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FFE now has a automatic optimization of the mu-parameters -> enables the fast comparison of LMS, NLMS and RLS and also good for very long filters (1000+ taps) which might be unstable else.

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Minor changes here and there
This commit is contained in:
Silas Oettinghaus
2026-05-12 10:40:03 +02:00
parent 56b48095db
commit f883071dec
13 changed files with 524 additions and 65 deletions
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96
Classes/04_DSP/Equalizer/FFE.m
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@@ -15,7 +15,7 @@ classdef FFE < handle
e e
e_tr e_tr
error error
debug_struct
len_tr len_tr
mu_tr mu_tr
@@ -31,6 +31,13 @@ classdef FFE < handle
constellation % symbol constellation constellation % symbol constellation
decide %wether to return the (hard) decisions or the result after FFE (soft) decide %wether to return the (hard) decisions or the result after FFE (soft)
save_debug = 0;
debug_struct
optmize_mus = 0;
mu_optimization
mu_optimization_iter = 0;
end end
methods methods
@@ -51,6 +58,9 @@ classdef FFE < handle
options.decide = false; options.decide = false;
options.save_debug = 0;
options.optmize_mus = 0;
end end
fn = fieldnames(options); fn = fieldnames(options);
@@ -75,6 +85,12 @@ classdef FFE < handle
delta = 0.05; delta = 0.05;
obj.P = (1/delta) * eye(obj.order); obj.P = (1/delta) * eye(obj.order);
if obj.optmize_mus
obj.optimizeMus(X.signal,D.signal);
obj.e = zeros(obj.order,1);
obj.P = (1/delta) * eye(obj.order);
end
% Training Mode % Training Mode
training = 1; training = 1;
showviz = 0; showviz = 0;
@@ -131,7 +147,6 @@ classdef FFE < handle
mask = ones(obj.order,1); mask = ones(obj.order,1);
maincursor_pos=ceil(length(obj.e)/2); maincursor_pos=ceil(length(obj.e)/2);
always_ideal_decision = 0; always_ideal_decision = 0;
save_debug = 0;
grad =0; grad =0;
weight = 0; weight = 0;
update = 0; update = 0;
@@ -205,21 +220,88 @@ classdef FFE < handle
end end
if save_debug if obj.save_debug
obj.debug_struct.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error obj.debug_struct.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
if training if training
obj.debug_struct.error_tr(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error obj.debug_struct.error_tr(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
obj.debug_struct.update_tr(epoch,symbol) = update.'*update ./ rms(obj.e); obj.debug_struct.update_tr(epoch,symbol) = update.'*update ./ rms(obj.e);
else
% if symbol == length(d)
% M = numel(unique(d));
% bits_ref = PAMmapper(M,0).demap(d);
% bits_eq = PAMmapper(M,0).demap(d_hat);
% [~,~,obj.debug_struct.ber(epoch)] = calc_ber(bits_eq,bits_ref);
% end
end end
obj.debug_struct.main_cursor(epoch,symbol) = abs(obj.e(maincursor_pos));
obj.debug_struct.mu_nlms(epoch,symbol) = weight; % obj.debug_struct.main_cursor(epoch,symbol) = abs(obj.e(maincursor_pos));
obj.debug_struct.update_gradient(epoch,symbol) = grad.'*grad; % obj.debug_struct.mu_nlms(epoch,symbol) = weight;
obj.debug_struct.update(epoch,symbol) = update.'*update ./ rms(obj.e); % obj.debug_struct.update_gradient(epoch,symbol) = grad.'*grad;
% obj.debug_struct.update(epoch,symbol) = update.'*update ./ rms(obj.e);
end end
end end
end end
end end
function optimizeMus(obj,x,d)
switch obj.adaption_technique
case adaption_method.lms
mu_range = [1e-5, 1e-2];
case adaption_method.nlms
mu_range = [1e-3, 5e-1];
case adaption_method.rls
mu_range = [0.98, 0.99999];
end
mu_tr_var = optimizableVariable("mu_tr",mu_range,"Transform","log");
vars = mu_tr_var;
if obj.dd_mode
vars = [vars, optimizableVariable("mu_dd",mu_range,"Transform","log")];
end
obj.mu_optimization_iter = 0;
obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x,d),vars, ...
"MaxObjectiveEvaluations",10, ...
"AcquisitionFunctionName","expected-improvement-plus", ...
"IsObjectiveDeterministic",false, ...
"Verbose",0, ...
"PlotFcn",[]);
obj.mu_tr = obj.mu_optimization.XAtMinObjective.mu_tr;
if obj.dd_mode
obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
end
fprintf("\nFFE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, metric=%9.3e\n", ...
obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective);
end
function objective = muObjective(obj,params,x,d)
old_debug = obj.save_debug;
obj.save_debug = 1;
obj.e = zeros(obj.order,1);
obj.P = (1/0.05) * eye(obj.order);
obj.debug_struct = struct();
obj.equalize(x,d,params.mu_tr,obj.epochs_tr,obj.len_tr,1,0);
if obj.dd_mode
obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),0,0);
objective = mean(obj.debug_struct.error(end,:),"omitnan");
else
objective = mean(obj.debug_struct.error_tr(end,:),"omitnan");
end
if ~isfinite(objective)
objective = inf;
end
obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
if obj.dd_mode
fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, metric=%9.3e", ...
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,objective);
else
fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, metric=%9.3e", ...
obj.mu_optimization_iter,params.mu_tr,objective);
end
obj.save_debug = old_debug;
end
end end
end end

