%% Bayesian Optimization for FFE Parameter Tuning % This script uses bayesopt to find optimal mu_dd and mu_tr values % that minimize BER for the FFE equalizer. clear; clc; %% Setup - Same as gpu_processing_dpfiber.m s.wavelengthplan = calcWavelengthPlan(4, 400e9, 1310); link_length = 10; s.pmd = 0.1; s.gamma = 0.0023; s.M = 4; fsym = 112e9; fdac = 2*fsym; fadc = 120000000000; s.random_key = 1; % Laser / Modulator vbias_rel = 0.5; u_pi = 4.6; vbias = -vbias_rel*u_pi; laser_linewidth = 0e6; duob_mode = db_mode.no_db; rcalpha = 0.05; Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha); s.chirpalpha = 0; s.p_launch = 3; s.p = "co"; N = numel(s.wavelengthplan); switch s.p case "co" pol_rot = 100.*ones(1,N); d_local = 0; end f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9); margin = 25e12; f_span = (max(f_plan)+margin)-(min(f_plan)-margin); f_nyq = f_span/2; kover = 4; upsample_required = f_nyq./(fdac*kover/2); upsample_pow = 2^nextpow2(upsample_required); s.f_opt = fdac*kover*upsample_pow; s.f_opt_nyq = s.f_opt/2; s.rop = -8; % Fixed ROP for optimization %% Generate TX signals (run once) fprintf('Generating TX signals...\n'); for l = 1:N [Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource( ... "fsym",fsym,"M",s.M,"order",15,"useprbs",0, ... "fs_out",fdac, ... "applyclipping",0,"clipfactor",1.5, ... "applypulseform",1,"pulseformer",Pform, ... "randkey",s.random_key+l, ... "mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode ... ).process(); Lp_awg = Filter('filtdegree',3,"f_cutoff",56e9,"fs",fdac*kover, ... "filterType",filtertypes.gaussian,"active",true); El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover, ... "bit_resolution",6,"upsampling_method","samplehold","precomp_sinc_rolloff",0, ... "H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig); El_sig = El_sig.normalize("mode","oneone"); scaling = 0.6*(u_pi/2-abs(vbias-u_pi/2)); El_sig = El_sig .* scaling; Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs, ... "lambda",s.wavelengthplan(l),"bias",vbias,"u_pi",u_pi, ... "linewidth",laser_linewidth,"randomkey",s.random_key+l,"alpha",s.chirpalpha).process(El_sig); signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",pol_rot(l)).process(Eml_out); end %% WDM mux + launch Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover, ... "lambda_center",1310,"random_key",0,"filtype",1,"B",120e9).process(signal_cell); Opt_sig_wdm = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ... "amplification_db",s.p_launch+10*log10(N)).process(Opt_sig_wdm); %% Fiber propagation segment_length = 1; nSegments = link_length/segment_length; nSegments = round(nSegments); zdw = 1310; randomize_D = true; Dvec = getDispersionVector(nSegments, d_local, zdw, randomize_D, s.random_key); Opt_sig_wdm_fib = Opt_sig_wdm; fprintf('Running fiber propagation...\n'); for seg = 1:nSegments fprintf('Segment %d/%d\n', seg, nSegments); Opt_sig_wdm_fib = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ... "beat_len",10,"corr_len",100,"dz",1,"manakov",0, ... "gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ... "SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",true,"useSingle",true).process(Opt_sig_wdm_fib); end %% Pre-process to get Rx_sig (do demux once) fprintf('Pre-processing receiver chain...