Scattered stuff from Silas during Dissertation
This commit is contained in:
Silas Oettinghaus
257
projects/Diss/investigate_noise_enhancement.m
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257
projects/Diss/investigate_noise_enhancement.m
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% Minimal IMDD model for investigating FFE noise enhancement.
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% Parameters mirror the current IMDD_base_system/imdd_it.m,
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% IMDD_base_system/imdd_model.m, and the FFE branch in dsp_scope_signal.m.
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clearvars;
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close all;
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%% Sweep parameters from imdd_it.m
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fsym_values = (152:16:200).*1e9;
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precomp = 0;
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laser_wavelength = 1310;
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M = 4;
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link_length = 2;
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channel_alpha = 0.5;
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duob_mode = db_mode.db_precoded;
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decoding_mode = db_decoder.sequencedetection;
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channel_mode = channel_model.physical;
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channel_snr_dB = 20;
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rop_values = -8:2:4;
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%% Inner model parameters from imdd_model.m
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bitrate = [];
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apply_pulsef = 0;
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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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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_linewidth = 0;
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eml_alpha = 0;
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len_tr = 4096*2;
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pf_ncoeffs = 1;
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mu_dc = 0.005;
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nRates = numel(fsym_values);
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nRops = numel(rop_values);
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eq_names = ["FFE","VNLE"];
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nEqs = numel(eq_names);
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emptyResult = struct("fsym",[],"ROP",[],"equalizer",[],"BER",[],"SNR",[],"numBitErr",[], ...
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"eq_noise",[],"eq_signal_sd",[],"eq",[],"Rx_sig",[],"Tx_sig",[],"Tx_symbols",[]);
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results = repmat(emptyResult,nRates,nRops,nEqs);
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cols = flip(cbrewer2('RdYlBu',nRates+4));
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midIdx = floor(size(cols,1)/2) + (-1:2);
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cols(midIdx,:) = [];
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for i = 1:numel(fsym_values)
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fsym = fsym_values(i);
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fprintf("Running %.0f GBd (%d/%d)\n", fsym*1e-9, i, numel(fsym_values));
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if isempty(bitrate)
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bitrateCurrent = fsym * log2(M);
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end
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Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
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[Digi_sig, Symbols, ~] = 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, ...
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"duobinary_mode",duob_mode, ...
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"mrds_code",0,"mrds_blocklength",512).process();
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rateResults = repmat(emptyResult,nRops,nEqs);
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Tx_sig = Digi_sig;
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El_sig = M8199B("kover",kover).process(Digi_sig);
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tx_bwl = 85e9;
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El_sig = Filter("filtdegree",4,"f_cutoff",tx_bwl,"fs",fdac*kover, ...
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"filterType",filtertypes.bessel_inp,"active",true).process(El_sig);
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El_sig = El_sig.normalize("mode","oneone");
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scaling = 0.7*(u_pi/2-abs(vbias-u_pi/2));
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El_sig = El_sig .* scaling;
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Opt_sig = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs, ...
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"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi, ...
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"linewidth",laser_linewidth,"randomkey",random_key+1, ...
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"alpha",eml_alpha).process(El_sig);
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Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length, ...
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"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
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parfor j = 1:nRops
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rop = rop_values(j);
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fprintf(" ROP %.1f dBm (%d/%d)\n", rop, j, nRops);
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Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
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"amplification_db",rop).process(Opt_sig);
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Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08, ...
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"responsivity",0.6,"temperature",20,"nep",1.8e-11).process(Rx_sig);
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rx_bwl = 90e9;
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Rx_sig = Filter("filtdegree",4,"f_cutoff",rx_bwl,"fs",fdac*kover, ...
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"filterType",filtertypes.butterworth,"active",true).process(Rx_sig);
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Lp_scpe = Filter("filtdegree",4,"f_cutoff",110e9,"fs",fadc, ...
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"filterType",filtertypes.butterworth,"active",true);
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Rx_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, ...
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"samp_jitter",0,"adcresolution",8,"quantbuffer",0.1, ...
