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ML Equalizer works now.

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Not yet perfectly integrated into all the routines
This commit is contained in:
Silas Oettinghaus
2025-11-12 09:24:02 +01:00
parent 39bc8243fc
commit 0080cb2264
44 changed files with 5289 additions and 2868 deletions
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48
Functions/EQ_structures/dsp_runid.m
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@@ -13,14 +13,13 @@ arguments
end
try
% Initialize output structures
output.ffe_package = {};
output.mlse_package = {};
output.vnle_package = {};
output.dbtgt_package = {};
output.dbenc_package = {};
output.mlmlse_package = {};
if options.mode == "load_run_id" || options.append_to_db
% Initialize database connection
@@ -98,9 +97,10 @@ try
use_ffe = 0;
use_dfe = 0;
use_vnle_mlse = 1;
use_vnle_mlse = 0;
use_dbtgt = 0;
use_dbenc = 0;
use_ml_mlse = 1;
addProcessingResultToDatabase = 0;
@@ -130,7 +130,7 @@ try
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0,"adaption_technique","lms");
eq_post =FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption",adaption_method(adaption),"dd_mode",use_dd_mode);
eq_post = FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption",adaption_method(adaption),"dd_mode",use_dd_mode);
% Duobinary signaling (db encoded)
mlse_db_enc = MLSE("DIR", [1,1], "duobinary_output", 0, "M", M, "trellis_states", PAMmapper(M,0).levels);
eq_db_enc = EQ("Ne", ffe_order, "Nb", dfe_order, "training_length", len_tr, ...
@@ -233,30 +233,25 @@ try
database.addProcessingResult(run_id, ffe_results.metrics, ffe_results.config);
end
% pf_ncoeffs = 2;
% eq_ = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
% pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
% % mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
% mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
%
% [ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
% "precode_mode", duob_mode,...
% 'showAnalysis', 0, ...
% "postFFE", [],...
% "eth_style_symbol_mapping", 0);
%
% ffe_results.metrics.print;
% mlse_results.metrics.print;
%
% output.mlse_package{r} = mlse_results;
% output.vnle_package{r} = ffe_results;
%
% if options.append_to_db
% database.addProcessingResult(run_id, mlse_results.metrics, mlse_results.config);
% database.addProcessingResult(run_id, ffe_results.metrics, ffe_results.config);
% end
end
if use_ml_mlse
%ML-based MLSE (L=2)
mu_ml = 0.01; training_epochs = 250;
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, ...
"traceback_depth",128,"L",2,"delta",4,"adaptive_mu",0);
[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Scpe_sig, Symbols, Tx_bits,"precode_mode",duob_mode);
output.mlmlse_package{r} = ml_mlse_results;
if options.append_to_db
database.addProcessingResult(run_id, ml_mlse_results.metrics, ml_mlse_results.config);
end
end
if use_dbtgt
@@ -292,6 +287,7 @@ try
if duob_mode == db_mode.db_encoded
mlse_db_enc = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
db_results = duobinary_signaling(eq_db_enc, mlse_db_enc, M, Scpe_sig, Symbols, Tx_bits, "precode_mode",duob_mode, "showAnalysis",0,"postFFE",[]);
output.dbenc_package{r} = db_results;
if options.append_to_db