Added:
- Class folder 'Timing Recovery' with different timing recoveries - Minimal example for the timing recovery on the FSO data - New evaluation scripts in the FSO project folder
This commit is contained in:
magf
251
projects/FSO_transmission/FSO_timing_recovery_minimal_example.m
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251
projects/FSO_transmission/FSO_timing_recovery_minimal_example.m
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%%
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clear all;
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close all;
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%% Choose a fitting base leading to the FSO Data
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base = "C:\Users\magf\Desktop\Desktop\MATLAB-Zeugs\FSO Equalizer\FSO_FP_QCL_60umUTC\";
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mode = 0; %0 oder 1
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M = 4;
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all_files = dir(fullfile(base, "**/*.mat"));
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% data_tr_mf is the already recovered and filtered data
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if M == 2
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tx_data_path = fullfile(base, "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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data_tr_mf = load("C:\Users\magf\Desktop\Desktop\MATLAB-Zeugs\FSO Equalizer\FSO_FP_QCL_60umUTC\Already Recovered and Filtered\AfterSync_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_path = fullfile(base, "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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data_tr_mf = load("C:\Users\magf\Desktop\Desktop\MATLAB-Zeugs\FSO Equalizer\FSO_FP_QCL_60umUTC\Already Recovered and Filtered\AfterSync_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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tx_data = load(tx_data_path);
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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_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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%%
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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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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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%Calculate Transfer Function
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Tx_spectrum = Digi_sig_compare;
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Rx_spectrum = Scope_sig;
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[~,Rx_synced_spectrum,inverted_spectrum,sequenceFound_spectrum,sequenceStarts_spectrum] = Rx_spectrum.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 1);
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Rx_spectrum = Rx_synced_spectrum{1};
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Tx_spectrum = Tx_spectrum.resample("fs_out",fsym);
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Tx_spectrum.signal = fft(Tx_spectrum.signal);
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Rx_spectrum = Rx_spectrum.resample("fs_out",fsym);
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Rx_spectrum.signal = fft(Rx_spectrum.signal);
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H_transfer = Rx_spectrum.signal./Tx_spectrum.signal;
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H_inv = 1./H_transfer;
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%Number of Samples/Symbol after Matched Filter
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Kov = 14;
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Scope_sig = Scope_sig.resample('fs_in',fs,'fs_out',Kov*fsym);
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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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% 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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Matched_Filter = Pulseformer("fsym",fsym,"fdac",Kov*fsym,"pulse","rrc","pulselength",16,"alpha",rolloff,"matched",1);
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Rx_matched = Matched_Filter.process(Scope_sig);
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Rx_matched.spectrum("displayname",'Signal after matched filter','fignum',1);
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% Loading and synchronizing their matched filtered and timing recovered data
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data_tr_mf = Electricalsignal(data_tr_mf.Results, "fs", fsym);
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[~,Rx_synced_cell_tr_mf,inverted_tr_mf,sequenceFound_tr_mf,sequenceStarts_tr_mf] = data_tr_mf.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 1);
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Rx_tr_mf = Rx_synced_cell_tr_mf{1};
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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_matched.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 1);
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Rx_matched_1 = Rx_synced_cell{1};
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% Timing recovery
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Time_Rec = 1;
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Timing_Mode = 3;
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if Time_Rec
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if Timing_Mode == 1 % Zero-Crossing/Gardner/Early-Late/Müller-Mueller Timing Recovery
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[Rx_Time_Rec, Timing_Error] = Timing_Recovery("modulation", 'PAM/PSK/QAM', "timing_error_detector",'Gardner (non-data-aided)','sps',Kov,'damping_factor',1,'normalized_loop_bandwidth',1e-4,'detector_gain',2.7).process(Rx_matched_1);
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elseif Timing_Mode == 2 % Godard Timing Recovery (resampling is required as the Godard timing recovery does not change the number of samples per symbol)
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[Rx_Time_Rec, Timing_Error_MG] = Godard_Timing_Recovery('mode',3,'num_blocks',1,'fft_length',length(Rx_matched_1),'sps',Kov,'rolloff',0.6,'mu',-0.2,'Ki',1e-4).process(Rx_matched_1);
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Rx_Time_Rec = Rx_Time_Rec.resample('fs_in',Kov*fsym,'fs_out',fsym);
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elseif Timing_Mode == 3 % Maximum Variance Timing Recovery
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Rx_Time_Rec = MaxVar_Timing_Recovery('mode',0,'fsym',fsym,'fadc',Kov*fsym,'num_tau',Kov*128,'sps',Kov,'comp_signal',Rx_tr_mf,'comp_mode',0).process(Rx_matched_1);
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end
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else
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Rx_Time_Rec = Rx_matched_1.resample('fs_in',Kov*fsym,'fs_out',fsym);
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end
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sps = 1;
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Rx_Time_Rec.fs = fsym;
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% % Compensate using the inverse transfer function
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% Amax = 10;
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% H_inv = min(abs(H_inv), Amax) .* exp(1j*angle(H_inv));
