dsp_options.storage_path    = 'Z:\2024\sioe_labor\';
dsp_options.max_occurences = 1;
db    = DBHandler("dataBase", 'labor_highspeed', "type",  'mysql' );

fp = QueryFilter();

fp.where('Runs','fiber_length','EQUALS', 2);
fp.where('Runs','wavelength','EQUALS', 1310);
fp.where('Runs','bitrate','EQUALS', 300e9);
fp.where('Runs','pam_level','EQUALS', 4);
fp.where('Runs','rop_attenuation','EQUALS', 0);
fp.where('Runs','is_mpi','EQUALS', 0);
fp.where('Runs', 'db_mode','EQUALS', 0); 
fields = db.getTableFieldNames('Runs');
[dataTable,~] = db.queryDB(fp, fields);


fsym        = dataTable.symbolrate;
M           = double(dataTable.pam_level);
duob_mode   = db_mode(strrep(dataTable.db_mode,'"',''));


% Load and Sync signal data from DB
[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);

% Preprocess signal
Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);

% Show spectrum
Scpe_sig.spectrum("fignum",1,"displayname",'Rx')






%% simple FFE

mu_ffe      = [0.0001, 0.0008, 0.001];
mu_dfe      = 0.0004;
ffe_order   = [50, 0, 0];
eq_dfe    = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",4096,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);

ffe_results = ffe(eq_dfe,M,Scpe_sig,Symbols,Tx_bits,...
    "precode_mode",duob_mode,...
    'showAnalysis',0,...
    "postFFE",[],...
    "eth_style_symbol_mapping",0);


ffe_results.metrics.print("description",'FFE');
ffe_results.config.equalizer_structure = "ffe";

%% a) VNLE // b) concatenated VNLE + MLSE

pf_ncoeffs  = 1;
ffe_order   = [50, 5, 5];
dfe_order   = [0,0,0];
eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",4096,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
pf_    = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);



if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
    trellexlusion = 1;
else
    trellexlusion = 0;
end

%state_mode 3 -> stat lvl; state_mode 2 -> use target lvls
%scale_mode 2 -> mmse adaption

mlse_  = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',2);

[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
    "precode_mode", duob_mode,...
    'showAnalysis', 0, ...
    "postFFE", [],...
    "eth_style_symbol_mapping", 0);

vnle_results.metrics.print("description",'VNLE');
mlse_results.metrics.print("description",'VNLE + PF + MLSE');


%% Duobinary Equalization


if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
    trellexlusion = 1;
else
    trellexlusion = 0;
end
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',3);

ffe_order   = [50, 5, 5];
dfe_order   = [0,0,0];
eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",4096,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.005,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);

dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
    "precode_mode", duob_mode, ...
    'showAnalysis', 0,...
    "postFFE", []);

dbt_results.metrics.print("description",'Duobinary EQ');

%% Ml based Viterbi 

%ML-based MLSE (L=2)
mu_ml = 0.01; training_epochs = 100;
ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
    "len_tr",length(Scpe_sig),"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
    "traceback_depth",128,"L",1,"delta",4,"adaptive_mu",0);

[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Scpe_sig, Symbols, Tx_bits,"precode_mode",duob_mode);

ml_mlse_results.metrics.print("description",'ML pre Eq. + Viterbi')