305 lines
12 KiB
Matlab
305 lines
12 KiB
Matlab
%%% Run parameters
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% TX
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% --- FIRST LINE: evaluate settings located beside this script ---
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run(fullfile(fileparts(mfilename('fullpath')),'WDM_settings.m'));
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num_realiz = 50;
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wavelengthplan = calcWavelengthPlan(16,400e9,1310);
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% wavelengthplan = [1295,1305,1315,1325];
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link_length = 10;
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pmd = 0.1;
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gamma = 0.0023;
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M = 4;
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m = floor(log2(M)*10)/10;
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fsym = 224e9;
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fdac = 2*fsym;
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fadc = 2*fsym;
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random_key = 2;
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% Laser / Modulator
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vbias_rel = 0.5;
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u_pi = 3.2;
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vbias = -vbias_rel*u_pi;
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laser_linewidth = 0e6;
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% EQ SETTINGS
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vnle_order1 = 50;
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vnle_order2 = 3;
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vnle_order3 = 3;
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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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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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%DB Stuff
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db_precode = 0;
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db_encode = 0;
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duob_mode = db_mode.no_db;
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apply_pulsef = 0;
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rcalpha = 0.05;
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Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
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N = numel(wavelengthplan);
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f_plan = physconst('lightspeed')./(wavelengthplan.*1e-9);
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margin = 25e12; % some THz left and right
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f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
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f_nyq = f_span/2;
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kover = 8;
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upsample_required = f_nyq./(fdac*kover/2);
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upsample_pow = 2^nextpow2(upsample_required);
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upsample_ceil = ceil(upsample_required);
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f_opt = fdac*kover*upsample_pow;
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f_opt_nyq = f_opt/2;
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signal_cell = {};
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Symbols = {};
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Tx_bits = {};
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rop = -8.25:0.75:0;
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output_ffe = cell(length(wavelengthplan),length(rop),num_realiz);
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output_vnle = cell(length(wavelengthplan),length(rop),num_realiz);
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output_mlse = cell(length(wavelengthplan),length(rop),num_realiz);
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output_dbt = cell(length(wavelengthplan),length(rop),num_realiz);
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for realiz = 1:num_realiz
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parfor l = 1:N
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[Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource(...
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"fsym",fsym,"M",M,"order",18,"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+l+realiz,...
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"db_precode",db_precode,"db_encode",db_encode,...
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"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode).process();
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% Digi_sig.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
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Lp_awg = Filter('filtdegree',3,"f_cutoff",100e9,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true);
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El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",0,"H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig);
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% El_sig = M8199B("kover",kover).process(Digi_sig);
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% El_sig.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
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%%%%% Electrical Driver Amplifier %%%%%%
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El_sig = El_sig.normalize("mode","oneone");
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% El_sig = El_sig.setPower(1,"dBm");
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% figure;histogram(El_sig.signal);
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%%%%% MODULATE E/O CONVERSION %%%%%
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Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",wavelengthplan(l),"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+l+realiz).process(El_sig);
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signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",30).process(Eml_out);
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end
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Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover,...
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"lambda_center",1310,"random_key",0,"filtype",1,"B",200e9).process(signal_cell);
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Opt_sig_wdm = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",3+10*log10(N)).process(Opt_sig_wdm);
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% Opt_sig_wdm.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
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% Opt_sig_wdm.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',1,'max_num_lines',2);
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%%%%%% Fiber %%%%%%
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Opt_sig_wdm_fib=Opt_sig_wdm;
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segment_length = 1; % km
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nSegments = link_length/segment_length;
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zdw = 1310;
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D_local = 0; %if ~=0, simulation uses "segmented fiber with d+,d-)
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randomize_D = true;
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Dvec = getDispersionVector(nSegments, D_local, zdw, randomize_D, random_key+realiz);
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for s = 1:nSegments
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Opt_sig_wdm_fib = DP_Fiber("L",segment_length,"D",Dvec(s),"Dpmd",pmd,"Ds",0.07,...
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"beat_len",10,"corr_len",100,"dz",1,"manakov",0,...
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"gamma",gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01,...
