FSO analysis:
- minimal changes in several files - new Timing Rec from Magnus
This commit is contained in:
Silas Oettinghaus
@@ -1,15 +1,15 @@
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base = "C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC";
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base = "C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC";
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mode = 0; %0 oder 1
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M = 2;
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all_files = dir(fullfile(base, "**/*.mat"));
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if M == 2
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tx_data = load("C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\14G_PAM2\tx_info\tx_info_PAM2_14Gbd0.75RRC.mat");
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tx_data = load("C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\14G_PAM2\tx_info\tx_info_PAM2_14Gbd0.75RRC.mat");
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filename = fullfile(base, "14G_PAM2\M=2_Rs=1.4e10_Fs=8e10_I=265mA_RoP=46.3mW_L=31m_PS=RRC_rolloff=0.75_Mode=Rise.mat");
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elseif M == 4
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tx_data = load("C:\Users\Silas\Nextcloud\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\6G_PAM4\tx_info\tx_info_PAM4_6Gbd0.6RRC.mat");
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tx_data = load("C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC\6G_PAM4\tx_info\tx_info_PAM4_6Gbd0.6RRC.mat");
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filename = fullfile(base, "6G_PAM4\M=4_Rs=6e9_Fs=8e10_I=255mA_RoP=42.3mW_L=31m_PS=RRC_rolloff=0.6_Mode=Rise.mat");
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end
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@@ -53,10 +53,14 @@ Bits_ = PM.demap(Symbols);
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assert(ber == 0);
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%% For comparison, apply pulsef on Tx Symbols
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Pform = Pulseformer("fsym",fsym,"fdac",fs,"pulse","rc","pulselength",16,"alpha",rolloff);
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Digi_sig_compare = Pform.process(Symbols);
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MF = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
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Rx_sig_compare = MF.process(Digi_sig_compare);
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Pform = Pulseformer("fsym",fsym,"fdac",fs,"pulse","rrc","pulselength",16,"alpha",rolloff);
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Digi_sig_tx_compare = Pform.process(Symbols);
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Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
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Digi_sync = Pform.process(Symbols);
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MF = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff);
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Rx_sig_compare = MF.process(Digi_sig_tx_compare);
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%%
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@@ -68,53 +72,47 @@ scoperead_volts = (traceData.RawData - traceData.YReference) * traceData.YIncrem
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demystified = isequal(traceData.YData,scoperead_volts);
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assert(demystified);
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timesig_compare = [0:1:datas.tr.lastData(1).trace.ch3.Points-1] ./ fs;
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timesig = datas.tr.lastData(1).trace.ch3.XData;
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assert(isequal(traceData.YData,scoperead_volts));
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Scope_sig = Electricalsignal(traceData.YData,"fs",fs);
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Scope_sig.plot("displayname",'raw','fignum',100);
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Scope_sig.spectrum("displayname",'raw','fignum',101)
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%%
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% 1) matched filter
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% pulse is symmetric, hence we can use pulsef firectly as matched filter.
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% pulse is symmetric, hence we can use pulsef directly as matched filter.
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% It feels off (bit I think correct) that the fsym is now the output freq.!!
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% -> output 2 sps to omit timing recovery!?
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Pform = Pulseformer("fsym",fsym,"fdac",2*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1);
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Rx_matched = Pform.process(Scope_sig);
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Rx_matched.spectrum("displayname",'Signal after matched filter','fignum',1);
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apply_matched_filter = 0;
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k = 4;
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if apply_matched_filter
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Pform = Pulseformer("fsym",fsym,"fdac",k*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1);
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Rx_matched = Pform.process(Scope_sig);
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else
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Rx_matched = Filter('filtdegree',4,"f_cutoff",fsym*0.5,"fs",Scope_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scope_sig);
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Rx_matched = Rx_matched.resample("fs_out",k*fsym);
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end
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Rx_matched.spectrum();
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%%
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sys = comm.SymbolSynchronizer('TimingErrorDetector', 'Gardner (non-data-aided)', ...
