ML Equalizer works now.
Not yet perfectly integrated into all the routines
This commit is contained in:
Silas Oettinghaus
@@ -1,95 +1,139 @@
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clear; clc;
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ber_ffe = [];
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ber_mlse = [];
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ber_dbtgt = [];
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ber_ml = [];
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M = 4;
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randkey = 1;
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duob_mode = db_mode.no_db;
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mlse = 1;
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dbtgt = 1;
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rops = linspace(-15,-5,12);
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parfor i = 1:length(rops)
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rop = rops(i);
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M = 4;
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[Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1] = standard_link_model("M",M,"fsym",224e9,"rop",rop,"laser_linewidth",1300,"link_length_m",0,"random_key",1);
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% [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);
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% [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);
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%% FFE + MLSE
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if mlse
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pf_ncoeffs = 1;
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ffe_order = [50, 0, 0];
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mu_ffe = [0.0001, 0.0008, 0.001];
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mu_dfe = 0.0004;
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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);
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pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
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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);
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[ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
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"precode_mode", duob_mode,...
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'showAnalysis', 0, ...
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"postFFE", [],...
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"eth_style_symbol_mapping", 0);
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ber_ffe(i) = ffe_results.metrics.BER;
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ber_mlse(i) = mlse_results.metrics.BER;
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end
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baudrates = 180e9:2e9:220e9; % outer loop
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rops = linspace(-10,0,12); % inner sweep
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FEC_thr = 3.8e-3; % BER target
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% --- allocate results
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reqROP_FFE = nan(size(baudrates));
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reqROP_MLSE = nan(size(baudrates));
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reqROP_DBTGT = nan(size(baudrates));
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reqROP_ML_MLSE2 = nan(size(baudrates));
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reqROP_ML_MLSE3 = nan(size(baudrates));
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%% ====================== OUTER LOOP ======================
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for b = 1:numel(baudrates)
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baudrate = baudrates(b);
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fprintf('\n=== %.0f GBd ===\n', baudrate/1e9);
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ber_ffe = nan(size(rops));
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ber_mlse = nan(size(rops));
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ber_dbtgt = nan(size(rops));
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ber_ml2 = nan(size(rops));
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ber_ml3 = nan(size(rops));
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%% -------- inner ROP loop --------
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for i = 1:length(rops)
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rop = rops(i);
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[Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1] = standard_link_model( ...
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"M",M,"fsym",baudrate,"rop",rop,"laser_linewidth",1310, ...
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"link_length_m",0,"random_key",1);
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%% FFE DB tgt. + MLSE
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if dbtgt
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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);
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ffe_order = [50, 0, 0];
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mu_ffe = [0.0001, 0.0008, 0.001];
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mu_dfe = 0.0004;
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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);
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dbt_results = duobinary_target(eq_, mlse_db_, M, Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
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"precode_mode", duob_mode, ...
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'showAnalysis', 0,...
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"postFFE", []);
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ber_dbtgt(i) = dbt_results.metrics.BER;
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end
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%% RUN ML-Based MLSE
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mu_lms = 0.15;
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ml_mlse_equalizer = ML_MLSE("epochs_tr",30,"epochs_dd",1,"len_tr",2^14,...
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"mu_dd",mu_lms,"mu_tr",mu_lms,"order",4,"sps",2,...
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"traceback_depth",128,"L",2,"delta",0);
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[y_ml_mlse,~] = ml_mlse_equalizer.process(Rx_sig_2sps_v1,Symbols_v1);
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ml_mlse_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse);
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[~, errors, ber_ml(i), errpos] = calc_ber(ml_mlse_bits.signal, Tx_bits_v1.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
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fprintf('ML MLSE BER: %.2e \n',ber_ml(i));
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% figure(11);hold on
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% plot(1:numel(ml_mlse_equalizer.ber),ml_mlse_equalizer.ber);
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% beautifyBERplot;
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% xlim([1,numel(ml_mlse_equalizer.ber)])
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%% FFE + MLSE
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if mlse
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pf_ncoeffs = 1;
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ffe_order = [50, 0, 0];
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mu_ffe = [0.0001, 0.0008, 0.001];
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mu_dfe = 0.0004;
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eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^13, ...
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"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005, ...
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"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0, ...
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"plotfinal",0,"ideal_dfe",1);
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pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
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mlse_ = MLSE("duobinary_output",0,'M',M, ...
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'trellis_states',PAMmapper(M,0).levels,'scale_mode',2, ...
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'trellis_exclusion',0,'trellis_state_mode',2,'debug',0, ...
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'DIR',pf_.coefficients);
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[ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, ...
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Rx_sig_2sps_v1, Symbols_v1, Tx_bits_v1, ...
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"precode_mode", duob_mode,'showAnalysis', 0, "postFFE", [], ...
