ML_MLSE (before testing in depth)
minor changes here and there
This commit is contained in:
Silas Oettinghaus
@@ -5,7 +5,7 @@ db = DBHandler("dataBase", [dataBase], "type", database_type);
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M = 4;
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fp = QueryFilter();
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% fp.where('Runs', 'run_id','EQUALS', 987);
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fp.where('Runs', 'pam_level','EQUALS', M);
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% fp.where('Runs', 'pam_level','EQUALS', M);
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% fp.where('Runs', 'symbolrate','EQUALS', 150e9);
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% fp.where('Runs', 'fiber_length','EQUALS', 10);
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fp.where('Runs', 'is_mpi','EQUALS', 0);
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@@ -20,10 +20,11 @@ fields = db.getTableFieldNames('power_state_info');
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fields = [fields; db.getTableFieldNames('dashboard_ungrouped_new')];
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[dataTable,~] = db.queryDB(fp, fields);
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%%
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cfg = struct;
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cfg.x_axis = 'accumulated_dispersion'; % 'symbolrate' | 'baudrate' | 'bitrate' | 'wavelength'
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cfg.y_axis = 'BER'; % 'BER' | 'GMI' | 'AIR' | ...
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cfg.group_by = {'equalizer_structure','pre_emph'};
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cfg.x_axis = 'wavelength'; % 'symbolrate' | 'baudrate' | 'bitrate' | 'wavelength'
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cfg.y_axis = 'power_mzm'; % 'BER' | 'GMI' | 'AIR' | ...
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cfg.group_by = {};
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cfg.filters = struct('is_mpi',0,'pam_level',M,'equalizer_structure',[equalizer_structure.vnle,equalizer_structure.ffe,equalizer_structure.vnle_pf_mlse,equalizer_structure.vnle_db_mlse,equalizer_structure.dfe]);
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cfg.y_scale = 'auto'; % auto -> log for BER*, linear otherwise
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181
projects/IMDD_base_system/model_linewidth_evaluation.m
Normal file
181
projects/IMDD_base_system/model_linewidth_evaluation.m
Normal file
@@ -0,0 +1,181 @@
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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 = 1300;
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laser_linewidth = logspace(0,6.2,24);
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% Channel
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link_length = 10;
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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 = [200:16:256].*1e9;
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% nonlin_mod = [0.5:0.01:0.75];
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fsym = ones(size(laser_linewidth)).*fsym(1);
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nonlin_mod = ones(size(laser_linewidth)).*0.5;
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ffe_results = {};
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mlse_results_lin= {};
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parfor r = 1:length(laser_linewidth)
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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",18,"useprbs",1,...
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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(r),"randomkey",random_key+1).process(El_sig);
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%%%%%% Fiber %%%%%%
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mpi = 1;
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if mpi
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Combined_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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else
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% 2) ping pong fiber propagation
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mpi_path = 00;
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Interference_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",mpi_path*2,"alpha",0,"D",0,"lambda0",1310,"gamma",0).process(Opt_sig);
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Interference_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",-30).process(Interference_sig);
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[Main_sig,dly] = Opt_sig.delay("delay_meter",mpi_path*2);
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% Add
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Combined_sig = Main_sig + Interference_sig;
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% Cut (due to the delays there is a jump in the signals)
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if dly == 0;dly = 1;end
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Combined_sig.signal = Combined_sig.signal(ceil(dly):end);
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% Fiber
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Combined_sig = Fiber("fsimu",Combined_sig.fs,"fiber_length",2,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.08).process(Combined_sig);
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end
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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(Combined_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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% Symbols.signal = Symbols.signal(1:Scpe_sig_2sps.length/2);
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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", 0);
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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", 0);
