<<<<<<< HEAD base = "C:\Users\magf\Desktop\Desktop\MATLAB-Zeugs\FSO Equalizer\FSO_FP_QCL_60umUTC\"; ======= base = "C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\FSO_FP_QCL_60umUTC"; >>>>>>> 719e5508e776c18e3dcf72a954c71bdedda179e0 mode = 0; %0 oder 1 M = 2; all_files = dir(fullfile(base, "**/*.mat")); if M == 2 <<<<<<< HEAD tx_data_path = fullfile(base, "14G_PAM2\tx_info\tx_info_PAM2_14Gbd0.75RRC.mat"); 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"); elseif M == 4 tx_data_path = fullfile(base, "6G_PAM4\tx_info\tx_info_PAM4_6Gbd0.6RRC.mat"); ======= 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"); 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"); elseif M == 4 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"); >>>>>>> 719e5508e776c18e3dcf72a954c71bdedda179e0 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"); end if mode == 1 [f, p] = uigetfile(fullfile(base, "**/*.mat")); if f~=0 filename = fullfile(p,f); end end tx_data = load(tx_data_path); datas = load(filename); %% str = filename; M_ = str2double(regexp(str, 'M=([^_]+)', 'tokens', 'once')); assert(M==M_); fsym = str2double(regexp(str, 'Rs=([^_]+)', 'tokens', 'once')); fs = str2double(regexp(str, 'Fs=([^_]+)', 'tokens', 'once')); I = sscanf(char(regexp(str, 'I=([^_]+)', 'tokens', 'once')), '%f'); rop = sscanf(char(regexp(str, 'RoP=([^_]+)', 'tokens', 'once')), '%f'); L = sscanf(char(regexp(str, 'L=([^_]+)', 'tokens', 'once')), '%f'); pulseshape = string( regexp(str, 'PS=([^_]+)', 'tokens', 'once')); rolloff = str2double(regexp(str, 'rolloff=([^_]+)', 'tokens', 'once')); mode = string( regexp(str, 'Mode=([^\.]+)', 'tokens', 'once')); %% % Tx data Bits = Informationsignal(tx_data.tx_data,"fs",fsym); Symbols = Informationsignal(real(tx_data.tx_PAM_sym),"fs",fsym); mapping_style = M==4; % Pam2 is like move-it; PAM-4 is different, same mapping like ETH peopled used in Zurich... hence the "eth_style" argument here and there PM = PAMmapper(M,0,"eth_style",mapping_style); % one should rename "eth style" as this is simply a different mapping scheme Symbols_ = PM.map(Bits) .* PM.scaling; assert(isequal(Symbols.signal,Symbols_.signal)); Bits_ = PM.demap(Symbols); [bits,errors,ber,errorIndice] = calc_ber(Bits_.signal,Bits.signal); assert(ber == 0); %% For comparison, apply pulsef on Tx Symbols Pform = Pulseformer("fsym",fsym,"fdac",fs,"pulse","rrc","pulselength",16,"alpha",rolloff); Digi_sig_tx_compare = Pform.process(Symbols); Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff); Digi_sync = Pform.process(Symbols); MF = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff); Rx_sig_compare = MF.process(Digi_sig_tx_compare); %% % Rx Data traceData = datas.tr.lastData(2).trace.ch3; %FYI: Voltage=(RawData−YReference)×YIncrement+YOrigin scoperead_volts = (traceData.RawData - traceData.YReference) * traceData.YIncrement + traceData.YOrigin; demystified = isequal(traceData.YData,scoperead_volts); assert(demystified); timesig_compare = [0:1:datas.tr.lastData(1).trace.ch3.Points-1] ./ fs; timesig = datas.tr.lastData(1).trace.ch3.XData; assert(isequal(traceData.YData,scoperead_volts)); Scope_sig = Electricalsignal(traceData.YData,"fs",fs); %% % 1) matched filter % pulse is symmetric, hence we can use pulsef directly as matched filter. % It feels off (bit I think correct) that the fsym is now the output freq.!! % -> output 2 sps to omit timing recovery!? apply_matched_filter = 0; k = 4; if apply_matched_filter Pform = Pulseformer("fsym",fsym,"fdac",k*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1); Rx_matched = Pform.process(Scope_sig); else Rx_matched = Filter('filtdegree',4,"f_cutoff",fsym*0.5,"fs",Scope_sig.fs,"filterType",filtertypes.gaussian,"active",true).process(Scope_sig); Rx_matched = Rx_matched.resample("fs_out",k*fsym); end Rx_matched.spectrum(); <<<<<<< HEAD %% sys = comm.SymbolSynchronizer('TimingErrorDetector', 'Gardner (non-data-aided)', ... 'SamplesPerSymbol', 2, ... 'DampingFactor', 0.7, ... 'NormalizedLoopBandwidth', 0.01); Rx_symbolsync = Rx_matched; [Rx_symbolsync.signal, timing_error] = sys(Rx_matched.signal); plot(timing_error); % If this is a ramp, you have drift! %% timing sync -> at this point we still have no symbol timing recovery, we % % try to do this with 2sps EQ! [~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_symbolsync.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 1); % Rx_matched_1 = Rx_synced_cell{1}; % % % Rx_Time_Rec = Rx_matched; % [Rx_Time_Rec, Timing_Error] = Timing_Recovery("modulation", 'PAM/PSK/QAM', "timing_error_detector",'Gardner (non-data-aided)','sps',2,'damping_factor',1,'normalized_loop_bandwidth',0.01,'detector_gain',2.7).process(Rx_matched_1); % figure;plot(Timing_Error); % % Rx_Time_Rec.signal = resample(Rx_Time_Rec.signal, 28e9, 14e9); %% not working.. % Rx_synced = Rx_Time_Rec; Rx_synced = Rx_synced_cell{1}; len_tr = 4096*2; mu_ffe1 = 0.0001; mu_ffe2 = 0.0008; mu_ffe3 = 0.001; mu_dc = 0.005; mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3]; mu_dfe = 0.0004; duob_mode = db_mode.no_db; Rx_synced.plot("displayname",'RX: Matched+Sync+2sps','fignum',2); Digi_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped','fignum',1,'normalizeTo0dB',0); Rx_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped + matched filtered ','fignum',1,'normalizeTo0dB',0); Rx_synced.normalize("mode","rms").spectrum("displayname",'RX: matched filtered + synced','fignum',1,'normalizeTo0dB',0); ======= %% Timing Rec apply_timing_rec = 1; if apply_timing_rec [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); figure();plot(timing_error); Rx_symbolsync.fs = fsym; % Tsynch [~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_symbolsync.