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Restore lost changes from pre-merge state

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magf
2026-02-02 13:38:56 +01:00
parent 005e821131
commit 7565ed0cf1
6 changed files with 7223 additions and 64 deletions
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48
Classes/04_DSP/Equalizer/EQ.m
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@@ -10,6 +10,10 @@ classdef EQ < handle
training_length %Number of training symbols
training_loops %Number of loops through sequence for training mode
ideal_dfe %Error free DFE decisions
weighted_DFE %Weighted DFE on/off
weighted_DFE_mode %Weighted DFE mode
weighted_DFE_d_min %d_min threshold parameter for weighted DFE mode 1
weighted_DFE_I_mode %[a_s, b_s, I_max]-parameters for the weighted DFE
DB_aim %Aim at duobinary output sequence
@@ -68,6 +72,10 @@ classdef EQ < handle
options.training_length = 1024 %Number of training symbols
options.training_loops = 1 %Number of loops through sequence for training mode
options.ideal_dfe = 0 %Error free DFE decisions
options.weighted_DFE = 0;
options.weighted_DFE_mode = 'R1';
options.weighted_DFE_d_min = 0.5;
options.weighted_DFE_I_mode = [5,0.5,0.6];
options.DB_aim %Aim at duobinary output sequence
@@ -438,6 +446,43 @@ classdef EQ < handle
dd_out(k) = constellation_in_(dd_idx);
end
% Implementation of a weighted DFE in
% order to prevent error propagation.
% For further details, study [1], chapter 3.2.2 -
% Modifications of DFE
if obj.weighted_DFE
% define new constellations
const = unique(ref_in);
% determine reliability factor gamma_k
if output_vec(m) > min(const) && output_vec(m) < max(const)
gamma_k = 1 - abs(output_vec(m) - dd_out(k));
else
gamma_k = 1;
end
% select mode
if strcmp(obj.weighted_DFE_mode,'R1')
if gamma_k >= obj.weighted_DFE_d_min
f_gamma_k = 1;
else
f_gamma_k = 0;
end
elseif strcmp(obj.weighted_DFE_mode,'R2')
f_gamma_k = gamma_k;
elseif strcmp(obj.weighted_DFE_mode,'I1')
nom = 1-exp(-obj.weighted_DFE_I_mode(1)*((gamma_k/obj.weighted_DFE_I_mode(2))-1));
denom = 1+exp(-obj.weighted_DFE_I_mode(1)*((gamma_k/obj.weighted_DFE_I_mode(2))-1));
f_gamma_k = (1/2)*((nom/denom) - 1);
elseif strcmp(obj.weighted_DFE_mode,'I2')
nom = 1-exp(-obj.weighted_DFE_I_mode(1)*((gamma_k/obj.weighted_DFE_I_mode(2))-1));
denom = 1+exp(-obj.weighted_DFE_I_mode(1)*((gamma_k/obj.weighted_DFE_I_mode(2))-1));
f_gamma_k = (obj.weighted_DFE_I_mode(3)/2)*((nom/denom) - 1);
end
% calculate weighted output
output_vec(m) = f_gamma_k.*dd_out(k)+(1-f_gamma_k).*output_vec(m);
end
if obj.Nb(1) > 0
dd_DFE(2:end) = dd_DFE(1:end-1);
@@ -739,3 +784,6 @@ classdef EQ < handle
end
end
% References
% [1] T. J. Wettlin, “Experimental Evaluation of Advanced Digital Signal Processing for Intra-Datacenter Systems using Direct-Detection,” 2023. [Online]. Available: https://nbn-resolving.org/urn:nbn:de:gbv:8:3-2023-00703-8

2
Classes/04_DSP/Equalizer/ML_MLSE.m
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@@ -597,7 +597,7 @@ classdef ML_MLSE < handle
obj.S = obj.nSym;
obj.Nf = obj.order * obj.sps;
obj.nStates = obj.S^obj.L;
obj.nFeasible = obj.nStates * obj.S; %feasible state transitions
obj.nFeasible = obj.nStates * obj.S;
% --- Trellis mapping
obj.trellis_states = reshape(obj.constellation,1,[]);

