullfile(T_sel.folder(i),T_sel.file(i)),'res');
+R = cell(numel(T_sel.file),1);
-res = res.res;
+for i = 1:numel(T_sel.file)
+    S = load(fullfile(T_sel.folder(i),T_sel.file(i)),'res');
+    S.res = strip_config_from_res(S.res);
+    R{i} = S.res;
+end
+
+res_all = combine_res_list(R);
+res_all = drop_empty_realizations(res_all);
+
+
+%%
+
+
+
+%%
+
 
 %%
 % Routine A: plot BER curves and compute crossings
-S = plot_BER_vs_ROP(res, 'fec', 3.8e-3);
+S = plot_BER_vs_ROP(res_all, 'fec', 3.8e-3);
 
 %% Routine B: violin plot (independent)
-plot_FEC_violin(res, 'tech','VNLE', 'fec',3.8e-3, 'ylim',[-10 0]);
+plot_FEC_violin(res_all, 'tech','VNLE', 'fec',3.8e-3, 'ylim',[-10 0],'eval_ptr',5);
+
+
+%%
+
+function res = strip_config_from_res(res)
+% Remove the "config" payload from every non-empty result cell
+% Works whether entries are structs-with-field or objects-with-property.
+
+techs = {'ffe','dfe','vnle','mlse','dbt'};
+
+for t = 1:numel(techs)
+    fn = techs{t};
+    if ~isfield(res, fn) || isempty(res.(fn)), continue; end
+
+    C = res.(fn);
+    if ~iscell(C), continue; end
+
+    idx = find(~cellfun('isempty', C));  % fast builtin
+    for k = 1:numel(idx)
+        x = C{idx(k)};
+
+        % Case 1: struct entry with field "config"
+        if isstruct(x) && isfield(x,'config')
+            x = rmfield(x,'config');
+
+        % Case 2: object entry with property "config"
+        elseif isobject(x) && isprop(x,'config')
+            try
+                x.config = [];  % lighter than keeping Equalizerstruct JSON
+            catch
+                % ignore if class forbids assignment
+            end
+        end
+
+        C{idx(k)} = x;
+    end
+
+    res.(fn) = C;
+end
+end
+
+
+% ===================== Local helper functions =====================
+
+function res_out = combine_res_list(R)
+% Concatenate along realization dimension (dim=3) for all techniques.
+% Requires consistent sizes in dims 1,2,4 and consistent eval_dist_km.
+
+fieldsTech = ["ffe","dfe","vnle","mlse","dbt"];
+
+% Start with first
+res_out = R{1};
+
+% --- Build common distance axis (union, stable) ---
+dist_all = res_out.eval_dist_km(:).';
+for k = 2:numel(R)
+    dist_all = unique([dist_all, R{k}.eval_dist_km(:).'], 'stable');
+end
+
+% --- If res_out not already on dist_all, expand it ---
+if ~isequal(res_out.eval_dist_km(:).', dist_all)
+    res_out = expand_res_dist(res_out, dist_all);
+end
+
+% --- For each file: expand to dist_all, then merge realizations (your logic) ---
+for k = 2:numel(R)
+    R{k} = expand_res_dist(R{k}, dist_all);
+end
+
+
+% Concatenate technique cell arrays along dim=3 (realizations)
+for f = fieldsTech
+    A = res_out.(f);
+    for k = 2:numel(R)
+        B = R{k}.(f);
+        A = cat(3, A, B);
+    end
+    res_out.(f) = A;
+end
+
+% Update num_realiz in settings to match concatenated dim
+dims = size(res_out.ffe); if numel(dims)<3, dims(3)=1; end
+res_out.settings.num_realiz = dims(3);
+end
+
+function res = expand_res_dist(res, dist_all)
+techs = {'ffe','dfe','vnle','mlse','dbt'};
+
+old = res.eval_dist_km(:).';
+new = dist_all(:).';
+[~,loc] = ismember(old, new);              % mapping old -> new positions
+if any(loc==0)
+    error('expand_res_dist: internal: old distances not found in union.');
+end
+
+% sizes from an existing field (prefer ffe)
+Cref = res.ffe;
+sz = size(Cref); sz(end+1:4) = 1;          % ensure 4 dims
+Nch=sz(1); Nrop=sz(2); Nreal=sz(3); Nnew=numel(new);
+
+for t = 1:numel(techs)
+    fn = techs{t};
+    if ~isfield(res,fn) || isempty(res.(fn)), continue; end
+    Cold = res.(fn);
+    sz2 = size(Cold); sz2(end+1:4) = 1;
+
+    Cnew = cell(sz2(1), sz2(2), sz2(3), Nnew);
+    Cnew(:,:,:,loc) = Cold;                % place old distances into new axis
+    res.(fn) = Cnew;
+end
+
+res.eval_dist_km = new;
+end
+
+
+function res_out = drop_empty_realizations(res_in)
+% Removes realizations where ALL entries are empty across ALL techniques
+% (across channels, rop, distances).
+
+    res_out = res_in;
+    fieldsTech = ["ffe","dfe","vnle","mlse","dbt"];
+    
+    % Determine sizes from one field
+    dims = size(res_in.ffe);
+    if numel(dims) < 4, dims(end+1:4) = 1; end
+    Nreal = dims(3);
+    
+    % For each realization, check if there is at least one non-empty result anywhere
+    keep = false(1, Nreal);
+    for r = 1:Nreal
+        hasAny = false;
+        for f = fieldsTech
+            C = res_in.(f); % cell array
+            % Slice all ch,rop,dist for this realization
+            slice = C(:,:,r,:);                % still a cell array
+            hasAny = any(~cellfun(@isempty, slice(:)));
+            if hasAny, break; end
+        end
+        keep(r) = hasAny;
+    end
+    
+    fprintf('drop_empty_realizations: keeping %d/%d realizations (dropping %d)\n', ...
+        sum(keep), Nreal, sum(~keep));
+    
+    % Apply keep mask
+    for f = fieldsTech
+        res_out.(f) = res_out.(f)(:,:,keep,:);
+    end
+    
+    % Update settings
+    res_out.settings.num_realiz = sum(keep);
+end
+