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"block_dc",1,"lpf_active",1,"H_lpf",Lp_scpe).process(Rx_sig);
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txPulseformer = [];
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if apply_pulsef
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txPulseformer = Pform;
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end
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Scpe_sig = preprocessSignal(Rx_sig, Symbols, fsym, ...
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"mode","auto", ...
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"tx_pulseformer",txPulseformer, ...
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"debug_plots",0);
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Scpe_sig.signal = Scpe_sig.signal(1:2*Symbols.length);
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Scpe_sig.signal = real(Scpe_sig.signal);
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for k = 1:nEqs
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switch eq_names(k)
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case "FFE"
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eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
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"mu_dd",1e-1,"mu_tr",0.4,"order",50, ...
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"sps",2,"decide",0,"optmize_mus",1,"dd_mode",1, ...
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"adaption_technique","nlms","mu_dc",1.021e-05);
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case "VNLE"
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eq_ = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
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"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0, ...
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"order",[25,2,2],"sps",2,"decide",0, ...
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"optmize_mus",1,"mu_dc",0.1);
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end
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[eq_signal_sd, eq_noise] = eq_.process(Scpe_sig, Symbols);
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eq_signal_hd = PAMmapper(M,0).quantize(eq_signal_sd);
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rx_bits = PAMmapper(M,0).demap(eq_signal_hd);
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tx_bits_demapped = PAMmapper(M,0).demap(Symbols);
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[~, errors, ber] = calc_ber(rx_bits.signal, tx_bits_demapped.signal, ...
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"skip_front",10,"skip_end",10);
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localResult = emptyResult;
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localResult.fsym = fsym;
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localResult.ROP = rop;
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localResult.equalizer = eq_names(k);
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localResult.BER = ber;
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localResult.SNR = snr(eq_signal_sd.signal, eq_noise.signal);
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localResult.numBitErr = errors;
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localResult.eq_noise = eq_noise;
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localResult.eq_signal_sd = eq_signal_sd;
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localResult.eq = eq_;
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localResult.Rx_sig = Rx_sig;
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localResult.Tx_sig = Tx_sig;
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localResult.Tx_symbols = Symbols;
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rateResults(j,k) = localResult;
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end
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end
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results(i,:,:) = reshape(rateResults,1,nRops,nEqs);
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end
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%% Plot section
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close all
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berGrid = reshape([results.BER],nRates,nRops,nEqs);
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figure(400);
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clf;
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for k = 1:nEqs
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subplot(1,nEqs,k);
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plot(fsym_values.*1e-9, berGrid(:,:,k), "LineWidth",0.8, ...
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"LineStyle","-","Marker",".","MarkerSize",15);
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set(gca,"YScale","log");
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grid on;
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grid minor;
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xlabel("Symbol rate (GBd)");
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ylabel("BER");
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title(eq_names(k) + " BER vs. Symbol Rate");
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legend(compose("ROP %.1f dBm",rop_values),"Location","best");
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end
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%%
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figure(500);
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clf;
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for k = 1:nEqs
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subplot(1,nEqs,k);
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plot(rop_values, berGrid(:,:,k).', "LineWidth",0.8, ...
