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.
Minor changes here and there
This commit is contained in:
Silas Oettinghaus
342
Theory/Signal_Processing/EQ_adaption.m
Normal file
342
Theory/Signal_Processing/EQ_adaption.m
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@@ -0,0 +1,342 @@
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% minimal example IM/DD
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M = 4;
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fsym = 180e9;
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apply_pulsef = 1;
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fdac = 256e9;
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fadc = 256e9;
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random_key = 1;
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rcalpha = 0.05;
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kover = 16;
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duob_mode = db_mode.no_db;
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vbias_rel = 0.5;
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u_pi = 3;
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vbias = -vbias_rel*u_pi;
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laser_wavelength = 1293;
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laser_linewidth = 0;
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tx_bw_nyquist = 0.8;
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% Channel
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link_length = 1;
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% RX
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rop = -7;
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rx_bw_nyquist = 0.8;
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vnle_order1 = 50;
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vnle_order2 = 7;
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vnle_order3 = 7;
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vnle_order=[vnle_order1,vnle_order2,vnle_order3];
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dfe_order = [0 0 0];
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pf_ncoeffs = 1;
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alpha = 0;
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len_tr = 4096*2;
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mu_ffe1 = 0.0001;
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mu_ffe2 = 0.0008;
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mu_ffe3 = 0.001;
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mu_dc = 0.005;
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% mu_dc = 0;
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mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
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mu_dfe = 0.0004;
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n_stat_runs = 1;
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base_random_key = random_key;
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output.eq_stats = struct();
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for stat_run = 1:n_stat_runs
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random_key = base_random_key + stat_run - 1;
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Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
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[Digi_sig,Symbols,Tx_bits] = PAMsource(...
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"fsym",fsym,"M",M,"order",16,"useprbs",0,...
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"fs_out",fdac,...
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"applyclipping",0,"clipfactor",1.5,...
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"applypulseform",apply_pulsef,"pulseformer",Pform,...
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"randkey",random_key,...
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'duobinary_mode',duob_mode,...
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"mrds_code",0,"mrds_blocklength",512).process();
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%%%%% AWG
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El_sig = M8199A("kover",kover).process(Digi_sig);
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% 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);
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El_sig.spectrum("displayname",'Digi Spectrum','fignum',1,'normalizeTo0dB',1);
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xlim([0,130]);
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ylim([-30,5]);
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% El_sig = El_sig.setPower(0,"dBm");
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%%%%% Electrical Driver Amplifier %%%%%%
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% El_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",3).process(El_sig);
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El_sig = El_sig.normalize("mode","oneone");
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scaling = 0.6*(u_pi/2-abs(vbias-u_pi/2));
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El_sig = El_sig .* scaling;
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%%%%% MODULATE E/O CONVERSION %%%%%%
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[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);
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Opt_sig.spectrum("displayname",'Opt Spectrum','fignum',10,'normalizeTo0dB',1);
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% Opt_sig.eye(fsym,M,"displayname",'eye adter modulator','fignum',2026);
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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);
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%%%%%% ROP %%%%%%
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Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
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%%%%%% PD Square Law %%%%%%
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Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
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%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
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rx_bwl = 80e9;
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Rx_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(Rx_sig);
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% %%%%%% Low-pass Scope %%%%%%
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Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
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%%%%%% Scope %%%%%%
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Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
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"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
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"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
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"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);
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%%
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% 1) matched filter
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% pulse is symmetric, hence we can use pulsef firectly as matched filter.
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% It feels off (bit I think correct) that the fsym is now the output freq.!!
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% -> output 2 sps to omit timing recovery!?
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Pform = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha,"matched",1);
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Scpe_sig = Pform.process(Scpe_sig);
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Scpe_sig.spectrum("displayname",'Signal after matched filter','fignum',1,'normalizeTo0dB',1);
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%
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% %%
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% %%%%%% Sample to 2x fsym %%%%%%
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% Scpe_sig = Scpe_sig.resample("fs_out",2*fsym);
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% Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
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%%
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%%%%%% Sync Rx signal with reference %%%%%%
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[Scpe_sig,~] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym,"debug_plots",0);
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Scpe_sig.spectrum("displayname",'Opt Spectrum','fignum',11,'normalizeTo0dB',1);
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% Scpe_sig = Filter('filtdegree',4,"f_cutoff",Symbols.fs.*0.5,"fs",Scpe_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scpe_sig);
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Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
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Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
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%% -------------------- FFE --------------------
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ffe_options = struct( ...
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"epochs_tr", 3, ...
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"epochs_dd", 3, ...
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"len_tr", 4096, ...