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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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%% RUN DSP
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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, 3, 3];
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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",2,"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_2sps, 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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ffe_results{r}.metrics.print;
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mlse_results_lin{r}.metrics.print;
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end
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figure(1);hold on;
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plot(laser_linewidth.*1e-6,cellfun(@(x) x.metrics.BER, ffe_results),'DisplayName','VNLE')
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plot(laser_linewidth.*1e-6,cellfun(@(x) x.metrics.BER, mlse_results_lin),'DisplayName','VNLE+MLSE')
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xlabel('Linewidth [GHz]');
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ylabel('BER')
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set(gca,'YScale','log');
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legend;
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ylim([1e-5 1e-1]);
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beautifyBERplot;
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figure();hold on;
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plot(laser_linewidth.*1e-6,cellfun(@(x) x.metrics.AIR.*1e-9, ffe_results),'DisplayName','FFE')
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plot(laser_linewidth.*1e-6,cellfun(@(x) x.metrics.AIR.*1e-9, mlse_results_lin),'DisplayName','FFE+MLSE')
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xlabel('Linewidth [GHz]');
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ylabel('AIR [GBd]')
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% set(gca,'YScale','log');
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legend;
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beautifyBERplot("logscale",0);
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figure(); hold on
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stem(calcWavelengthPlan(16,400e9,1310),ones(16,1),'DisplayName','16x400','Marker','.','LineWidth',1);
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stem(calcWavelengthPlan(8,800e9,1310),ones(8,1),'DisplayName','8x800','Marker','.','LineWidth',1);
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stem(calcWavelengthPlan(16,800e9,1310),ones(16,1),'DisplayName','16x800','Marker','.','LineWidth',1);
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ylim([0,1.2]);
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ylabel('wavelength [nm]');
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xlim([1270, 1350])
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@@ -332,12 +332,14 @@ for r = 1:length(fsym)
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plot_stuff = 0;
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gmi_mlse_pr_tgt_ = zeros(size(alpha_vec));
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ber_mlse_pr_tgt_ = zeros(size(alpha_vec));
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parfor a = 1:numel(alpha_vec)
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for a = 1:numel(alpha_vec)
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alpha_vec(a) = 0.9;
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eq_ = EQ("Ne",[vnle_order1,vnle_order2,vnle_order3],"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",0.00,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
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Symbols_filt = Symbols.filter([1,alpha_vec(a)],1);
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[eq_signal_prtgt, eq_noise] = eq_.process(Rx_sig, Symbols_filt);
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showLevelHistogram(eq_signal_prtgt,Symbols_filt,"displayname",'VNLE Out','fignum',201);
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if plot_stuff
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% Plot the response for respective EQ targets
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213
projects/ML_based_MLSE/minimal_model_gpt.m
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213
projects/ML_based_MLSE/minimal_model_gpt.m
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@@ -0,0 +1,213 @@
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function minimal_model_gpt
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% Minimal binary IM/DD + ISI (M=3), AWGN. RX: ML-based MLSE vs classic MLSE
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% Fixes:
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% 1) Correct (s',s) labeling: s'=[x_{k-1},x_{k-2}], s=[x_k,x_{k-1}]
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% 2) Window standardization (z-score) from pilot
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% 3) Non-negative learned BM: c_hat = (w^T y_std + b).^2
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% 4) Stable training loop bounds; BER length alignment
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clear; clc; rng(1);
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%% Parameters
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Ktr = 4000; % pilot
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Kte = 20000; % test
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M = 3; % true channel memory
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L = 3; % MLSE memory (states keep L-1 symbols)
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h = [0.55 1.00 0.45]; % ISI FIR
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EsN0dB= 12;
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D = 5*L; % traceback (unused here; full TB)
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Nwin = 7; % BM feature window
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Delta = 0;
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%% TX, channel, noise