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0); Rx_synced = Rx_synced_cell{1}; sps = 1; else % Tsynch [~,Rx_synced_cell,inverted,sequenceFound,sequenceStarts] = Rx_matched.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0); Rx_synced = Rx_synced_cell{1}; Rx_synced = Rx_synced.resample("fs_out",2*fsym); sps = 2; end >>>>>>> 719e5508e776c18e3dcf72a954c71bdedda179e0 if M == 2 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 elseif M == 4 ber_in_paper = 10^(-2.5); end %% Rx_synced = Rx_synced_cell{1}; % -------------------- FFE -------------------- % requires some more digging what is going on :-) eq_ffe = EQ("Ne",[50, 1, 1],"Nb",[2,0,0], ... "training_length",512,"training_loops",5,"dd_loops",5, ... "K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ... "FFEmu",0,"plotfinal",0,"ideal_dfe",1); vars = logspace(-4,-3,36); parfor i = 1:numel(vars) len_tr = 4096; mu_ffe1 = 0.01;% mus(i);%0.0001; mu_ffe2 = 0.0008; mu_ffe3 = 0.001; mu_dc = 0.005; mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3]; mu_dfe = vars(i); duob_mode = db_mode.no_db; % requires some more digging what is going on :-) eq_ffe_1 = EQ("Ne",[150, 1, 0],"Nb",[50,0,0], ... "training_length",len_tr,"training_loops",5,"dd_loops",5, ... "K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ... "FFEmu",0,"plotfinal",0,"ideal_dfe",0); 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); % 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); ffe_results = ffe(eq_ffe_1,M,Rx_synced,Symbols,Bits, ... "precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ... "eth_style_symbol_mapping",mapping_style); ffe_results.metrics.BER bers(i) = ffe_results.metrics.BER; end figure(); plot(vars,bers); yline(ber_in_paper) beautifyBERplot(); % fprintf('Paper: %.1e \n \n',ber_in_paper); ffe_results.metrics.print("description",'FFE'); fprintf('FFE: %.1e \n',ffe_results.metrics.BER); %% -------------------- VNLE + MLSE -------------------- len_tr = 4096; mu_ffe1 = 0.0001;% mus(i);%0.0001; mu_ffe2 = 0.0008; mu_ffe3 = 0.001; mu_dc = 0.005; mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3]; mu_dfe = 0.0004; duob_mode = db_mode.no_db; pf_ncoeffs = 4; vars = 1:7; bers = zeros(size(vars)); parfor i = 1:numel(vars) eqv = EQ("Ne",[200, 1, 0],"Nb",[2, 0, 0], ... "training_length",len_tr,"training_loops",5,"dd_loops",5, ... "K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ... "FFEmu",0,"plotfinal",0,"ideal_dfe",1); pf_ncoeffs = vars(i); pf = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1); mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0,"eth_style",mapping_style).levels); [vnle_results, mlse_results] = vnle_postfilter_mlse(eqv, pf, mlse_, M, Rx_synced, Symbols, Bits, ... "precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style); fprintf('Paper: %.1e \n \n',ber_in_paper); vnle_results.metrics.print("description",'VNLE'); mlse_results.metrics.print("description",'MLSE'); bers(i) = mlse_results.metrics.BER; end %% figure();hold on plot(vars,bers_ffe,'DisplayName','FFE [200,0,0] + PF + MLSE'); plot(vars,bers_vnle,'DisplayName','VNLE [200,1,0] + PF + MLSE'); plot(vars,bers_vnledfe,'DisplayName','VNLE [200,1,0] + DFE [2] + PF + MLSE'); plot(vars,bers_vnledfe_ideal,'DisplayName','VNLE [200,1,0] + ideal DFE [2] + PF + MLSE'); yline(ber_in_paper); yline([2e-2, 4.85e-3, 3.8e-3, 2,2e-4],'LineWidth',2,'Color',[0.8,0.8,0.8],'LineStyle',':','HandleVisibility','off'); ylim([1e-5,0.1]); beautifyBERplot(); %% -------------------- DB target -------------------- mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels); eq_ = EQ("Ne",[50, 5, 5],"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5, ... "K",sps,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1); dbt_results = duobinary_target(eq_,mlse_db_, M, Rx_synced, Symbols, Bits, ... "precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", [],"eth_style_symbol_mapping",mapping_style); fprintf('Paper: %.1e \n \n',ber_in_paper); dbt_results.metrics.print("description",'Duobinary'); %% %ML-based MLSE (L=2) mu_ml = 0.01; training_epochs = 100; ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ... "len_tr",len_tr,"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",1, ... "traceback_depth",128,"L",3,"delta",4,"adaptive_mu",0); [ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode); ml_mlse_results.metrics.print("description",'ML ');