\n'); l = 1; % Use channel 1 for optimization Opt_sig_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1, ... "fs_out",fdac*kover,"fs_in",fdac*kover*upsample_pow,"lambda_center",1310).process(Opt_sig_wdm_fib); Opt_sig_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ... "amplification_db",s.rop).process(Opt_sig_demux{l}); PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20, ... "nep",1.8e-11,"randomkey",s.random_key+l).process(Opt_sig_rx); rx_bwl = 100e9; PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover, ... "filterType",filtertypes.butterworth,"active",true).process(PD_sig); Lp_scpe = Filter('filtdegree',4,"f_cutoff",80e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true); 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',0,'H_lpf',Lp_scpe).process(PD_sig); Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym); [~, Scpe_cell, ~, ~] = Scpe_sig_2sps.tsynch("reference", Symbols{l}, "fs_ref", fsym, "debug_plots", 0); Rx_sig = Scpe_cell{1}; Rx_sig = Rx_sig.normalize("mode","rms"); fprintf('Receiver pre-processing complete. Ready for optimization.\n\n'); %% Define the objective function for bayesopt function ber = ffe_objective(params, Rx_sig, Symbols_l, Tx_bits_l, M, duob_mode) mu_dd = params.mu_dd; mu_tr = params.mu_tr; try eq_ffe = FFE("epochs_tr", 5, "epochs_dd", 2, "len_tr", 2^13, ... "mu_dd", mu_dd, "mu_tr", mu_tr, ... "order", 50, "sps", 2, "decide", 0, ... "adaption", adaption_method.nlms, "dd_mode", 1); ffe_results = ffe(eq_ffe, M, Rx_sig, Symbols_l, Tx_bits_l, ... "precode_mode", duob_mode, ... 'showAnalysis', 0, ... "postFFE", [], ... "eth_style_symbol_mapping", 0); ber = ffe_results.metrics.BER; if ber == 0 ber = 1e-10; end if ~isfinite(ber) ber = 0.5; end fprintf(' mu_dd=%.4e, mu_tr=%.4e -> BER=%.4e\n', mu_dd, mu_tr, ber); catch ME fprintf(' mu_dd=%.4e, mu_tr=%.4e -> FAILED (%s)\n', mu_dd, mu_tr, ME.message); ber = 0.5; end end %% Define optimizable variables mu_dd_var = optimizableVariable('mu_dd', [1e-5, 0.1], 'Transform', 'log'); mu_tr_var = optimizableVariable('mu_tr', [1e-5, 0.1], 'Transform', 'log'); %% Run Bayesian Optimization fprintf('========== Starting Bayesian Optimization ==========\n'); fprintf('Optimizing mu_dd and mu_tr to minimize BER\n'); fprintf('Search range: mu_dd=[1e-5, 0.1], mu_tr=[1e-5, 0.1]\n\n'); objective_fn = @(params) ffe_objective(params, Rx_sig, Symbols{l}, Tx_bits{l}, s.M, duob_mode); results = bayesopt(objective_fn, [mu_dd_var, mu_tr_var], ... 'MaxObjectiveEvaluations', 30, ... 'AcquisitionFunctionName', 'expected-improvement-plus', ... 'IsObjectiveDeterministic', false, ... 'ExplorationRatio', 0.5, ... 'Verbose', 1, ... 'PlotFcn', []); %% Display Results fprintf('\n========== FFE Optimization Complete ==========\n'); fprintf('Best FFE parameters found:\n'); fprintf(' mu_dd = %.6e\n', results.XAtMinObjective.mu_dd); fprintf(' mu_tr = %.6e\n', results.XAtMinObjective.mu_tr); fprintf(' BER = %.6e\n', results.MinObjective); %% Verify with optimal parameters fprintf('\nVerifying optimal FFE parameters...\n'); best_mu_dd = results.XAtMinObjective.mu_dd; best_mu_tr = results.XAtMinObjective.mu_tr; eq_ffe_best = FFE("epochs_tr", 5, "epochs_dd", 2, "len_tr", 2^13, ... "mu_dd", best_mu_dd, "mu_tr", best_mu_tr, ... "order", 50, "sps", 2, "decide", 0, ... "adaption", adaption_method.nlms, "dd_mode", 1); ffe_results_best = ffe(eq_ffe_best, s.M, Rx_sig, Symbols{l}, Tx_bits{l}, ... "precode_mode", duob_mode, ... 'showAnalysis', 1, ... "postFFE", [], ... "eth_style_symbol_mapping", 0); fprintf('\nFinal FFE BER with optimal parameters: %.6e\n', ffe_results_best.metrics.BER);