2
Classes/DataBaseHandler/DBHandler.m
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@@ -54,7 +54,7 @@ classdef DBHandler < handle
options.password, ... % Password (or getSecret) options.password, ... % Password (or getSecret)
"Vendor", "MySQL", ... "Vendor", "MySQL", ...
"Server", options.server, ... "Server", options.server, ...
"PortNumber", 3306, ... "PortNumber", options.port, ...
"JDBCDriverLocation", "C:\Users\Silas\Documents\mysql-connector-j-9.3.0\mysql-connector-j-9.3.0.jar"); "JDBCDriverLocation", "C:\Users\Silas\Documents\mysql-connector-j-9.3.0\mysql-connector-j-9.3.0.jar");
end end

8
Functions/EQ_structures/dsp_runid.m
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@@ -7,6 +7,10 @@ arguments
options.parameters = struct(); options.parameters = struct();
options.database_type options.database_type
options.dataBase options.dataBase
options.server = "134.245.243.254";
options.port = 3306;
options.user = "silas";
options.password = "silas";
options.load_file_path = struct(); options.load_file_path = struct();
options.storage_path options.storage_path
options.mode options.mode
@@ -18,7 +22,8 @@ try
if options.mode == "load_run_id" || options.append_to_db if options.mode == "load_run_id" || options.append_to_db
database = DBHandler("dataBase", [options.dataBase], "type", options.database_type, ... database = DBHandler("dataBase", [options.dataBase], "type", options.database_type, ...
'user', "silas", 'password', "silas", 'server', "134.245.243.254"); "user", options.user, "password", options.password, ...
"server", options.server, "port", options.port);
end end
if options.mode == "load_run_id" if options.mode == "load_run_id"
@@ -68,7 +73,6 @@ function output = initializeOutput()
end end
function dspInput = loadDspInputFromFiles(run_id, options) function dspInput = loadDspInputFromFiles(run_id, options)
%#ok<INUSD>
Tx_bits = load(options.load_file_path.tx_bits_path); Tx_bits = load(options.load_file_path.tx_bits_path);
Symbols = load(options.load_file_path.tx_symbols_path); Symbols = load(options.load_file_path.tx_symbols_path);
Scpe_sig_raw = load(options.load_file_path.rx_raw_path); Scpe_sig_raw = load(options.load_file_path.rx_raw_path);

3
Functions/EQ_structures/ffe.m
View File

@@ -82,6 +82,9 @@ try
eq_.b2 = []; eq_.b2 = [];
eq_.b3 = []; eq_.b3 = [];
end end
try
eq_.mu_optimization = [];
end
ffe_results.config = Equalizerstruct(); ffe_results.config = Equalizerstruct();
ffe_results.config.eq = jsonencode(eq_); ffe_results.config.eq = jsonencode(eq_);

