silas/imdd_silas
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

start with 400G analysis work

Browse Source
This commit is contained in:
Silas Oettinghaus
2026-07-20 10:22:11 +02:00
parent cae81c0dae
commit 125d8508ca
43 changed files with 4089 additions and 123 deletions
Download Patch File Download Diff File Expand all files Collapse all files

67
projects/IMDD_base_system/minimal_example.m
View File

@@ -133,14 +133,19 @@ Scpe_sig.signal = Scpe_sig.signal(1:2*length(Symbols));
if 1
% -------------------- FFE --------------------
ffe_order = [150, 0, 0];
eq_ = EQ("Ne",ffe_order,"Nb",[2,0,0], ...
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0], ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
% mu_tr_rls = 9.805e-01; mu_dd_rls = 0.999989348903919;
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",1.933e-04,"order",ffe_order(1),"sps",2,"decide",0,"optmize_mus",1,"dd_mode",1,"adaption_technique","nlms");
eq_ = FFE_plain("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",1.933e-04,"order",ffe_order(1),"sps",2,"decide",0,"optmize_mus",1,"dd_mode",1,"adaption_technique","nlms");
eq_ = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2, ...
"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",[0.0001 0.0008 0.001], ...
"order",[150,5,5],"sps",2,"decide",1, ...
"optmize_mus",1,"mu_optimization_len",2^15);
% eq_ = FFE("epochs_tr",4,"epochs_dd",5,"len_tr",4096,"mu_dd",0.01,"mu_tr",0.01,"order",50,"sps",2,"decide",0, "adaption",adaption_method.nlms,"dd_mode",1);
% eq_ = FFE_DFE("epochs_tr",5,"epochs_dd",5,"len_tr",512,"ffe_mu_dd",1e-4,"dfe_mu_dd",5e-4,"ffe_mu_tr",0,"dfe_mu_tr",0,"ffe_order",99,"dfe_order",99,"sps",2,"decide",0);
@@ -149,37 +154,37 @@ if 1
"precode_mode",duob_mode,'showAnalysis',1,"postFFE",[], ...
"eth_style_symbol_mapping",0);
output.ffe_results.metrics.print("description",'DFE');
output.ffe_results.metrics.print("description",'VNLE');
end
%%
% -------------------- VNLE + MLSE --------------------
pf_ncoeffs = 1;
ffe_order3 = [50, 5, 5];
eq_v = EQ("Ne",ffe_order3,"Nb",dfe_order, ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
[output.vnle_results, output.mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", 0);
output.mlse_results.metrics.print("description",'MLSE');
if 0
% -------------------- VNLE + MLSE --------------------
pf_ncoeffs = 1;
ffe_order3 = [50, 5, 5];
eq_v = EQ("Ne",ffe_order3,"Nb",dfe_order, ...
"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005, ...
"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
[output.vnle_results, output.mlse_results] = vnle_postfilter_mlse(eq_v, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 1, "postFFE", [], "eth_style_symbol_mapping", 0);
output.mlse_results.metrics.print("description",'MLSE');
end
%%
% -------------------- DB target --------------------
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);
ffe_order = [50, 5, 5];
eq_ = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
output.dbt_results = duobinary_target(eq_,mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", []);
output.dbt_results.metrics.print("description",'Duobinary');
if 0
% -------------------- DB target --------------------
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,'trellis_states',PAMmapper(M,0).levels);
ffe_order = [50, 5, 5];
eq_ = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
output.dbt_results = duobinary_target(eq_,mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode, 'showAnalysis', 0, "postFFE", []);
output.dbt_results.metrics.print("description",'Duobinary');
end