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% Rx_Time_Rec.signal = fft(Rx_Time_Rec.signal);
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% Rx_Time_Rec.signal = ifft(Rx_Time_Rec.signal .* H_inv);
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% Normalization
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Rx_Time_Rec = Rx_Time_Rec.normalize('mode','rms');
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Rx_tr_mf = Rx_tr_mf.normalize('mode','rms');
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% Compare spectra of our and their time signal
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Rx_Time_Rec.spectrum("displayname",'Our signal','normalizeTo0dB',1,'fignum',101111);
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% Rx_matched_original.spectrum('normalizeTo0dB',1,"displayname",'Our signal','fignum',101111);
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Rx_tr_mf.spectrum("displayname",'Their signal','normalizeTo0dB',1,'fignum',101111);
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% Compare our and their time signal
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Rx_Time_Rec.normalize("mode","rms").plot("displayname",'Our signal','fignum',101311);
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% Rx_matched_original.normalize("mode","rms").plot("displayname",'Original signal','fignum',101311);
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Rx_tr_mf.normalize("mode","rms").plot("displayname",'Their signal','fignum',101311);
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%%
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% Use our or their signal
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for our_signal = 1
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if our_signal
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Rx_synced = Rx_Time_Rec;
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else
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Rx_synced = Rx_tr_mf;
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end
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% Rx_synced = Rx_Time_Rec;
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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 = 0.004;
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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',103);
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Rx_synced.spectrum("displayname",'RX: Matched+Sync+2sps','fignum',104);
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Digi_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped','fignum',1,'normalizeTo0dB',1);
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Rx_sig_compare.normalize("mode","rms").spectrum("displayname",'Tx: RC-shaped + matched filtered ','fignum',1,'normalizeTo0dB',1);
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Rx_synced.normalize("mode","rms").spectrum("displayname",'RX: matched filtered + synced','fignum',1,'normalizeTo0dB',1);
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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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%% -------------------- FFE --------------------
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% requires some more digging what is going on :-)
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% eq_ffe = EQ("Ne",[150, 0, 0],"Nb",[0,0,0], ...
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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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%
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% % eq_ffe = FFE_DFE('ffe_order',99,'dfe_order',99,'len_tr',len_tr,'epochs_tr',5,'epochs_dd',5,'sps',sps,'decide',0);
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%
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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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%
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% if our_signal
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% fprintf('Our signal: %.1e \n',ffe_results.metrics.BER);
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% fprintf('Paper: %.1e \n \n',ber_in_paper);
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% else
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% fprintf('Their signal: %.1e \n',ffe_results.metrics.BER);
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% fprintf('Paper: %.1e \n \n',ber_in_paper);
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% end
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%% -------------------- VNLE + MLSE --------------------
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pf_ncoeffs = 4;
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eq_v = EQ("Ne",[300, 0, 0],"Nb",[0, 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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'weighted_DFE',0,'weighted_DFE_d_min',0.5,'weighted_DFE_mode','R2','weighted_DFE_I_mode',[5,0.5,0.6]);
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% eq_v = FFE_DFE('ffe_order',300,'dfe_order',5,'len_tr',len_tr,'epochs_tr',5,'epochs_dd',5, ...
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% 'ffe_mu_dd',mu_ffe,'ffe_mu_tr',0,'dfe_mu_dd',mu_dfe,'dfe_mu_tr',0.005,'sps',sps,'decide',0);
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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
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if our_signal
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fprintf('Our Signal: %.1e \n',mlse_results.metrics.BER);
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fprintf('Paper: %.1e \n \n',ber_in_paper);
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else
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fprintf('Their Signal: %.1e \n',mlse_results.metrics.BER);
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fprintf('Paper: %.1e \n \n',ber_in_paper);
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end
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%% -------------------- ML-based MLSE (L=2) --------------------
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% ml_mlse_equalizer = ML_MLSE("epochs_tr",100,"epochs_dd",1, ...
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% "len_tr",length(Rx_synced),"mu_dd",0.03,"mu_tr",0.03,"order",11,"sps",sps, ...
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% "traceback_depth",256,"L",4,"delta",4,"adaptive_mu",0);
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%
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% [ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode,"eth_style_symbol_mapping",mapping_style);
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% if our_signal
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% fprintf('Our Signal: %.1e \n',ml_mlse_results.metrics.BER);
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% fprintf('Paper: %.1e \n \n',ber_in_paper);
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% else
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% fprintf('Their EQ: %.1e \n',ml_mlse_results.metrics.BER);
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% fprintf('Paper: %.1e \n \n',ber_in_paper);
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% end
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end
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