16
Functions/EQ_structures/vnle_postfilter_mlse.m
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@@ -274,32 +274,32 @@ showEQNoisePSD(eq_noise, "fignum", 338, "displayname", 'Residual Noise after VNL
for t = 1:4 for t = 1:4
pf_.ncoeff = t; pf_.ncoeff = t;
[~,~] = pf_.process(eq_signal_sd, eq_noise); [~,~] = pf_.process(eq_signal_sd, eq_noise);
showEQNoisePSD(eq_noise, "fignum", 3388, "displayname", 'Residual Noise after VNLE', 'postfilter_taps', pf_.coefficients); showEQNoisePSD(eq_noise, "fignum", 339, "displayname", 'Residual Noise after VNLE', 'postfilter_taps', pf_.coefficients);
end end
tx_symbols.spectrum("displayname",'Equalized Signal','fignum',1234,'normalizeTo0dB',1); tx_symbols.spectrum("displayname",'Equalized Signal','fignum',340,'normalizeTo0dB',1);
if ~isempty(postFFE) if ~isempty(postFFE)
showEQcoefficients('n1', postFFE.e, "displayname", 'Coefficients', 'fignum', 338); showEQcoefficients('n1', postFFE.e, "displayname", 'Coefficients', 'fignum', 341);
end end
% showEQcoefficients('n1', eq_.e1,'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); showEQfilter(eq_.e, eq_signal_sd.fs.*2);
figure(340); clf; figure(340); clf;
eq_signal_sd.eye(eq_signal_sd.fs, M, "fignum", 340); eq_signal_sd.eye(eq_signal_sd.fs, M, "fignum", 342);
figure(341); clf; figure(341); clf;
showLevelHistogram(eq_signal_sd, tx_symbols, "fignum", 341); showLevelHistogram(eq_signal_sd, tx_symbols, "fignum", 343);
warning off warning off
showLevelScatter(eq_signal_sd, tx_symbols, "fignum", 400); showLevelScatter(eq_signal_sd, tx_symbols, "fignum", 344);
% showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 401); % showLevelScatter(rx_signal.resample("fs_out", tx_symbols.fs), tx_symbols, "fignum", 401);
drawnow; drawnow;
warning on warning on
whitened_noise.spectrum("displayname",'after postfilter','fignum',342); whitened_noise.spectrum("displayname",'after postfilter','fignum',345);
eq_noise.spectrum("displayname",'before postfilter','fignum',342); eq_noise.spectrum("displayname",'before postfilter','fignum',345);
end end

23
Functions/Job_Processing/submitJobs.m
View File

@@ -36,17 +36,10 @@ for r = 1:nRunIds
for k = 1:nJobsPerRunId for k = 1:nJobsPerRunId
jobCounter = jobCounter + 1; jobCounter = jobCounter + 1;
optionalVars = buildOptionalVars(k, submit_options.wh); optionalVars = buildOptionalVars(k, submit_options.wh);
jobOptions = dsp_options;
jobOptions.parameters = optionalVars;
jobs(jobCounter).args = { ... jobs(jobCounter).args = [{run_ids(r)}, structToNameValue(jobOptions)];
run_ids(r), ...
"database_type", dsp_options.database_type, ...
"dataBase", dsp_options.dataBase, ...
"append_to_db", dsp_options.append_to_db, ...
"load_file_path", dsp_options.load_file_path, ...
"max_occurences", dsp_options.max_occurences, ...
"storage_path", dsp_options.storage_path, ...
"mode", dsp_options.mode, ...
"parameters", optionalVars};
jobs(jobCounter).label = sprintf('RunID %d, Job %d', run_ids(r), k); jobs(jobCounter).label = sprintf('RunID %d, Job %d', run_ids(r), k);
jobs(jobCounter).meta.runIndex = r; jobs(jobCounter).meta.runIndex = r;
jobs(jobCounter).meta.jobIndex = k; jobs(jobCounter).meta.jobIndex = k;
@@ -91,6 +84,16 @@ end
end end
end end
function nameValue = structToNameValue(options)
names = fieldnames(options);
nameValue = cell(1, 2*numel(names));
for i = 1:numel(names)
nameValue{2*i - 1} = string(names{i});
nameValue{2*i} = options.(names{i});
end
end
function storeResult(val, job, ~) function storeResult(val, job, ~)
if ~isempty(wh) if ~isempty(wh)
jobIndex = job.meta.jobIndex; jobIndex = job.meta.jobIndex;