66
projects/FSO_transmission/Evaluation Scripts/first_analysis_tr_mf.m
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@@ -135,10 +135,10 @@ Time_Rec = 1;
if Time_Rec
% [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);
[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);
Rx_Time_Rec = Rx_Time_Rec.resample('fs_in',Kov*fsym,'fs_out',fsym);
% [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);
% Rx_Time_Rec = Rx_Time_Rec.resample('fs_in',Kov*fsym,'fs_out',fsym);
% 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);
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);
sps = 1;
else
@@ -232,40 +232,40 @@ for our_signal = 1
% end
%% -------------------- VNLE + MLSE --------------------
pf_ncoeffs = 4;
eq_v = EQ("Ne",[300, 0, 0],"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, ...
'weighted_DFE',0,'weighted_DFE_d_min',0.5,'weighted_DFE_mode','R2','weighted_DFE_I_mode',[5,0.5,0.6]);
% eq_v = FFE_DFE('ffe_order',300,'dfe_order',5,'len_tr',len_tr,'epochs_tr',5,'epochs_dd',5, ...
% 'ffe_mu_dd',mu_ffe,'ffe_mu_tr',0,'dfe_mu_dd',mu_dfe,'dfe_mu_tr',0.005,'sps',sps,'decide',0);
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(eq_v, pf_, mlse_, M, Rx_synced, Symbols, Bits, ...
"precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
mlse_results.metrics.print
if our_signal
fprintf('Our Signal: %.1e \n',mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
else
fprintf('Their Signal: %.1e \n',mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
end
%% -------------------- ML-based MLSE (L=2) --------------------
% ml_mlse_equalizer = ML_MLSE("epochs_tr",100,"epochs_dd",1, ...
% "len_tr",length(Rx_synced),"mu_dd",0.03,"mu_tr",0.03,"order",11,"sps",sps, ...
% "traceback_depth",256,"L",4,"delta",4,"adaptive_mu",0);
% pf_ncoeffs = 4;
% eq_v = EQ("Ne",[300, 0, 0],"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, ...
% 'weighted_DFE',0,'weighted_DFE_d_min',0.5,'weighted_DFE_mode','R2','weighted_DFE_I_mode',[5,0.5,0.6]);
% % eq_v = FFE_DFE('ffe_order',300,'dfe_order',5,'len_tr',len_tr,'epochs_tr',5,'epochs_dd',5, ...
% % 'ffe_mu_dd',mu_ffe,'ffe_mu_tr',0,'dfe_mu_dd',mu_dfe,'dfe_mu_tr',0.005,'sps',sps,'decide',0);
% pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
% mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0,"eth_style",mapping_style).levels);
%
% [ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode,"eth_style_symbol_mapping",mapping_style);
% [vnle_results, mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Rx_synced, Symbols, Bits, ...
% "precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
%
% mlse_results.metrics.print
% if our_signal
% fprintf('Our Signal: %.1e \n',ml_mlse_results.metrics.BER);
% fprintf('Our Signal: %.1e \n',mlse_results.metrics.BER);
% fprintf('Paper: %.1e \n \n',ber_in_paper);
% else
% fprintf('Their EQ: %.1e \n',ml_mlse_results.metrics.BER);
% fprintf('Their Signal: %.1e \n',mlse_results.metrics.BER);
% fprintf('Paper: %.1e \n \n',ber_in_paper);
% end
%% -------------------- ML-based MLSE (L=2) --------------------
ml_mlse_equalizer = ML_MLSE("epochs_tr",100,"epochs_dd",1, ...
"len_tr",length(Rx_synced),"mu_dd",0.03,"mu_tr",0.03,"order",11,"sps",sps, ...
"traceback_depth",256,"L",1,"delta",4,"adaptive_mu",0);
[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode,"eth_style_symbol_mapping",mapping_style);
if our_signal
fprintf('Our Signal: %.1e \n',ml_mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
else
fprintf('Their EQ: %.1e \n',ml_mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
end
end