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map = @(b) 2*b-1;
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b_tr = randi([0 1],Ktr,1); x_tr = map(b_tr);
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b_te = randi([0 1],Kte,1); x_te = map(b_te);
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convpad = @(x) filter(h,1,[x; zeros(M-1,1)]);
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ytr_clean = convpad(x_tr);
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yte_clean = convpad(x_te);
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EsN0 = 10^(EsN0dB/10);
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sigma2 = 1/(2*EsN0);
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awgn = @(len) sqrt(sigma2)*randn(len,1);
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y_tr = ytr_clean + awgn(length(ytr_clean));
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y_te = yte_clean + awgn(length(yte_clean));
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% remove tail to keep equal lengths
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trim = M-1;
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y_tr = y_tr(1:end-trim); x_tr = x_tr(1:end-trim); b_tr = b_tr(1:end-trim);
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y_te = y_te(1:end-trim); x_te = x_te(1:end-trim); b_te = b_te(1:end-trim);
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%% Trellis
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nStates = 2^(L-1);
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statesBin = de2bi(0:nStates-1,L-1,'left-msb');
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symOfBit = @(bit) (2*bit-1);
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% transitions (s'->s) with input symbol u in {±1}
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trans = struct('sp',[],'s',[],'u',[],'ubit',[]);
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idx=1;
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for sp=1:nStates
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prev_bits = statesBin(sp,:); % [x_{k-1}, x_{k-2}] as bits
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for ubit=[0 1]
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u = symOfBit(ubit);
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nb = [prev_bits(1),ubit]; % new state s = [x_k, x_{k-1}]
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s = bi2de(nb,'left-msb')+1;
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trans(idx).sp=sp; trans(idx).s=s; trans(idx).u=u; trans(idx).ubit=ubit;
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idx=idx+1;
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end
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end
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nTrans = numel(trans);
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%% Classic BM (Gaussian)
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BM_classic = @(yk, sp, s, u) (( yk - h * [u, symOfBit(statesBin(sp,:))]')^2)/(2*sigma2);
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%% ML-based BM: c_hat = (w^T y_std + b)^2 (nonnegative)
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padN = Nwin-1;
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pad = @(y) [zeros(Delta+padN,1); y; zeros(max(0,Nwin-1-Delta),1)];
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y_tr_p = pad(y_tr); y_te_p = pad(y_te);
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% standardize window features using pilot
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Ytr_mat = im2col_sliding(y_tr_p, Nwin, Delta); % Nwin × T
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mu_win = mean(Ytr_mat,2);
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sd_win = std(Ytr_mat,0,2)+1e-8;
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W = zeros(Nwin,nTrans);
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b0= zeros(1,nTrans);
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softmax = @(z) exp(z - max(z))./sum(exp(z - max(z)));
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mu = 0.005; % LR
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nEpoch = 6; % light training
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Ttr = length(y_tr);
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for ep=1:nEpoch
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v = zeros(nStates,1); v(2:end)=Inf;
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for k = L : min(Ttr - (L-1), Ttr) % need x(k-1), x(k-2)
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% true (s',s):
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sp_bits = [(x_tr(k-1)<0), (x_tr(k-2)<0)];
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s_bits = [(x_tr(k) <0), (x_tr(k-1)<0)];
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sp_star = bi2de(sp_bits,'left-msb')+1;
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s_star = bi2de(s_bits ,'left-msb')+1;
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% feature window (z-scored)
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yw = y_tr_p(k-Delta : k-Delta+Nwin-1);
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yw = (yw - mu_win) ./ sd_win;
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% compute extended PM logits over all (s',s)
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vtil = -inf(nTrans,1);
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for t=1:nTrans
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z = W(:,t).'*yw + b0(t);
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c_hat = z*z; % (.)^2 nonnegative BM
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vtil(t) = -( v(trans(t).sp) + c_hat);
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end
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pext = softmax(vtil).';
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t_star = find([trans.sp]==sp_star & [trans.s]==s_star,1);
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% CE gradient wrt c_hat, chain rule through square
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delta = pext; delta(t_star)=delta(t_star)-1; delta = -delta; % sign for vtil=-(...)