342
Theory/Signal_Processing/EQ_adaption.m Normal file
View File

@@ -0,0 +1,342 @@
% minimal example IM/DD
M = 4;
fsym = 180e9;
apply_pulsef = 1;
fdac = 256e9;
fadc = 256e9;
random_key = 1;
rcalpha = 0.05;
kover = 16;
duob_mode = db_mode.no_db;
vbias_rel = 0.5;
u_pi = 3;
vbias = -vbias_rel*u_pi;
laser_wavelength = 1293;
laser_linewidth = 0;
tx_bw_nyquist = 0.8;
% Channel
link_length = 1;
% RX
rop = -7;
rx_bw_nyquist = 0.8;
vnle_order1 = 50;
vnle_order2 = 7;
vnle_order3 = 7;
vnle_order=[vnle_order1,vnle_order2,vnle_order3];
dfe_order = [0 0 0];
pf_ncoeffs = 1;
alpha = 0;
len_tr = 4096*2;
mu_ffe1 = 0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dc = 0.005;
% mu_dc = 0;
mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
mu_dfe = 0.0004;
n_stat_runs = 1;
base_random_key = random_key;
output.eq_stats = struct();
for stat_run = 1:n_stat_runs
random_key = base_random_key + stat_run - 1;
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
"fsym",fsym,"M",M,"order",16,"useprbs",0,...
"fs_out",fdac,...
"applyclipping",0,"clipfactor",1.5,...
"applypulseform",apply_pulsef,"pulseformer",Pform,...
"randkey",random_key,...
'duobinary_mode',duob_mode,...
"mrds_code",0,"mrds_blocklength",512).process();
%%%%% 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',1,'normalizeTo0dB',1);
xlim([0,130]);
ylim([-30,5]);
% El_sig = El_sig.setPower(0,"dBm");
%%%%% Electrical Driver Amplifier %%%%%%
% El_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",3).process(El_sig);
El_sig = El_sig.normalize("mode","oneone");
scaling = 0.6*(u_pi/2-abs(vbias-u_pi/2));
El_sig = El_sig .* scaling;
%%%%% MODULATE E/O CONVERSION %%%%%%
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1,"alpha",alpha).process(El_sig);
Opt_sig.spectrum("displayname",'Opt Spectrum','fignum',10,'normalizeTo0dB',1);
% Opt_sig.eye(fsym,M,"displayname",'eye adter modulator','fignum',2026);
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
%%%%%% ROP %%%%%%
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
%%%%%% PD Square Law %%%%%%
Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
rx_bwl = 80e9;
Rx_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(Rx_sig);
% %%%%%% Low-pass Scope %%%%%%
Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
%%%%%% Scope %%%%%%
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);
%%
% 1) matched filter
% pulse is symmetric, hence we can use pulsef firectly 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",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.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 = Scpe_sig - mean(Scpe_sig.signal);
Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
%% -------------------- FFE --------------------
ffe_options = struct( ...
"epochs_tr", 3, ...
"epochs_dd", 3, ...
"len_tr", 4096, ...
"order", 40, ...
"sps", 2, ...
"decide", 0, ...
"adaption", adaption_method.rls, ...
"dd_mode", 1);
eq_nlms = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
"len_tr",ffe_options.len_tr,"mu_dd",0.001,"mu_tr",0.001, ...
"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1,"optmize_mus",1);
output.ffe_results = ffe(eq_nlms,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
"eth_style_symbol_mapping",0);
output.ffe_results.metrics.print("description","FFE");
%%
% -------------------- FFE --------------------
%%
for i = 3:-1:1
mu_adaptation = adaption_method(i);
switch mu_adaptation
case adaption_method.lms
mu_search_range = [1e-5, 1e-2];
case adaption_method.nlms
mu_search_range = [1e-3, 5e-1];
case adaption_method.rls
mu_search_range = [0.98, 0.99999]; % here mu is lambda
end
mu_max_evaluations = 10;
ffe_options = struct( ...
"epochs_tr", 3, ...
"epochs_dd", 2, ...
"len_tr", 4096*2, ...
"order", 20, ...
"sps", 2, ...
"decide", 0, ...
"adaption", mu_adaptation, ...
"dd_mode", 1);
method_name = char(mu_adaptation);
rng(random_key, "twister");
mu_dd_var = optimizableVariable("mu_dd", mu_search_range, "Transform", "log");
mu_tr_var = optimizableVariable("mu_tr", mu_search_range, "Transform", "log");
global ffe_mu_trace
ffe_mu_trace = table('Size',[0 5], ...
'VariableTypes',{'double','double','double','double','double'}, ...
'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'});
objective_fn = @(params) ffe_mu_objective(params, ffe_options, M, Scpe_sig, Symbols, Tx_bits, duob_mode);
output.eq_stats.(method_name).mu_optimization{stat_run} = bayesopt(objective_fn, [mu_dd_var, mu_tr_var], ...
"MaxObjectiveEvaluations", mu_max_evaluations, ...
"AcquisitionFunctionName", "expected-improvement-plus", ...
"IsObjectiveDeterministic", false, ...
"Verbose", 1, ...
"PlotFcn", []);
output.eq_stats.(method_name).mu_observed(stat_run,:) = output.eq_stats.(method_name).mu_optimization{stat_run}.XAtMinObjective;