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"eth_style_symbol_mapping", 0);
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ber_ffe(i) = ffe_results.metrics.BER;
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ber_mlse(i) = mlse_results.metrics.BER;
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end
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%% FFE + duobinary target MLSE
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if dbtgt
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mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M, ...
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"trellis_states",PAMmapper(M,0).levels,'scale_mode',2, ...
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'trellis_exclusion',0,'trellis_state_mode',3);
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ffe_order = [50, 0, 0];
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mu_ffe = [0.0001, 0.0008, 0.001];
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mu_dfe = 0.0004;
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eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",2^13, ...
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"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005, ...
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"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0, ...
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"plotfinal",0,"ideal_dfe",1);
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dbt_results = duobinary_target(eq_, mlse_db_, M, Rx_sig_2sps_v1, ...
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Symbols_v1, Tx_bits_v1, "precode_mode", duob_mode, ...
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'showAnalysis', 0, "postFFE", []);
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ber_dbtgt(i) = dbt_results.metrics.BER;
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end
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%% ML-based MLSE (L=2)
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mu_ml = 0.1; training_epochs = 100;
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ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
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"len_tr",2^16,"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
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"traceback_depth",128,"L",2,"delta",4,"adaptive_mu",0);
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[y_ml_mlse,y_ref] = ml_mlse_equalizer.process(Rx_sig_2sps_v1,Symbols_v1);
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ref_bits = PAMmapper(M,0).demap(y_ref);
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ml_bits = PAMmapper(M,0).demap(y_ml_mlse);
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[~,~,ber_ml2(i)] = calc_ber(ml_bits.signal, ref_bits.signal, ...
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"skip_front",10,"skip_end",10);
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%% ML-based MLSE (L=3)
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ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
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"len_tr",2^16,"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
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"traceback_depth",128,"L",3,"delta",4,"adaptive_mu",0);
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[y_ml_mlse,y_ref] = ml_mlse_equalizer.process(Rx_sig_2sps_v1,Symbols_v1);
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ref_bits = PAMmapper(M,0).demap(y_ref);
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ml_bits = PAMmapper(M,0).demap(y_ml_mlse);
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[~,~,ber_ml3(i)] = calc_ber(ml_bits.signal, ref_bits.signal, ...
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"skip_front",10,"skip_end",10);
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end % ROP loop
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%% --- find required ROP (FEC crossing)
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reqROP_FFE(b) = interp_fec_cross(rops, ber_ffe, FEC_thr);
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reqROP_MLSE(b) = interp_fec_cross(rops, ber_mlse, FEC_thr);
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reqROP_DBTGT(b) = interp_fec_cross(rops, ber_dbtgt, FEC_thr);
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reqROP_ML_MLSE2(b) = interp_fec_cross(rops, ber_ml2, FEC_thr);
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reqROP_ML_MLSE3(b) = interp_fec_cross(rops, ber_ml3, FEC_thr);
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% --- diagnostic
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fprintf('Baud %.0f GBd: FFE %.1f, MLSE %.1f, DB %.1f, ML2 %.1f, ML3 %.1f\n', ...
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baudrate/1e9, reqROP_FFE(b), reqROP_MLSE(b), reqROP_DBTGT(b), ...
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reqROP_ML_MLSE2(b), reqROP_ML_MLSE3(b));
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end
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%%
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figure(3); hold on;
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if mlse
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plot(rops,ber_ffe,'DisplayName','FFE');
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plot(rops,ber_mlse,'DisplayName','MLSE');
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end
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if dbtgt
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plot(rops,ber_dbtgt,'DisplayName','DB tgt');
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end
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plot(rops,ber_ml,'DisplayName','ML-MLSE');
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beautifyBERplot;
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legend
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%% ====================== PLOT REQUIRED ROP ======================
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cols = cbrewer2('Set1',8);
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colFFE = cols(1,:);
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colMLSE = cols(2,:);
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colDBTGT = cols(4,:);
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colML_MLSE = cols(3,:);
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figure(); hold on
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plot(baudrates/1e9, reqROP_FFE, '-o','Color',colFFE, 'DisplayName','FFE');
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plot(baudrates/1e9, reqROP_MLSE, '-s','Color',colMLSE, 'DisplayName','FFE+PF+MLSE');
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plot(baudrates/1e9, reqROP_DBTGT, '--^','Color',colDBTGT, 'DisplayName','DB tgt. MLSE');
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plot(baudrates/1e9, reqROP_ML_MLSE2, '-v','Color',colML_MLSE, 'DisplayName','ML-based MLSE (L=2)');
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plot(baudrates/1e9, reqROP_ML_MLSE3, '-d','Color',colML_MLSE*0.8,'DisplayName','ML-based MLSE (L=3)');
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xlabel('Baud rate [GBd]');
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ylabel('Required ROP [dBm]');
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title('ROP required for FEC threshold');
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grid on; legend('Location','northwest');
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beautifyBERplot("logscale",0,"polyfit",1,"polyorder",4,"fitmethod",'polyfit');
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