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+++ Changes +++

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+ duobinary_target now supports memoryless decoding (FFE targets DB response without MLSE)
+ PAMmapper.quantize now supports custom constellations for quantization
+ Added a new folder 'Documentations' for pdfs, slides, etc.
+ Added new FSO evaluation scripts in projects/FSO transmission/Evaluation Scripts
+ Added ffe_db (rudimentary module, not important anymore)
This commit is contained in:
magf
2026-03-05 10:41:21 +01:00
parent 2a724b833f
commit 3676d92b30
32 changed files with 2307 additions and 232 deletions
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62
projects/FSO_transmission/Evaluation Scripts/first_analysis_tr_mf.m
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@@ -189,7 +189,7 @@ for our_signal = 1
end
% Rx_synced = Rx_Time_Rec;
% Rx_synced = Rx_synced_cell{1};
len_tr = 4096*4;
len_tr = 4096*2;
mu_ffe1 = 0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
@@ -212,7 +212,7 @@ for our_signal = 1
end
%% -------------------- FFE --------------------
% requires some more digging what is going on :-)
% % requires some more digging what is going on :-)
% eq_ffe = EQ("Ne",[150, 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, ...
@@ -222,7 +222,7 @@ for our_signal = 1
%
% ffe_results = ffe(eq_ffe,M,Rx_synced,Symbols,Bits, ...
% "precode_mode",duob_mode,'showAnalysis',0,"postFFE",[], ...
% "eth_style_symbol_mapping",mapping_style);
% "eth_style_symbol_mapping",mapping_style,'db_target',1);
%
% if our_signal
% fprintf('Our signal: %.1e \n',ffe_results.metrics.BER);
@@ -233,29 +233,43 @@ for our_signal = 1
% end
%% -------------------- VNLE + MLSE --------------------
pf_ncoeffs = 4;
eq_v = EQ("Ne",[300, 0, 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",0, ...
'weighted_DFE',1,'weighted_DFE_d_min',0.5,'weighted_DFE_mode','I2','weighted_DFE_I_mode',[1,0.1,0.1], ...
'PDFE_coefficient',0.01);
% 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);
% 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",0, ...
% 'weighted_DFE',2,'weighted_DFE_d_min',0.9,'weighted_DFE_mode','R1','weighted_DFE_I_mode',[1,0.1,0.1], ...
% 'PDFE_coefficient',0.01);
% % 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
[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);
%% -------------------- DB target --------------------
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);
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
eq_ = EQ("Ne",[150, 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);
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,'decoding_mode',0);
dbt_results.metrics.print("description",'Duobinary');
fprintf('My EQ: %.1e \n',dbt_results.metrics.BER);
fprintf('Paper: %.1e \n \n',ber_in_paper);
BER_value = dbt_results.metrics.BER;
%% -------------------- ML-based MLSE (L=2) --------------------
% ml_mlse_equalizer = ML_MLSE("epochs_tr",100,"epochs_dd",1, ...