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'SamplesPerSymbol', 2, ...
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'DampingFactor', 0.7, ...
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'NormalizedLoopBandwidth', 0.01);
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Rx_symbolsync = Rx_matched;
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[Rx_symbolsync.signal, timing_error] = sys(Rx_matched.signal);
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plot(timing_error); % If this is a ramp, you have drift!
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%% timing sync -> at this point we still have no symbol timing recovery, we
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% % try to do this with 2sps EQ!
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[~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_symbolsync.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 1);
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%% not working..
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Rx_synced = Rx_synced_cell{1};
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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_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
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mu_dfe = 0.0004;
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duob_mode = db_mode.no_db;
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Rx_synced.plot("displayname",'RX: Matched+Sync+2sps','fignum',2);
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Digi_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped','fignum',1,'normalizeTo0dB',0);
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Rx_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped + matched filtered ','fignum',1,'normalizeTo0dB',0);
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Rx_synced.normalize("mode","rms").spectrum("displayname",'RX: matched filtered + synced','fignum',1,'normalizeTo0dB',0);
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%% Timing Rec
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apply_timing_rec = 1;
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if apply_timing_rec
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[Rx_symbolsync, timing_error] = Timing_Recovery("timing_error_detector",'Gardner (non-data-aided)','sps',k,'damping_factor',0.1,'normalized_loop_bandwidth',0.1,'detector_gain',2.7).process(Rx_matched);
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figure();plot(timing_error);
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Rx_symbolsync.fs = fsym;
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% Tsynch
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[~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_symbolsync.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
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Rx_synced = Rx_synced_cell{1};
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sps = 1;
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else
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% Tsynch
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[~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_matched.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
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Rx_synced = Rx_synced_cell{1};
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Rx_synced = Rx_synced.resample("fs_out",2*fsym);
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sps = 2;
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end
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if M == 2
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ber_in_paper = 10^(-2.6); %fig 3a) 4 Gb/s MWIR FSO Transmission using Directly Modulated QCL and an Uncooled UTC-PD at Room-Temperature
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@@ -122,50 +120,121 @@ elseif M == 4
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ber_in_paper = 10^(-2.5);
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end
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%% -------------------- FFE --------------------
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% requires some more digging what is going on :-)
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eq_ffe = EQ("Ne",[50, 5, 5],"Nb",[2,0,0], ...
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"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
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"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
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%%
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Rx_synced = Rx_synced_cell{1};
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% -------------------- FFE --------------------
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% requires some more digging what is going on :-)
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eq_ffe = EQ("Ne",[50, 1, 1],"Nb",[2,0,0], ...
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"training_length",512,"training_loops",5,"dd_loops",5, ...
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"K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
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"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
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ffe_results = ffe(eq_ffe,M,Rx_synced,Symbols,Bits, ...
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"precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
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"eth_style_symbol_mapping",mapping_style);
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vars = logspace(-4,-3,36);
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% ffe_results.metrics.print
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fprintf('My EQ: %.1e \n',ffe_results.metrics.BER);
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parfor i = 1:numel(vars)
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len_tr = 4096;
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mu_ffe1 = 0.01;% mus(i);%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_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
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mu_dfe = vars(i);
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duob_mode = db_mode.no_db;
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% requires some more digging what is going on :-)
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eq_ffe_1 = EQ("Ne",[150, 1, 0],"Nb",[50,0,0], ...
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"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
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"K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
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"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
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eq_ffe_2 = FFE("epochs_tr",1,"epochs_dd",vars(i),"len_tr",4096,"mu_dd",vars(i),"mu_tr",vars(i),"order",999,"sps",1,"decide",0, "adaption",adaption_method.nlms,"dd_mode",0);
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% eq_ffe_2 = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"ffe_mu_dd",1e-5,"dfe_mu_dd",mus(i),"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",50,"dfe_order",10,"sps",1,"decide",1);
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ffe_results = ffe(eq_ffe_1,M,Rx_synced,Symbols,Bits, ...
