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Few more scripts to evaluate new ML-MLSE Equalizer - which is not better!!! :-(

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silas (home)
2025-11-03 08:17:39 +01:00
parent 09f345aa91
commit 39bc8243fc
7 changed files with 443 additions and 319 deletions
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109
projects/ML_based_MLSE/rate_evaluation.m Normal file
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ber_ffe = [];
ber_mlse = [];
ber_dbtgt = [];
ber_ml = [];
mlse = 1;
dbtgt = 1;
baudrates = [136:8:224].*1e9;
parfor i = 1:length(baudrates)
rop = -8;
M = 4;
[Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1] = standard_link_model("M",M,"fsym",baudrates(i),"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",1,"apply_pulsef",0);
% [Rx_sig_2sps_v2, Symbols_v2, Tx_bits_v2] = standard_link_model("M",M,"fsym",200e9,"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",2);
% [Rx_sig_2sps_v3, Symbols_v3, Tx_bits_v3] = standard_link_model("M",M,"fsym",200e9,"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",3);
%% FFE + MLSE
if mlse
pf_ncoeffs = 1;
ffe_order = [50, 0, 0];
mu_ffe = [0.0001, 0.0008, 0.001];
mu_dfe = 0.0004;
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^13,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0,'DIR',pf_.coefficients);
[ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
"precode_mode", duob_mode,...
'showAnalysis', 0, ...
"postFFE", [],...
"eth_style_symbol_mapping", 0);
ber_ffe(i) = ffe_results.metrics.BER;
ber_mlse(i) = mlse_results.metrics.BER;
end
%% FFE DB tgt. + MLSE
if dbtgt
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',3);
ffe_order = [50, 0, 0];
mu_ffe = [0.0001, 0.0008, 0.001];
mu_dfe = 0.0004;
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^13,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
dbt_results = duobinary_target(eq_, mlse_db_, M, Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
"precode_mode", duob_mode, ...
'showAnalysis', 0,...
"postFFE", []);
ber_dbtgt(i) = dbt_results.metrics.BER;
end
%%
mu_lms = 0.0005;
pf_ncoeffs = 2;
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"dd_mode",1,"adaption_technique","lms");
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
% FFE
[y_ffe, ffe_noise] = eq_.process(Rx_sig_2sps_v1, Symbols_v1);
% Postfilter
[y_white,whitened_noise] = pf_.process(y_ffe, ffe_noise);
%% RUN ML-Based MLSE
mu_lms = 0.15;
ml_mlse_equalizer = ML_MLSE("epochs_tr",30,"epochs_dd",1,"len_tr",2^15,...
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",5,"sps",1,...
"traceback_depth",128,"L",3,"delta",0);
[y_ml_mlse,~] = ml_mlse_equalizer.process(y_white,Symbols_v1);
ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
[~, errors, ber_ml(i), errpos] = calc_ber(ml_mlse_bits.signal, Tx_bits_v1.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
fprintf('ML MLSE BER: %.2e \n',ber_ml(i));
% figure(11);hold on
% plot(1:numel(ml_mlse_equalizer.ber),ml_mlse_equalizer.ber);
% beautifyBERplot;
% xlim([1,numel(ml_mlse_equalizer.ber)])
end
%%
figure(6); hold on;
if mlse
plot(baudrates,ber_ffe,'DisplayName','FFE');
plot(baudrates,ber_mlse,'DisplayName','MLSE');
end
if dbtgt
plot(baudrates,ber_dbtgt,'DisplayName','DB tgt');
end
plot(baudrates,ber_ml,'DisplayName','ML-MLSE');
beautifyBERplot;
legend