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for t=1:nTrans
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z = W(:,t).'*yw + b0(t);
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dc_dz = 2*z; % d(z^2)/dz
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g = delta(t) * dc_dz; % ∂L/∂z
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W(:,t) = W(:,t) - mu * g * yw;
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b0(t) = b0(t) - mu * g;
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end
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% PM update for next step
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v_next = inf(nStates,1);
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for t=1:nTrans
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sp=trans(t).sp; s=trans(t).s;
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z = W(:,t).'*yw + b0(t);
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c_hat = z*z;
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cand = v(sp)+c_hat;
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if cand < v_next(s)
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v_next(s)=cand;
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end
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end
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v = v_next;
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end
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end
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%% Decode (full-length traceback)
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[xhat_ml, bhat_ml] = viterbi_decode(y_te, L, trans, @(k) feat_std(y_te_p,k,Nwin,Delta,mu_win,sd_win), ...
|
||||
@(yk,sp,s,u) learnedBM(W,b0,yk,sp,s,u), 0);
|
||||
[xhat_ex, bhat_ex] = viterbi_decode(y_te, L, trans, @(k) y_te(k), ...
|
||||
@(yk,sp,s,u) BM_classic(yk,sp,s,u), 0);
|
||||
|
||||
%% BER (align by min length)
|
||||
Lmin = min([length(bhat_ml), length(bhat_ex), length(b_te)]);
|
||||
BER_ml = mean(bhat_ml(1:Lmin) ~= (b_te(1:Lmin)>0));
|
||||
BER_ex = mean(bhat_ex(1:Lmin) ~= (b_te(1:Lmin)>0));
|
||||
fprintf('BER (ML-based MLSE): %.3e\n', BER_ml);
|
||||
fprintf('BER (classic MLSE ): %.3e\n', BER_ex);
|
||||
end
|
||||
|
||||
% ----------------- helpers -----------------
|
||||
function Y = im2col_sliding(y_pad, Nwin, Delta)
|
||||
T = length(y_pad) - (Nwin-1) - Delta;
|
||||
Y = zeros(Nwin,T);
|
||||
for k=1:T
|
||||
Y(:,k) = y_pad(k-Delta : k-Delta+Nwin-1);
|
||||
end
|
||||
end
|
||||
|
||||
function yw = feat_std(y_pad,k,Nwin,Delta,mu_win,sd_win)
|
||||
yw = y_pad(k-Delta : k-Delta+Nwin-1);
|
||||
yw = (yw - mu_win) ./ sd_win;
|
||||
end
|
||||
|
||||
function c = learnedBM(W,b0,yw,sp,s,~)
|
||||
% pick parameters of the (sp->s) transition
|
||||
persistent map;
|
||||
if isempty(map)
|
||||
% build once: index of W/b0 for each (sp,s)
|
||||
nStates = size(W,1)*0+1; %#ok<NASGU>
|
||||
end
|
||||
% linear scan is fine at this size:
|
||||
% (use first match of (sp,s))
|
||||
c = inf;
|
||||
for t=1:size(W,2)
|
||||
% suppose we stored (sp,s) order as in training; we cannot access here.
|
||||
% Instead, pass the exact column via a small mapper (build each call):
|
||||
end
|
||||
% Faster: precompute a table outside; here we reconstruct like in training
|
||||
% (rebuild tiny mapper)
|
||||
persistent key_sp key_s
|
||||
if isempty(key_sp)
|
||||
key_sp = evalin('caller','[trans.sp]');
|
||||
key_s = evalin('caller','[trans.s]');
|
||||
end
|
||||
t = find(key_sp==sp & key_s==s,1);
|
||||
z = W(:,t).'*yw + b0(t);
|
||||
c = z*z;
|
||||
end
|
||||
|
||||
function [xhat, bhat] = viterbi_decode(y, L, trans, getFeat, BMfun, D_unused)
|
||||
nStates = 2^(L-1);
|
||||
T = length(y);
|
||||
v = inf(nStates,T); v(:,1)=Inf; v(1,1)=0;
|
||||
prev = zeros(nStates,T); in_u = zeros(nStates,T);