output.eq_stats.(method_name).mu_estimated(stat_run,:) = output.eq_stats.(method_name).mu_optimization{stat_run}.XAtMinEstimatedObjective;
output.eq_stats.(method_name).optimization_trace{stat_run} = ffe_mu_trace;
best_mu_dd = output.eq_stats.(method_name).mu_observed.mu_dd(stat_run);
best_mu_tr = output.eq_stats.(method_name).mu_observed.mu_tr(stat_run);
output.eq_stats.(method_name).mu_used(stat_run,:) = table(best_mu_dd,best_mu_tr, ...
'VariableNames',{'mu_dd','mu_tr'});
eq_nlms = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
"len_tr",ffe_options.len_tr,"mu_dd",best_mu_dd,"mu_tr",best_mu_tr, ...
"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1);
output.ffe_results = ffe(eq_nlms,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
"eth_style_symbol_mapping",0);
output.ffe_results.metrics.print("description",sprintf('%s',mu_adaptation));
output.eq_stats.(method_name).error_tr_first_epoch(stat_run,:) = eq_nlms.debug_struct.error_tr(1,:);
% figure(6)
% hold on
% for epoch = 1:ffe_options.epochs_tr
% plot(1:size(eq_nlms.debug_struct.error_tr,2),movmean(eq_nlms.debug_struct.error_tr(epoch,:),10));
% end
end
end
%%
figure(5);
clf;
hold on;
method_names = fieldnames(output.eq_stats);
cols = cbrewer2('Paired',6);
cols = cols(2:2:end,:);
for i = 1:numel(method_names)
method_name = method_names{i};
first_epoch = output.eq_stats.(method_name).error_tr_first_epoch;
for j = 1:size(first_epoch,1)
plot(movmean(output.eq_stats.(method_name).error_tr_first_epoch(j,:),10), ...
"DisplayName",method_name,"Color",[0.7,0.7,0.7],"LineWidth",0.1,'HandleVisibility','off');
end
end
for i = 1:numel(method_names)
method_name = method_names{i};
first_epoch = output.eq_stats.(method_name).error_tr_first_epoch;
output.eq_stats.(method_name).error_tr_first_epoch_mean = mean(first_epoch,1,"omitnan");
output.eq_stats.(method_name).error_tr_first_epoch_std = std(first_epoch,0,1,"omitnan");
plot(movmean(output.eq_stats.(method_name).error_tr_first_epoch_mean,50), ...
"DisplayName",method_name,"Color",cols(i,:));
end
grid on;
xlabel("Training symbol index","Interpreter","tex");
ylabel("Mean squared error, first epoch","Interpreter","tex");
legend("Location","best","Interpreter","tex");
figure(6);
clf;
tiledlayout(numel(method_names),3,"TileSpacing","compact","Padding","compact");
metric_names = {'BER','SNR','mse_last_epoch'};
metric_labels = {'BER','SNR','mean abs(error)^2, last epoch'};
for i = 1:numel(method_names)
method_name = method_names{i};
trace = vertcat(output.eq_stats.(method_name).optimization_trace{:});
observed_mu = output.eq_stats.(method_name).mu_observed;
estimated_mu = output.eq_stats.(method_name).mu_estimated;
for metric_idx = 1:numel(metric_names)
nexttile;
metric = trace.(metric_names{metric_idx});
if metric_idx ~= 2
metric = log10(max(metric,realmin));
end
scatter(trace.mu_tr,trace.mu_dd,25,metric,"filled");
hold on;
plot(observed_mu.mu_tr,observed_mu.mu_dd,"kp","MarkerSize",11, ...
"MarkerFaceColor","y","LineWidth",1.2,"DisplayName","Observed best");
plot(estimated_mu.mu_tr,estimated_mu.mu_dd,"kx","MarkerSize",10, ...
"LineWidth",1.6,"DisplayName","Estimated best");
set(gca,"XScale","log","YScale","log");
grid on;
xlabel('\mu_{tr}',"Interpreter","tex");
ylabel('\mu_{dd}',"Interpreter","tex");
title(sprintf("%s: %s",method_name,metric_labels{metric_idx}),"Interpreter","tex");
cb = colorbar;
if metric_idx ~= 2
cb.Label.String = sprintf("log10(%s)",metric_labels{metric_idx});
else
cb.Label.String = metric_labels{metric_idx};
end
cb.Label.Interpreter = "tex";
legend("Location","best","Interpreter","tex");
end
end
function objective = ffe_mu_objective(params, ffe_options, M, rx_signal, tx_symbols, tx_bits, duob_mode)
global ffe_mu_trace
try
eq_candidate = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
"len_tr",ffe_options.len_tr,"mu_dd",params.mu_dd,"mu_tr",params.mu_tr, ...
"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1);
candidate_results = ffe(eq_candidate, M, rx_signal, tx_symbols, tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
"eth_style_symbol_mapping",0);
objective = candidate_results.metrics.BER;
if ~isfinite(objective)
objective = 0.5;
end
mse_last_epoch = mean(eq_candidate.debug_struct.error_tr(end,:),"omitnan");
ffe_mu_trace = [ffe_mu_trace; table(params.mu_dd,params.mu_tr,objective, ...
candidate_results.metrics.SNR,mse_last_epoch, ...
'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'})];
fprintf("mu_dd=%9.3e, mu_tr=%9.3e -> BER=%9.3e, SNR=%6.2f dB, MSE=%9.3e\n", ...
params.mu_dd, params.mu_tr, objective, candidate_results.metrics.SNR, mse_last_epoch);
catch ME
objective = 0.5;
ffe_mu_trace = [ffe_mu_trace; table(params.mu_dd,params.mu_tr,objective,NaN,NaN, ...
'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'})];
fprintf("mu_dd=%9.3e, mu_tr=%9.3e -> failed: %s\n", ...
params.mu_dd, params.mu_tr, ME.message);
end
end

