Gif stuff
nonlinear MLSE investigation trying hard to implment ML-based pre Equalization to find the branch metrics
This commit is contained in:
Silas Oettinghaus
194
projects/ML_based_MLSE/model.m
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194
projects/ML_based_MLSE/model.m
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%%% Run parameters
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% TX
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M = 4;
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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 = 2;
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rcalpha = 0.05;
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kover = 8;
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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_wavelength = 1310;
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laser_linewidth = 1e6;
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% Channel
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link_length = 0;
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alpha = 0;
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doub_mode = db_mode.no_db;
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cols = linspecer(6);
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rop = [-6];
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bwl = [0.5:0.1:1.5];
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fsym = [208:16:256].*1e9;
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% nonlin_mod = [0.5:0.01:0.75];
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nonlin_mod = ones(size(fsym)).*0.5;
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ffe_results = {};
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mlse_results_lin= {};
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for r = 1:length(fsym)
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Pform = Pulseformer("fsym",fsym(r),"fdac",4*fsym(r),"pulse","rc","pulselength",16,"alpha",rcalpha);
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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 = 1;
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[Digi_sig,Symbols,Tx_bits] = PAMsource(...
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"fsym",fsym(r),"M",M,"order",17,"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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"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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El_sig = M8199B("kover",kover).process(Digi_sig);
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%%%%% Electrical Driver Amplifier %%%%%%
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El_sig = El_sig.normalize("mode","oneone");
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%%%%% MODULATE E/O CONVERSION %%%%%
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u_pi = 3.2;
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vbias = -u_pi*nonlin_mod(r);
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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).process(El_sig);
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%%%%%% Fiber %%%%%%
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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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%%%%%% ROP %%%%%%
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Opt_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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PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",random_key).process(Opt_sig);
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%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
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rx_bwl = 70e9;
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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(r));
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% 2sps
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[~, Scpe_cell, ~, found_sync] = Scpe_sig_2sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 1);
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Rx_sig_2sps = Scpe_cell{1};
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Rx_sig_2sps = Rx_sig_2sps.normalize("mode","rms");
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% 1sps
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Scpe_sig_1sps = Scpe_sig.resample("fs_out",1*fsym(r));
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[~, Scpe_cell_1sps, ~, found_sync] = Scpe_sig_1sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 1);
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Rx_sig_1sps = Scpe_cell_1sps{1};
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Rx_sig_1sps = Rx_sig_1sps.normalize("mode","rms");
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%% Implement DSP directly here:
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mu_lms = 0.0005;
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tic
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eq = ML_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",32,"L",1);
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[Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
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toc
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% tic
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% eq = FFE_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",32,"L",3);
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% [Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
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% toc
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Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(Eq_signal);
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[~, errors, ber, ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
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fprintf('FFE: %.2e \n',ber);
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Vit_signal_ = Vit_signal;
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Vit_signal_.signal = circshift(Vit_signal.signal,0);
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Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(Vit_signal_);
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[~, errors, ber, errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
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fprintf('Viterbi: %.2e \n',ber);
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%% optimize smth.
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tr_len = 2.^[2:15];
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tr_len = floor(tr_len);
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ber = zeros(size(tr_len));
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parfor m = 1:numel(tr_len)
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mu_lms = 0.0005;
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eq = ML_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",tr_len(m));
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[Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
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% Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(Eq_signal);
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% [~, errors, ber(m), ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
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Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(Vit_signal);
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[~, errors, ber(m), errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
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end
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figure(5); hold on
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title('1-SPS')
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plot(tr_len,ber,'DisplayName','BER');
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xlabel('Traceback Length')
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beautifyBERplot
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legend
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ylim([1e-4, 1e-1]);
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set(gca,'YScale','log');
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%% RUN Comparison
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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_ffe2 mu_ffe3];
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mu_dfe = 0.0004;
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pf_ncoeffs = 1;
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ffe_order = [50, 0, 0];
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mu_lms = 0.0005;
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eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",1,"dd_mode",1,"adaption_technique","lms");
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% eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",1,"DCmu",0.00,"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',0,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0);
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[ffe_results{r}, mlse_results_lin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_1sps, Symbols, Tx_bits, ...
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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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mlse_results_lin{r}.metrics.print;
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ffe_results{r}.metrics.print;
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end
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