|
||||
|
||||
for k=1:T
|
||||
if k==1, vprev=inf(nStates,1); vprev(1)=0; else, vprev=v(:,k-1); end
|
||||
vcur = inf(nStates,1); pre=zeros(nStates,1); inb=zeros(nStates,1);
|
||||
|
||||
feat = getFeat(k); % either scalar y(k) or standardized window
|
||||
for t=1:numel(trans)
|
||||
sp=trans(t).sp; s=trans(t).s; u=trans(t).u;
|
||||
ck = BMfun(feat, sp, s, u);
|
||||
cand = vprev(sp)+ck;
|
||||
if cand < vcur(s)
|
||||
vcur(s)=cand; pre(s)=sp; inb(s)=u;
|
||||
end
|
||||
end
|
||||
v(:,k)=vcur; prev(:,k)=pre; in_u(:,k)=inb;
|
||||
end
|
||||
|
||||
[~,st]=min(v(:,T));
|
||||
xhat=zeros(T,1);
|
||||
for k=T:-1:1
|
||||
xhat(k)=in_u(st,k);
|
||||
st=prev(st,k); if st==0, st=1; end
|
||||
end
|
||||
bhat = xhat>0;
|
||||
end
|
||||
@@ -22,9 +22,9 @@ alpha = 0;
|
||||
|
||||
doub_mode = db_mode.no_db;
|
||||
cols = linspecer(6);
|
||||
rop = [-6];
|
||||
rop = [-5];
|
||||
bwl = [0.5:0.1:1.5];
|
||||
fsym = [208:16:256].*1e9;
|
||||
fsym = [212:16:256].*1e9;
|
||||
% nonlin_mod = [0.5:0.01:0.75];
|
||||
nonlin_mod = ones(size(fsym)).*0.5;
|
||||
|
||||
@@ -41,7 +41,7 @@ for r = 1:length(fsym)
|
||||
apply_pulsef = 1;
|
||||
|
||||
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
|
||||
"fsym",fsym(r),"M",M,"order",17,"useprbs",0,...
|
||||
"fsym",fsym(r),"M",M,"order",18,"useprbs",0,...
|
||||
"fs_out",fdac,...
|
||||
"applyclipping",0,"clipfactor",1.5,...
|
||||
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||
@@ -94,59 +94,87 @@ for r = 1:length(fsym)
|
||||
[~, Scpe_cell_1sps, ~, found_sync] = Scpe_sig_1sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 1);
|
||||
Rx_sig_1sps = Scpe_cell_1sps{1};
|
||||
Rx_sig_1sps = Rx_sig_1sps.normalize("mode","rms");
|
||||
showLevelHistogram(Rx_sig_1sps,Symbols,"displayname",'ffe','fignum',111);
|
||||
|
||||
|
||||
%% Implement DSP directly here:
|
||||
%%
|
||||
|
||||
mu_lms = 0.0005;
|
||||
% tic
|
||||
% 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",1);
|
||||
% [Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
|
||||
% toc
|
||||
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"dd_mode",1,"adaption_technique","lms");
|
||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||
|
||||
% FFE
|
||||
[y_ffe, ffe_noise] = eq_.process(Rx_sig_2sps, Symbols);
|
||||
|
||||
Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ffe);
|
||||
[~, errors, ber_ffe, ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
fprintf('FFE: %.2e \n',ber_ffe);
|
||||
|
||||
tic
|
||||
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);
|
||||
[Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
|
||||
toc
|
||||
|
||||
Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(Eq_signal);
|
||||
[~, errors, ber, ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
fprintf('FFE: %.2e \n',ber);
|
||||
% Postfilter
|
||||
[y_white,whitened_noise] = pf_.process(y_ffe, ffe_noise);
|
||||
|
||||
Vit_signal_ = Vit_signal;
|
||||
Vit_signal_.signal = circshift(Vit_signal.signal,0);
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(Vit_signal_);
|
||||
[~, errors, ber, errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
fprintf('Viterbi: %.2e \n',ber);
|
||||
% Sequence Est
|
||||
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,'DIR',pf_.coefficients);
|
||||
[y_mlse] = mlse_.process(y_white,Symbols);
|
||||
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_mlse);
|
||||
[~, errors, ber_mlse_normal, errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
|
||||
fprintf('MLSE: %.2e \n',ber_mlse_normal);
|
||||
|
||||
showLevelHistogram(y_ffe,Symbols,"displayname",'ffe','fignum',111);
|
||||
% showLevelHistogram(y_white,Symbols,"displayname",'ffe','fignum',111);
|
||||
|
||||
%% optimize length
|
||||
|
||||
mu_lms = 0.15;
|
||||
eq = ML_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^14,...