19
projects/Advanced_DSP_for_400G_IMDD_experiments/Auswertung_JLT/final/FIGURE_Spectra.m
View File

@@ -1,6 +1,17 @@
dsp_options.storage_path = 'Z:\2024\sioe_labor\'; dsp_options.storage_path = 'Z:\2024\sioe_labor\';
dsp_options.max_occurences = 1; dsp_options.max_occurences = 1;
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' ); % database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
dsp_options.database_type = "mysql";
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
dsp_options.server = "192.168.178.192";% "134.245.243.254";
dsp_options.user = "silas";
dsp_options.password = "silas";
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
database = DBHandler("dataBase", [dsp_options.dataBase],...
"type", dsp_options.database_type,...
"server", dsp_options.server,...
"user", dsp_options.user, "password", dsp_options.password);
rates = [420e9]; rates = [420e9];
cols = cbrewer2('BuPu',25); cols = cbrewer2('BuPu',25);
@@ -10,10 +21,10 @@ cols = cbrewer2('Set1',6);
fignum = 200; fignum = 200;
fig=figure(fignum);clf; fig=figure(fignum);clf;
for dbmode = 1%length(rates) for dbmode = 0%length(rates)
if 0 if 1
rcalpha = 0.05; rcalpha = 0.05;
fsym = rates/2; fsym = rates/2;
pulsef = 1; pulsef = 1;
@@ -41,7 +52,7 @@ for dbmode = 1%length(rates)
maxamp = -37; maxamp = -37;
precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs); precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
precomp_path = "C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\precomp"; precomp_path = "W:\labdata\sioe_labor\precomp";
precomp_fn = "lab_high_speed"; precomp_fn = "lab_high_speed";
Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn); Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);

