1bf17ec6be
- try to analyze spectrum of FSO and use the coeffs in the ML analysis. simple channel coeff + awgn example
252 lines
9.4 KiB
Matlab
252 lines
9.4 KiB
Matlab
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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\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\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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if mode == 1
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[f, p] = uigetfile(fullfile(base, "**/*.mat"));
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if f~=0
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filename = fullfile(p,f);
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end
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end
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datas = load(filename);
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%%
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str = filename;
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M_ = str2double(regexp(str, 'M=([^_]+)', 'tokens', 'once'));
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assert(M==M_);
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fsym = str2double(regexp(str, 'Rs=([^_]+)', 'tokens', 'once'));
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fs = str2double(regexp(str, 'Fs=([^_]+)', 'tokens', 'once'));
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I = sscanf(char(regexp(str, 'I=([^_]+)', 'tokens', 'once')), '%f');
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rop = sscanf(char(regexp(str, 'RoP=([^_]+)', 'tokens', 'once')), '%f');
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L = sscanf(char(regexp(str, 'L=([^_]+)', 'tokens', 'once')), '%f');
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pulseshape = string( regexp(str, 'PS=([^_]+)', 'tokens', 'once'));
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rolloff = str2double(regexp(str, 'rolloff=([^_]+)', 'tokens', 'once'));
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mode = string( regexp(str, 'Mode=([^\.]+)', 'tokens', 'once'));
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%%
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% Tx data
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Bits = Informationsignal(tx_data.tx_data,"fs",fsym);
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Symbols = Informationsignal(real(tx_data.tx_PAM_sym),"fs",fsym);
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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
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PM = PAMmapper(M,0,"eth_style",mapping_style); % one should rename "eth style" as this is simply a different mapping scheme
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Symbols_ = PM.map(Bits) .* PM.scaling;
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assert(isequal(Symbols.signal,Symbols_.signal));
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Bits_ = PM.demap(Symbols);
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[bits,errors,ber,errorIndice] = calc_ber(Bits_.signal,Bits.signal);
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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","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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% Rx Data
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traceData = datas.tr.lastData(2).trace.ch3;
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%FYI: Voltage=(RawData−YReference)×YIncrement+YOrigin
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scoperead_volts = (traceData.RawData - traceData.YReference) * traceData.YIncrement + traceData.YOrigin;
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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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%%
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% 1) 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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apply_matched_filter = 1;
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k = 1;
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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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coefficients = arburg(Rx_matched.signal,25);
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figure()
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[h,w] = freqz(1,coefficients,Rx_matched.length,"whole",Rx_matched.fs);
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h = h/max(abs(h));
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hold on
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w_ = (w - Rx_matched.fs/2);
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plot(w_.*1e-9,20*log10(fftshift(abs(h))),'DisplayName',['Burg Coeffs: ', num2str(round(coefficients,2)), ' '],'LineWidth',2);
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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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elseif M == 4
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ber_in_paper = 10^(-2.5);
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end
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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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vars = logspace(-4,-3,36);
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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 = 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",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_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",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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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 '); |