|
||||
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",5,"sps",2,...
|
||||
"traceback_depth",128,"L",2,"delta",0);
|
||||
|
||||
[y_ml_mlse,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
|
||||
y_ml_mlse_ = y_ml_mlse;
|
||||
y_ml_mlse_.signal = circshift(y_ml_mlse.signal,0);
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse_);
|
||||
[~, errors, ber, errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 10, "skip_end", 10, "returnErrorLocation", 1);
|
||||
fprintf('ML MLSE: %.2e \n',ber);
|
||||
|
||||
bursts = count_error_bursts(errpos, 10);
|
||||
e = zeros(size(Vit_bits.signal));
|
||||
e(errpos) = 1;
|
||||
figure(8)
|
||||
stem(e)
|
||||
|
||||
%% optimize delta
|
||||
deltas = [-1:4];
|
||||
ber = zeros(1,length(deltas));
|
||||
parfor m = 1:numel(deltas)
|
||||
|
||||
mu_lms = 0.2;
|
||||
eq = ML_MLSE("epochs_tr",2,"epochs_dd",5,"len_tr",2^13,...
|
||||
"mu_dd",mu_lms,"mu_tr",mu_lms,"order",4,"sps",1,...
|
||||
"traceback_depth",128,"L",3,"delta",deltas(m));
|
||||
|
||||
[y_ml_mlse,Vit_signal] = eq.process(y_ffe,Symbols);
|
||||
y_ml_mlse_ = y_ml_mlse;
|
||||
y_ml_mlse_.signal = circshift(y_ml_mlse.signal,0);
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(y_ml_mlse_);
|
||||
[~, errors, ber(m), errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
fprintf('ML MLSE: %.2e \n',ber(m));
|
||||
end
|
||||
|
||||
|
||||
figure(7); hold on
|
||||
title('ML MLSE')
|
||||
plot(deltas,ber,'DisplayName','BER');
|
||||
yline(ber_ffe,'DisplayName','BER FFE');
|
||||
yline(ber_mlse_normal,'DisplayName','BER MLSE');
|
||||
xlabel('deltas')
|
||||
beautifyBERplot
|
||||
legend
|
||||
ylim([1e-5, 1e-1]);
|
||||
set(gca,'YScale','log');
|
||||
|
||||
|
||||
%% optimize smth.
|
||||
tr_len = 2.^[2:15];
|
||||
tr_len = floor(tr_len);
|
||||
ber = zeros(size(tr_len));
|
||||
for m = 1:numel(tr_len)
|
||||
|
||||
mu_lms = 0.0005;
|
||||
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));
|
||||
[Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
|
||||
|
||||
% Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(Eq_signal);
|
||||
% [~, errors, ber(m), ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
|
||||
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(Vit_signal);
|
||||
[~, errors, ber(m), errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
|
||||
end
|
||||
|
||||
figure(5); hold on
|
||||
title('1-SPS')
|
||||
plot(tr_len,ber,'DisplayName','BER');
|
||||
xlabel('Traceback Length')
|
||||
beautifyBERplot
|
||||
legend
|
||||
ylim([1e-4, 1e-1]);
|
||||
set(gca,'YScale','log');
|
||||
|
||||
|
||||
|
||||
%% RUN Comparison
|
||||
@@ -163,12 +191,12 @@ for r = 1:length(fsym)
|
||||
pf_ncoeffs = 1;
|
||||
ffe_order = [50, 0, 0];
|
||||
mu_lms = 0.0005;
|
||||
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");
|
||||
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"dd_mode",1,"adaption_technique","lms");
|
||||
% 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);
|
||||
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
|
||||
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);
|
||||
|
||||
[ffe_results{r}, mlse_results_lin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_1sps, Symbols, Tx_bits, ...
|
||||
[ffe_results{r}, mlse_results_lin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_2sps, Symbols, Tx_bits, ...
|
||||
"precode_mode", duob_mode,...
|
||||
'showAnalysis', 0, ...
|
||||
"postFFE", [],...
|
||||
|
||||
Reference in New Issue
Block a user