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"LineStyle","-","Marker",".","MarkerSize",15);
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set(gca,"YScale","log");
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grid on;
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grid minor;
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xlabel("ROP");
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ylabel("BER");
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title(eq_names(k) + " BER vs. ROP");
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legend(compose("%.0f GBd",fsym_values.*1e-9),"Location","best");
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end
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%%
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plotResults = reshape(results(:,end,:),nRates,nEqs);
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for k = 1:nEqs
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for i = 1:nRates
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fsym = plotResults(i,k).fsym;
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eq_noise = plotResults(i,k).eq_noise;
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eq_signal_sd = plotResults(i,k).eq_signal_sd;
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Rx_sig = plotResults(i,k).Rx_sig;
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Tx_sig = plotResults(i,k).Tx_sig;
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Tx_symbols = plotResults(i,k).Tx_symbols;
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displayName = sprintf('%s, %d GBd, ROP %.1f dBm',plotResults(i,k).equalizer,fsym.*1e-9,plotResults(i,k).ROP);
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showEQNoisePSD(eq_noise, "fignum", 1, "displayname", displayName,"color",cols(i,:));
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xlim([0, 120])
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/ffe_noise_enhancement/noise_psd.tex")
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Tx_sig.normalize("mode","rms").spectrum("displayname",displayName,'fignum',2,'normalizeTo0dB',0,"color",cols(i,:),"linestyle",'-','HandleVisibility','on');
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xlim([0, 120])
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/ffe_noise_enhancement/tx_signals.tex")
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Rx_sig.normalize("mode","rms").spectrum("displayname",displayName,'fignum',3,'normalizeTo0dB',0,"color",cols(i,:));
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xlim([0, 120])
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/ffe_noise_enhancement/rx_signals.tex")
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showEQNoiseSNR(eq_signal_sd, eq_noise,"displayname",displayName,"color",cols(i,:),"fignum",4);
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xlim([0, 120])
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/ffe_noise_enhancement/snr_after_ffe.tex")
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% showEQfilter(plotResults(i,k).eq.e, eq_signal_sd.fs.*2,"displayname",displayName,'fignum',5,"color",cols(i,:));
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% xlim([0, 120]);
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fprintf('%s BER %.2e \n',plotResults(i,k).equalizer,plotResults(i,k).BER)
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if i == 1 || i == nRates-1 || i == nRates
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show2Dconstellation(PAMmapper(M,0).get_scaling.*eq_signal_sd.signal(1:end), PAMmapper(M,0).get_scaling.*Tx_symbols.signal(1:end), ...
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"displayname",displayName, ...
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"fignum",5+(k-1)*nRates+i, ...
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"clear",i == 1 && k == 1,"rasterize", false);
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mat2tikz_improved(sprintf("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/ffe_noise_enhancement/noise_correlation_%s.tex",displayName),"cleanfigure",true);
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eq_signal_sd.eye(fsym,M,"fignum",8+(k-1)*nRates+i,"mode",1);
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end
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end
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end
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%%
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66
projects/Diss/peak_distortion.m
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66
projects/Diss/peak_distortion.m
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@@ -0,0 +1,66 @@
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% Build convolution matrix and solve c such that conv(x,c) approx delta
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x = [0.05; -0.12; 0.3; 1; 0.25; -0.1; 0.04]; % channel pulse response
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Ntaps = 7;
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mu = 0.002;
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Nit = 500;
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[c, hhist] = peakDistEqualizer(x, Ntaps, mu, Nit);
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h_eff = conv(x,c);
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stem(h_eff)
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grid on
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xlabel('Sample index')
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ylabel('Combined impulse response')
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function [c, h_hist] = peakDistEqualizer(x, Ntaps, mu, Nit)
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% Peak-distortion / Lucky sign-gradient equalizer
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%
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% x : sampled channel pulse response, main cursor should be near center
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% Ntaps : odd number of equalizer taps
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% mu : step size
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% Nit : number of training iterations
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%
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% c : equalizer coefficients
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% h : combined impulse response h = conv(x,c)
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x = x(:);
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assert(mod(Ntaps,2)==1, 'Ntaps must be odd');
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midTap = (Ntaps+1)/2;
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c = zeros(Ntaps,1);
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c(midTap) = 1; % start with through connection
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h_hist = cell(Nit,1);
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for it = 1:Nit
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h = conv(x,c);
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h_hist{it} = h;
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% locate main cursor
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[~, mainIdx] = max(abs(h));
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% normalize main cursor conceptually
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h = h / h(mainIdx);
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% update non-center equalizer taps
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for j = 1:Ntaps
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if j == midTap
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continue
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end
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% map equalizer tap j to corresponding ISI sample
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isiIdx = mainIdx + (j - midTap);
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if isiIdx >= 1 && isiIdx <= length(h)
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c(j) = c(j) - mu * sign(h(isiIdx));
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end
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end
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% optional: normalize center/main gain
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h = conv(x,c);
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[~, mainIdx] = max(abs(h));
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c = c / h(mainIdx);
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end
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end
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