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"order", 40, ...
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"sps", 2, ...
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"decide", 0, ...
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"adaption", adaption_method.rls, ...
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"dd_mode", 1);
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eq_nlms = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
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"len_tr",ffe_options.len_tr,"mu_dd",0.001,"mu_tr",0.001, ...
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"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
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"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1,"optmize_mus",1);
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output.ffe_results = ffe(eq_nlms,M,Scpe_sig,Symbols,Tx_bits, ...
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"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
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"eth_style_symbol_mapping",0);
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output.ffe_results.metrics.print("description","FFE");
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%%
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% -------------------- FFE --------------------
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%%
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for i = 3:-1:1
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mu_adaptation = adaption_method(i);
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switch mu_adaptation
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case adaption_method.lms
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mu_search_range = [1e-5, 1e-2];
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case adaption_method.nlms
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mu_search_range = [1e-3, 5e-1];
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case adaption_method.rls
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mu_search_range = [0.98, 0.99999]; % here mu is lambda
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end
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mu_max_evaluations = 10;
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ffe_options = struct( ...
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"epochs_tr", 3, ...
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"epochs_dd", 2, ...
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"len_tr", 4096*2, ...
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"order", 20, ...
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"sps", 2, ...
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"decide", 0, ...
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"adaption", mu_adaptation, ...
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"dd_mode", 1);
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method_name = char(mu_adaptation);
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rng(random_key, "twister");
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mu_dd_var = optimizableVariable("mu_dd", mu_search_range, "Transform", "log");
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mu_tr_var = optimizableVariable("mu_tr", mu_search_range, "Transform", "log");
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global ffe_mu_trace
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ffe_mu_trace = table('Size',[0 5], ...
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'VariableTypes',{'double','double','double','double','double'}, ...
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'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'});
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objective_fn = @(params) ffe_mu_objective(params, ffe_options, M, Scpe_sig, Symbols, Tx_bits, duob_mode);
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output.eq_stats.(method_name).mu_optimization{stat_run} = bayesopt(objective_fn, [mu_dd_var, mu_tr_var], ...
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"MaxObjectiveEvaluations", mu_max_evaluations, ...
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"AcquisitionFunctionName", "expected-improvement-plus", ...
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"IsObjectiveDeterministic", false, ...
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"Verbose", 1, ...
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"PlotFcn", []);
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output.eq_stats.(method_name).mu_observed(stat_run,:) = output.eq_stats.(method_name).mu_optimization{stat_run}.XAtMinObjective;
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output.eq_stats.(method_name).mu_estimated(stat_run,:) = output.eq_stats.(method_name).mu_optimization{stat_run}.XAtMinEstimatedObjective;
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output.eq_stats.(method_name).optimization_trace{stat_run} = ffe_mu_trace;
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best_mu_dd = output.eq_stats.(method_name).mu_observed.mu_dd(stat_run);
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best_mu_tr = output.eq_stats.(method_name).mu_observed.mu_tr(stat_run);
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output.eq_stats.(method_name).mu_used(stat_run,:) = table(best_mu_dd,best_mu_tr, ...
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'VariableNames',{'mu_dd','mu_tr'});
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eq_nlms = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
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"len_tr",ffe_options.len_tr,"mu_dd",best_mu_dd,"mu_tr",best_mu_tr, ...
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"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
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"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1);
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output.ffe_results = ffe(eq_nlms,M,Scpe_sig,Symbols,Tx_bits, ...
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"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
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"eth_style_symbol_mapping",0);
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output.ffe_results.metrics.print("description",sprintf('%s',mu_adaptation));
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output.eq_stats.(method_name).error_tr_first_epoch(stat_run,:) = eq_nlms.debug_struct.error_tr(1,:);
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% figure(6)
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% hold on
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% for epoch = 1:ffe_options.epochs_tr
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% plot(1:size(eq_nlms.debug_struct.error_tr,2),movmean(eq_nlms.debug_struct.error_tr(epoch,:),10));
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% end
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end
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end
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%%
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figure(5);
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clf;
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hold on;
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method_names = fieldnames(output.eq_stats);
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cols = cbrewer2('Paired',6);
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cols = cols(2:2:end,:);
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for i = 1:numel(method_names)
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method_name = method_names{i};
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first_epoch = output.eq_stats.(method_name).error_tr_first_epoch;
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for j = 1:size(first_epoch,1)
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plot(movmean(output.eq_stats.(method_name).error_tr_first_epoch(j,:),10), ...
