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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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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