155 lines
5.1 KiB
Matlab
155 lines
5.1 KiB
Matlab
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dsp_options.storage_path = 'Z:\2024\sioe_labor\';
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dsp_options.max_occurences = 1;
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db = DBHandler("dataBase", 'labor_highspeed', "type", 'mysql' );
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fp = QueryFilter();
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fp.where('Runs','fiber_length','EQUALS', 2);
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fp.where('Runs','wavelength','EQUALS', 1310);
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fp.where('Runs','bitrate','EQUALS', 300e9);
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fp.where('Runs','pam_level','EQUALS', 4);
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fp.where('Runs','rop_attenuation','EQUALS', 0);
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fp.where('Runs','is_mpi','EQUALS', 0);
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fp.where('Runs', 'db_mode','EQUALS', 0);
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% fields = db.getTableFieldNames('Runs');
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% [dataTable,~] = db.queryDB(fp, fields);
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fields = db.getTableFieldNames('power_state_info');
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fields = [fields; db.getTableFieldNames('Runs')];
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fields = [fields; db.getTableFieldNames('dashboard_ungrouped_alltime')]; %dashboard_ungrouped_after_nov_2025 dashboard_ungrouped_aug_nov_2025
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fields = unique(fields);
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[dataTable,~] = db.queryDB(fp, fields);
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fsym = dataTable(1,:).symbolrate;
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M = double(dataTable(1,:).pam_level);
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duob_mode = db_mode(strrep(dataTable(1,:).db_mode,'"',''));
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% Load and Sync signal data from DB
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[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable(1,:), dsp_options);
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% Preprocess signal
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Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
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% Show spectrum
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Scpe_sig.spectrum("fignum",1,"displayname",'Rx')
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% meta = struct();
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% meta.varnames = dataTable.Properties.VariableNames;
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%
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% for k = 1:numel(meta.varnames)
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% v = meta.varnames{k};
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% col = dataTable.(v);
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%
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% if isnumeric(col) || islogical(col)
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% meta.(v) = col;
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% elseif isstring(col)
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% meta.(v) = cellstr(col);
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% elseif iscellstr(col)
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% meta.(v) = col;
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% else
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% error("Unsupported table column type: %s", class(col))
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% end
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% end
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% exp_data.metadata = meta;
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exp_data = struct();
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exp_data.metadata = dataTable;
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exp_data.tx_bits = Tx_bits.signal;
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exp_data.tx_signal = Symbols.signal;
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exp_data.rx_signal_2sps = Scpe_sig.signal;
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fname = dataTable(1,:).rx_raw_path;
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[~, filename, ext] = fileparts(fname);
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filename = strrep(filename,"_raw_signal","");
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filename = filename + ext;
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savepath = fullfile('F:\2024\sioe_labor\export_skuehl\',filename);
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save(savepath,'exp_data','-v7.3');
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%% simple FFE
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mu_ffe = [0.0001, 0.0008, 0.001];
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mu_dfe = 0.0004;
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ffe_order = [50, 0, 0];
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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);
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ffe_results = ffe(eq_dfe,M,Scpe_sig,Symbols,Tx_bits,...
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"precode_mode",duob_mode,...
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'showAnalysis',0,...
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"postFFE",[],...
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"eth_style_symbol_mapping",0);
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ffe_results.metrics.print("description",'FFE');
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ffe_results.config.equalizer_structure = "ffe";
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%% a) VNLE // b) concatenated VNLE + MLSE
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pf_ncoeffs = 1;
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ffe_order = [50, 5, 5];
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dfe_order = [0,0,0];
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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);
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pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
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if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
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trellexlusion = 1;
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else
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trellexlusion = 0;
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end
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%state_mode 3 -> stat lvl; state_mode 2 -> use target lvls
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%scale_mode 2 -> mmse adaption
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mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',2);
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[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
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"precode_mode", duob_mode,...
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'showAnalysis', 0, ...
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"postFFE", [],...
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"eth_style_symbol_mapping", 0);
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vnle_results.metrics.print("description",'VNLE');
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mlse_results.metrics.print("description",'VNLE + PF + MLSE');
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%% Duobinary Equalization
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if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
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trellexlusion = 1;
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else
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trellexlusion = 0;
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end
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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);
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ffe_order = [50, 5, 5];
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dfe_order = [0,0,0];
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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);
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dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
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"precode_mode", duob_mode, ...
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'showAnalysis', 0,...
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"postFFE", []);
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dbt_results.metrics.print("description",'Duobinary EQ');
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%% Ml based Viterbi
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%ML-based MLSE (L=2)
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mu_ml = 0.01; training_epochs = 100;
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ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
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"len_tr",length(Scpe_sig),"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
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"traceback_depth",128,"L",1,"delta",4,"adaptive_mu",0);
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[ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Scpe_sig, Symbols, Tx_bits,"precode_mode",duob_mode);
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ml_mlse_results.metrics.print("description",'ML pre Eq. + Viterbi') |