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"DisplayName",method_name,"Color",[0.7,0.7,0.7],"LineWidth",0.1,'HandleVisibility','off');
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end
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end
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for i = 1:numel(method_names)
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method_name = method_names{i};
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first_epoch = output.eq_stats.(method_name).error_tr_first_epoch;
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output.eq_stats.(method_name).error_tr_first_epoch_mean = mean(first_epoch,1,"omitnan");
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output.eq_stats.(method_name).error_tr_first_epoch_std = std(first_epoch,0,1,"omitnan");
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plot(movmean(output.eq_stats.(method_name).error_tr_first_epoch_mean,50), ...
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"DisplayName",method_name,"Color",cols(i,:));
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end
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grid on;
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xlabel("Training symbol index","Interpreter","tex");
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ylabel("Mean squared error, first epoch","Interpreter","tex");
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legend("Location","best","Interpreter","tex");
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figure(6);
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clf;
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tiledlayout(numel(method_names),3,"TileSpacing","compact","Padding","compact");
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metric_names = {'BER','SNR','mse_last_epoch'};
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metric_labels = {'BER','SNR','mean abs(error)^2, last epoch'};
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for i = 1:numel(method_names)
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method_name = method_names{i};
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trace = vertcat(output.eq_stats.(method_name).optimization_trace{:});
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observed_mu = output.eq_stats.(method_name).mu_observed;
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estimated_mu = output.eq_stats.(method_name).mu_estimated;
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for metric_idx = 1:numel(metric_names)
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nexttile;
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metric = trace.(metric_names{metric_idx});
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if metric_idx ~= 2
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metric = log10(max(metric,realmin));
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end
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scatter(trace.mu_tr,trace.mu_dd,25,metric,"filled");
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hold on;
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plot(observed_mu.mu_tr,observed_mu.mu_dd,"kp","MarkerSize",11, ...
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"MarkerFaceColor","y","LineWidth",1.2,"DisplayName","Observed best");
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plot(estimated_mu.mu_tr,estimated_mu.mu_dd,"kx","MarkerSize",10, ...
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"LineWidth",1.6,"DisplayName","Estimated best");
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set(gca,"XScale","log","YScale","log");
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grid on;
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xlabel('\mu_{tr}',"Interpreter","tex");
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ylabel('\mu_{dd}',"Interpreter","tex");
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title(sprintf("%s: %s",method_name,metric_labels{metric_idx}),"Interpreter","tex");
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cb = colorbar;
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if metric_idx ~= 2
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cb.Label.String = sprintf("log10(%s)",metric_labels{metric_idx});
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else
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cb.Label.String = metric_labels{metric_idx};
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end
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cb.Label.Interpreter = "tex";
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legend("Location","best","Interpreter","tex");
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end
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end
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function objective = ffe_mu_objective(params, ffe_options, M, rx_signal, tx_symbols, tx_bits, duob_mode)
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global ffe_mu_trace
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try
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eq_candidate = FFE("epochs_tr",ffe_options.epochs_tr,"epochs_dd",ffe_options.epochs_dd, ...
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"len_tr",ffe_options.len_tr,"mu_dd",params.mu_dd,"mu_tr",params.mu_tr, ...
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"order",ffe_options.order,"sps",ffe_options.sps,"decide",ffe_options.decide, ...
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"adaption",ffe_options.adaption,"dd_mode",ffe_options.dd_mode,"save_debug",1);
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candidate_results = ffe(eq_candidate, M, rx_signal, tx_symbols, tx_bits, ...
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"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
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"eth_style_symbol_mapping",0);
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objective = candidate_results.metrics.BER;
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if ~isfinite(objective)
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objective = 0.5;
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end
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mse_last_epoch = mean(eq_candidate.debug_struct.error_tr(end,:),"omitnan");
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ffe_mu_trace = [ffe_mu_trace; table(params.mu_dd,params.mu_tr,objective, ...
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candidate_results.metrics.SNR,mse_last_epoch, ...
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'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'})];
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fprintf("mu_dd=%9.3e, mu_tr=%9.3e -> BER=%9.3e, SNR=%6.2f dB, MSE=%9.3e\n", ...
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params.mu_dd, params.mu_tr, objective, candidate_results.metrics.SNR, mse_last_epoch);
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catch ME
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objective = 0.5;
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ffe_mu_trace = [ffe_mu_trace; table(params.mu_dd,params.mu_tr,objective,NaN,NaN, ...
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'VariableNames',{'mu_dd','mu_tr','BER','SNR','mse_last_epoch'})];
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fprintf("mu_dd=%9.3e, mu_tr=%9.3e -> failed: %s\n", ...
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params.mu_dd, params.mu_tr, ME.message);
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end
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end
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