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"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1).process(Opt_sig_wdm_fib);
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end
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Opt_sig_wdm_fib.spectrum("fignum",realiz,"displayname",'bla','lambda0_nm',1310,'useWavelengthAxis',0);
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% Opt_sig_wdm_fib.move_it_spectrum("fignum",100212,"displayname",'bla');
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% Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig)
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parfor ri = 1:length(rop)
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%%%%%% ROP %%%%%%
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Opt_sig_wdm_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop(ri)+10*log10(N)).process(Opt_sig_wdm_fib);
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Opt_sig_wdm_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1,"fs_out",Opt_sig_wdm_rx.fs/upsample_pow,"fs_in",Opt_sig_wdm_rx.fs,"lambda_center",1310).process(Opt_sig_wdm_rx);
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PD_cell = {};
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for l = 1:N
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%%%%%% PD Square Law %%%%%%
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assert(fdac*kover==Opt_sig_wdm_demux{l}.fs,'Sampling Frequencies do not match! Check previous steps');
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PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",random_key+l+realiz).process(Opt_sig_wdm_demux{l});
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PD_sig.spectrum("fignum",222,"displayname",'bla','normalizeTo0dB',1);
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%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
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rx_bwl = 100e9;
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PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(PD_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(PD_sig);
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Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym);
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% Scpe_sig.spectrum("fignum",222,"displayname",'bla','normalizeTo0dB',1);
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[~, Scpe_cell, ~, found_sync] = Scpe_sig_2sps.tsynch("reference", Symbols{l}, "fs_ref", fsym, "debug_plots", 1);
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Rx_sig = Scpe_cell{1};
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Rx_sig = Rx_sig.normalize("mode","rms");
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% FFE
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ffe_order = [50, 0, 0];
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eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0],"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",0);
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ffe_results = ffe(eq_ffe,M,Rx_sig,Symbols{l},Tx_bits{l},...
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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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output_ffe{l,ri,realiz} = ffe_results;
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%VNLE
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pf_ncoeffs = 1;
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ffe_order = [50, 5, 5];
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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);
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pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
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useviterbi = 0;
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if useviterbi
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mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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else
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mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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end
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[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig, Symbols{l},Tx_bits{l}, ...
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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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output_vnle{l,ri,realiz} = vnle_results;
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output_mlse{l,ri,realiz} = mlse_results;
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% DB tgt.
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useviterbi = 0;
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if useviterbi
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mlse_db_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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else
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mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
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end
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ffe_order = [50, 5, 5];
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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);
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dbt_results = duobinary_target(eq_, mlse_db_, M, Rx_sig, Symbols{l},Tx_bits{l}, ...
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"precode_mode", duob_mode, ...
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'showAnalysis', 0,...
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"postFFE", []);
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output_dbt{l,ri,realiz} = dbt_results;
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end
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end
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res = struct();
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res.ffe = output_ffe;
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res.vnle = output_vnle;
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res.mlse = output_mlse;
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res.dbt = output_dbt;
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% Save results
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save(fullfile(output_root, fname), 'res', '-v7.3');
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fprintf('Saved results to: %s\n', fullfile(output_root, fname));
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end
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figure();hold on;
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cols = linspecer(N);
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for l = 1:N
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% plot(rop,mean(squeeze(ber_vnle(l,:,:)),2,'omitnan'),'Marker','*','DisplayName',sprintf('Ch: %d',wavelengthplan(l)))
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% plot(rop,cellfun(@(c) c.metrics.BER, output_ffe(l,:), 'UniformOutput', true),'Marker','*','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle',':');
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plot(rop,cellfun(@(c) c.metrics.BER, res.vnle(l,:), 'UniformOutput', true),'Marker','x','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle','--')
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plot(rop,cellfun(@(c) c.metrics.BER, res.mlse(l,:), 'UniformOutput', true),'Marker','o','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle','-')
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end
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yline([3.8e-3,2.2e-4],'HandleVisibility','off');
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ylabel('BER');
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xlabel('ROP')
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title('BER vs. ROP');
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set(gca, 'XScale', 'linear', ...
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'YScale', 'log', ...
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'TickLabelInterpreter', 'latex', ...
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'FontSize', 11);
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xlim([min(rop) max(rop)])
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ylim([1e-5 0.3])
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% --- save as PNG ---
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outname = fullfile(output_root, 'BER_vs_ROP.png'); % saves to current folder
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print(gcf, outname, '-dpng', '-r300'); % 300 dpi
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fprintf('Saved figure to %s\n', outname);
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function dispersion_vector = getDispersionVector(N, D, ref_zdw, randomize_ZDW, randomkey)
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% MATLAB version of the Python generator shown above.
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% Returns an N×1 vector (ps/(nm·km)).
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%
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% D is the nominal dispersion magnitude. For D>0 the link is segmented with
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% alternating sign (+D, -D, +D, …). For D==0 it is flat (0) except for
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% ZDW randomization. The ZDW detuning is ~N(0, 2 nm) around 1310 nm and is
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% converted to dispersion via 0.09 ps/(nm·km) per nm.
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% constants (matching the Python code)
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meanLambda_nm = 1310; % center wavelength
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sigma_nm = 2; % ZDW sigma
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Dslope = 0.07; % ps/(nm·km) per nm detuning
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% random ZDW-induced dispersion offset
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if randomize_ZDW
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rng(randomkey, 'twister');
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rand_zdws_nm = meanLambda_nm + sigma_nm .* randn(N,1);
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rand_D = (rand_zdws_nm - ref_zdw) .* Dslope; % ps/(nm·km)
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else
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rand_D = zeros(N,1);
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end
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% nominal segmented pattern (match Python intent; keep length N)
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if D > 0
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base = (-1) .^ ((0:N-1).'); % +1,-1,+1,-1,...
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else % D == 0 (or anything else)
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base = ones(N,1);
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end
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dispersion_vector = base .* D + rand_D; % ps/(nm·km)
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end
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