17
projects/Advanced_DSP_for_400G_IMDD_experiments/Auswertung_JLT/final/generate_spectrum_plots.m
View File

@@ -1,7 +1,16 @@
dsp_options.storage_path = 'Z:\2024\sioe_labor\'; dsp_options.storage_path = 'Z:\2024\sioe_labor\';
dsp_options.max_occurences = 1; dsp_options.max_occurences = 1;
database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' ); % database = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
dsp_options.database_type = "mysql";
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
dsp_options.server = "192.168.178.192";% "134.245.243.254";
dsp_options.user = "silas";
dsp_options.password = "silas";
dsp_options.storage_path = 'W:\labdata\sioe_labor\';
database = DBHandler("dataBase", [dsp_options.dataBase],...
"type", dsp_options.database_type,...
"server", dsp_options.server,...
"user", dsp_options.user, "password", dsp_options.password);
rate = 390e9; rate = 390e9;
%% 1 - PAM 4 with preemphasis %% 1 - PAM 4 with preemphasis
@@ -14,7 +23,7 @@ fp.where('Runs', 'wavelength','EQUALS', 1310);
fp.where('Runs', 'db_mode','EQUALS', 0); fp.where('Runs', 'db_mode','EQUALS', 0);
fp.where('Runs', 'rop_attenuation','EQUAL', 0); 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); dataTable = queryRunid(dataTable.run_id, database);
fsym = dataTable.symbolrate; fsym = dataTable.symbolrate;
@@ -46,7 +55,7 @@ fp.where('Runs', 'wavelength','EQUALS', 1310);
fp.where('Runs', 'db_mode','EQUALS', 1); fp.where('Runs', 'db_mode','EQUALS', 1);
fp.where('Runs', 'rop_attenuation','EQUAL', 0); 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); dataTable = queryRunid(dataTable.run_id, database);
fsym = dataTable.symbolrate; fsym = dataTable.symbolrate;

7
projects/Advanced_DSP_for_400G_IMDD_experiments/Auswertung_JLT/run_dsp_from_db.m
View File

@@ -12,11 +12,14 @@ if dsp_options.mode == "load_run_id"
dsp_options.database_type = "mysql"; dsp_options.database_type = "mysql";
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db'; dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
dsp_options.server = "192.168.178.192";% "134.245.243.254";
dsp_options.user = "silas";
dsp_options.password = "silas";
dsp_options.storage_path = 'W:\labdata\sioe_labor\'; dsp_options.storage_path = 'W:\labdata\sioe_labor\';
db = DBHandler("dataBase", [dsp_options.dataBase],... db = DBHandler("dataBase", [dsp_options.dataBase],...
"type", dsp_options.database_type,... "type", dsp_options.database_type,...
"server","192.168.178.192",...% "server","134.245.243.254",... "server", dsp_options.server,...
"user","silas","password","silas"); "user", dsp_options.user, "password", dsp_options.password);
elseif experiment == "mpi_ecoc_2025" elseif experiment == "mpi_ecoc_2025"

24
projects/IMDD_base_system/minimal_example.m
View File

@@ -1,7 +1,7 @@
% minimal example IM/DD % minimal example IM/DD
M = 4; M = 4;
fsym = 112e9; fsym = 180e9;
apply_pulsef = 1; apply_pulsef = 1;
fdac = 256e9; fdac = 256e9;
@@ -63,8 +63,8 @@ Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"al
"mrds_code",0,"mrds_blocklength",512).process(); "mrds_code",0,"mrds_blocklength",512).process();
%%%%% AWG %%%%% AWG
% El_sig = M8199A("kover",kover).process(Digi_sig); El_sig = M8199B("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 = 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',1,'normalizeTo0dB',1); El_sig.spectrum("displayname",'Digi Spectrum','fignum',1,'normalizeTo0dB',1);
xlim([0,130]); xlim([0,130]);
ylim([-30,5]); ylim([-30,5]);
@@ -130,24 +130,24 @@ Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols)); Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
%% %%
if 0
% -------------------- FFE -------------------- % -------------------- FFE --------------------
ffe_order = [50, 0, 0]; ffe_order = [50, 0, 0];
eq_ = EQ("Ne",ffe_order,"Nb",[2,0,0], ... eq_ = EQ("Ne",ffe_order,"Nb",[2,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ... "training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ... "K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",0); "FFEmu",0,"plotfinal",0,"ideal_dfe",0);
% 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); % 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);
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); 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, ... output.ffe_results = ffe(eq_,M,Scpe_sig,Symbols,Tx_bits, ...
"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ... "precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
"eth_style_symbol_mapping",0); "eth_style_symbol_mapping",0);
output.ffe_results.metrics.print("description",'DFE'); output.ffe_results.metrics.print("description",'DFE');
end
%% %%
% -------------------- VNLE + MLSE -------------------- % -------------------- VNLE + MLSE --------------------