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"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
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"eth_style_symbol_mapping",mapping_style);
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ffe_results.metrics.BER
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bers(i) = ffe_results.metrics.BER;
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end
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figure();
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plot(vars,bers);
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yline(ber_in_paper)
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beautifyBERplot();
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%
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fprintf('Paper: %.1e \n \n',ber_in_paper);
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ffe_results.metrics.print("description",'FFE');
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fprintf('FFE: %.1e \n',ffe_results.metrics.BER);
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%% -------------------- VNLE + MLSE --------------------
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len_tr = 4096;
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mu_ffe1 = 0.0001;% mus(i);%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_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
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mu_dfe = 0.0004;
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duob_mode = db_mode.no_db;
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pf_ncoeffs = 1;
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eq_v = EQ("Ne",[100, 5, 5],"Nb",[0, 0, 0], ...
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pf_ncoeffs = 4;
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vars = 1:7;
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bers = zeros(size(vars));
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parfor i = 1:numel(vars)
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eqv = EQ("Ne",[200, 1, 0],"Nb",[2, 0, 0], ...
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"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
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"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
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"K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
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"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,"eth_style",mapping_style).levels);
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[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Rx_synced, Symbols, Bits, ...
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"precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
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mlse_results.metrics.print("description",'MLSE')
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fprintf('My EQ: %.1e \n',mlse_results.metrics.BER);
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fprintf('Paper: %.1e \n \n',ber_in_paper);
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pf_ncoeffs = vars(i);
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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,"eth_style",mapping_style).levels);
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[vnle_results, mlse_results] = vnle_postfilter_mlse(eqv, pf, mlse_, M, Rx_synced, Symbols, Bits, ...
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"precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
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fprintf('Paper: %.1e \n \n',ber_in_paper);
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vnle_results.metrics.print("description",'VNLE');
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mlse_results.metrics.print("description",'MLSE');
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bers(i) = mlse_results.metrics.BER;
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end
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%%
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figure();hold on
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plot(vars,bers_ffe,'DisplayName','FFE [200,0,0] + PF + MLSE');
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plot(vars,bers_vnle,'DisplayName','VNLE [200,1,0] + PF + MLSE');
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plot(vars,bers_vnledfe,'DisplayName','VNLE [200,1,0] + DFE [2] + PF + MLSE');
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plot(vars,bers_vnledfe_ideal,'DisplayName','VNLE [200,1,0] + ideal DFE [2] + PF + MLSE');
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yline(ber_in_paper);
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yline([2e-2, 4.85e-3, 3.8e-3, 2,2e-4],'LineWidth',2,'Color',[0.8,0.8,0.8],'LineStyle',':','HandleVisibility','off');
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ylim([1e-5,0.1]);
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beautifyBERplot();
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%% -------------------- DB target --------------------
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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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eq_ = EQ("Ne",[50, 5, 5],"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
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"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
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"K",sps,"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_synced, Symbols, Bits, ...
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"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", [],"eth_style_symbol_mapping",mapping_style);
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dbt_results.metrics.print("description",'Duobinary');
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mlse_results.metrics.print
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fprintf('My EQ: %.1e \n',dbt_results.metrics.BER);
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fprintf('Paper: %.1e \n \n',ber_in_paper);
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dbt_results.metrics.print("description",'Duobinary');
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%%
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%ML-based MLSE (L=2)
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mu_ml = 0.01; training_epochs = 100;
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ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
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"len_tr",len_tr,"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",1, ...
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"traceback_depth",128,"L",3,"delta",4,"adaptive_mu",0);
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[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode);
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ml_mlse_results.metrics.print("description",'ML ');
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