60
projects/FSO_transmission/FSO_timing_recovery_minimal_example.m
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@@ -212,40 +212,40 @@ for our_signal = 1
% end
%% -------------------- VNLE + MLSE --------------------
pf_ncoeffs = 4;
eq_v = EQ("Ne",[300, 0, 0],"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, ...
'weighted_DFE',0,'weighted_DFE_d_min',0.5,'weighted_DFE_mode','R2','weighted_DFE_I_mode',[5,0.5,0.6]);
% eq_v = FFE_DFE('ffe_order',300,'dfe_order',5,'len_tr',len_tr,'epochs_tr',5,'epochs_dd',5, ...
% 'ffe_mu_dd',mu_ffe,'ffe_mu_tr',0,'dfe_mu_dd',mu_dfe,'dfe_mu_tr',0.005,'sps',sps,'decide',0);
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(eq_v, pf_, mlse_, M, Rx_synced, Symbols, Bits, ...
"precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
mlse_results.metrics.print
if our_signal
fprintf('Our Signal: %.1e \n',mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
else
fprintf('Their Signal: %.1e \n',mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
end
%% -------------------- ML-based MLSE (L=2) --------------------
% ml_mlse_equalizer = ML_MLSE("epochs_tr",100,"epochs_dd",1, ...
% "len_tr",length(Rx_synced),"mu_dd",0.03,"mu_tr",0.03,"order",11,"sps",sps, ...
% "traceback_depth",256,"L",4,"delta",4,"adaptive_mu",0);
% pf_ncoeffs = 4;
% eq_v = EQ("Ne",[300, 0, 0],"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, ...
% 'weighted_DFE',0,'weighted_DFE_d_min',0.5,'weighted_DFE_mode','R2','weighted_DFE_I_mode',[5,0.5,0.6]);
% % eq_v = FFE_DFE('ffe_order',300,'dfe_order',5,'len_tr',len_tr,'epochs_tr',5,'epochs_dd',5, ...
% % 'ffe_mu_dd',mu_ffe,'ffe_mu_tr',0,'dfe_mu_dd',mu_dfe,'dfe_mu_tr',0.005,'sps',sps,'decide',0);
% pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
% mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0,"eth_style",mapping_style).levels);
%
% [ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode,"eth_style_symbol_mapping",mapping_style);
% [vnle_results, mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Rx_synced, Symbols, Bits, ...
% "precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", mapping_style);
%
% mlse_results.metrics.print
% if our_signal
% fprintf('Our Signal: %.1e \n',ml_mlse_results.metrics.BER);
% fprintf('Our Signal: %.1e \n',mlse_results.metrics.BER);
% fprintf('Paper: %.1e \n \n',ber_in_paper);
% else
% fprintf('Their EQ: %.1e \n',ml_mlse_results.metrics.BER);
% fprintf('Their Signal: %.1e \n',mlse_results.metrics.BER);
% fprintf('Paper: %.1e \n \n',ber_in_paper);
% end
%% -------------------- ML-based MLSE (L=2) --------------------
ml_mlse_equalizer = ML_MLSE("epochs_tr",100,"epochs_dd",1, ...
"len_tr",length(Rx_synced),"mu_dd",0.03,"mu_tr",0.03,"order",80,"sps",sps, ...
"traceback_depth",256,"L",1,"delta",4,"adaptive_mu",0);
[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Rx_synced, Symbols, Bits,"precode_mode",duob_mode,"eth_style_symbol_mapping",mapping_style);
if our_signal
fprintf('Our Signal: %.1e \n',ml_mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
else
fprintf('Their EQ: %.1e \n',ml_mlse_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
end
end

2310
tore --source rescue-premerge -- Classes Normal file
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