6
projects/Messung_Zürich/dsp_all_files.m
View File

@@ -1,5 +1,5 @@
M = 6; M = 4;
if M == 2 if M == 2
file_codes = {'20250221T035221' '20250221T035354' '20250221T035824' '20250221T035931' '20250221T040035' '20250221T040132' '20250221T040226' '20250221T040523' '20250221T040646' '20250221T040723' '20250221T040843' '20250221T041011' '20250221T041101' '20250221T041244'}; file_codes = {'20250221T035221' '20250221T035354' '20250221T035824' '20250221T035931' '20250221T040035' '20250221T040132' '20250221T040226' '20250221T040523' '20250221T040646' '20250221T040723' '20250221T040843' '20250221T041011' '20250221T041101' '20250221T041244'};
elseif M == 4 elseif M == 4
@@ -10,7 +10,7 @@ elseif M ==8
file_codes = {'20250221T004926' '20250221T023534' '20250221T024256' '20250221T024629' '20250221T024929' '20250221T025305' '20250221T025505' '20250221T025856' '20250221T030122' '20250221T030311' '20250221T030513'}; file_codes = {'20250221T004926' '20250221T023534' '20250221T024256' '20250221T024629' '20250221T024929' '20250221T025305' '20250221T025505' '20250221T025856' '20250221T030122' '20250221T030311' '20250221T030513'};
end end
if 0 if 1
uloops = struct; uloops = struct;
uloops.filecode = file_codes; uloops.filecode = file_codes;
uloops.vnle_order1 = [50]; uloops.vnle_order1 = [50];
@@ -19,7 +19,7 @@ if 0
wh = DataStorage(uloops); wh = DataStorage(uloops);
wh.addStorage("ber"); wh.addStorage("ber");
wh = submit_handle(@dsp_ief_file,wh,"parallel",1); wh = submit_handle(@dsp_ief_file,wh,"parallel",0);
end end
if 0 if 0

4
projects/Messung_Zürich/dsp_ief_file.m
View File

@@ -36,9 +36,11 @@ function [results] = dsp_ief_file(varargin)
end end
pathToMeasurement = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\20250221"; pathToMeasurement = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\20250221";
pathToMeasurement = "W:\labdata\Plasmonic O-band Modulator (ETH Zurich)\Data\20250221";
filename = findFileByCode(pathToMeasurement, filecode{1}); filename = findFileByCode(pathToMeasurement, filecode{1});
pathToTimingRecov = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\TR_ZIP"; pathToTimingRecov = "C:\Users\Silas\Documents\MATLAB\Datensätze\IEF_Polariton_2025\36_IMDD_Kiel\Data\TR_ZIP";
pathToTimingRecov = "W:\labdata\Plasmonic O-band Modulator (ETH Zurich)\Data\TR_ZIP";
filename_2 = findFileByCode(pathToTimingRecov, filecode{1}); filename_2 = findFileByCode(pathToTimingRecov, filecode{1});
% Extract parameters from the filename using an updated regex % Extract parameters from the filename using an updated regex
@@ -166,7 +168,7 @@ function [results] = dsp_ief_file(varargin)
%%%%% VNLE + PF + MLSE %%%% %%%%% VNLE + PF + MLSE %%%%
if 1 if 1
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",1001,"sps",1,"decide",0); eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",1.933e-04,"order",1001,"sps",1,"decide",0,"optmize_mus",1,"dd_mode",0);
pf_ = Postfilter("ncoeff",1,"useBurg",1); pf_ = Postfilter("ncoeff",1,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels); mlse_ = MLSE("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels);
mlse_ = MLSE_viterbi("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels); mlse_ = MLSE_viterbi("duobinary_output",0,'M',config.M,'trellis_states',PAMmapper(config.M,0).levels);