ML Equalizer works now.
Not yet perfectly integrated into all the routines
This commit is contained in:
Silas Oettinghaus
@@ -13,14 +13,13 @@ arguments
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end
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try
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% Initialize output structures
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output.ffe_package = {};
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output.mlse_package = {};
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output.vnle_package = {};
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output.dbtgt_package = {};
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output.dbenc_package = {};
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output.mlmlse_package = {};
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if options.mode == "load_run_id" || options.append_to_db
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% Initialize database connection
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@@ -98,9 +97,10 @@ try
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use_ffe = 0;
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use_dfe = 0;
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use_vnle_mlse = 1;
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use_vnle_mlse = 0;
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use_dbtgt = 0;
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use_dbenc = 0;
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use_ml_mlse = 1;
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addProcessingResultToDatabase = 0;
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@@ -130,7 +130,7 @@ try
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mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0,"adaption_technique","lms");
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eq_post =FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption",adaption_method(adaption),"dd_mode",use_dd_mode);
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eq_post = FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption",adaption_method(adaption),"dd_mode",use_dd_mode);
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% Duobinary signaling (db encoded)
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mlse_db_enc = MLSE("DIR", [1,1], "duobinary_output", 0, "M", M, "trellis_states", PAMmapper(M,0).levels);
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eq_db_enc = EQ("Ne", ffe_order, "Nb", dfe_order, "training_length", len_tr, ...
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@@ -233,30 +233,25 @@ try
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database.addProcessingResult(run_id, ffe_results.metrics, ffe_results.config);
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end
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% pf_ncoeffs = 2;
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% 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);
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% pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
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% % mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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% mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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%
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% [ffe_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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%
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% ffe_results.metrics.print;
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% mlse_results.metrics.print;
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%
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% output.mlse_package{r} = mlse_results;
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% output.vnle_package{r} = ffe_results;
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%
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% if options.append_to_db
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% database.addProcessingResult(run_id, mlse_results.metrics, mlse_results.config);
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% database.addProcessingResult(run_id, ffe_results.metrics, ffe_results.config);
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% end
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end
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if use_ml_mlse
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%ML-based MLSE (L=2)
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mu_ml = 0.01; training_epochs = 250;
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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",2,"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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output.mlmlse_package{r} = ml_mlse_results;
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if options.append_to_db
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database.addProcessingResult(run_id, ml_mlse_results.metrics, ml_mlse_results.config);
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end
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end
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if use_dbtgt
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@@ -292,6 +287,7 @@ try
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if duob_mode == db_mode.db_encoded
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mlse_db_enc = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
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db_results = duobinary_signaling(eq_db_enc, mlse_db_enc, M, Scpe_sig, Symbols, Tx_bits, "precode_mode",duob_mode, "showAnalysis",0,"postFFE",[]);
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output.dbenc_package{r} = db_results;
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if options.append_to_db
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@@ -36,8 +36,9 @@ if ~isempty(options.postFFE)
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end
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% Process through MLSE
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% [mlse_signal] = mlse_.process(eq_signal);
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[mlse_signal,~,GMI_MLSE] = mlse_.process(eq_signal,tx_symbols);
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[mlse_signal] = mlse_.process(eq_signal);
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% tx_symbols_ = Duobinary().decode(tx_symbols);
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% [mlse_signal,~,GMI_MLSE] = mlse_.process(eq_signal,tx_symbols);
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% Apply duobinary encoding and decoding
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mlse_signal = Duobinary().encode(mlse_signal);
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113
Functions/EQ_structures/ml_mlse.m
Normal file
113
Functions/EQ_structures/ml_mlse.m
Normal file
@@ -0,0 +1,113 @@
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function [ml_mlse_results] = ml_mlse(eq_, M, rx_signal, tx_symbols, tx_bits, options)
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%
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%
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% Inputs:
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% eq_ - Equalizer object
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% M - Modulation order
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% rx_signal - Received signal
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% tx_symbols - Transmitted symbols
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% tx_bits - Transmitted bits
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% options - Optional parameters
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%
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% Outputs:
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% ffe_results - Results from FFE processing
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arguments
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eq_
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M
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rx_signal
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tx_symbols
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tx_bits
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options.precode_mode db_mode
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options.eth_style_symbol_mapping = 0;
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options.postFFE = [];
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end
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%% Process signals through equalizer
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[eq_signal_hd,y_ref] = eq_.process(rx_signal,tx_symbols);
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%% Calculate BER based on precoding mode
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[bits, errors, ber, error_pos, errors_precoded, ber_precoded] = calculateBER(eq_signal_hd, tx_symbols, tx_bits, options.precode_mode, M, options.eth_style_symbol_mapping);
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% Create FFE results structure
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ml_mlse_results = struct();
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try
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eq_.e = [];
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eq_.e2 = [];
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eq_.e3 = [];
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eq_.b = [];
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eq_.b2 = [];
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eq_.b3 = [];
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end
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ml_mlse_results.config = Equalizerstruct();
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ml_mlse_results.config.eq = jsonencode(eq_);
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ml_mlse_results.config.equalizer_structure = int32(equalizer_structure.ml_mlse);
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ml_mlse_results.config.comment = 'function: ML-based MLSE';
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ml_mlse_results.metrics = Metricstruct;
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ml_mlse_results.metrics.result_id = NaN;
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ml_mlse_results.metrics.run_id = NaN;
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ml_mlse_results.metrics.eqParam_id = NaN;
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ml_mlse_results.metrics.date_of_processing = datetime('now');
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ml_mlse_results.metrics.BER = ber;
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ml_mlse_results.metrics.numBits = bits;
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ml_mlse_results.metrics.numBitErr = errors;
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ml_mlse_results.metrics.BER_precoded = ber_precoded;
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ml_mlse_results.metrics.numBitErr_precoded = errors_precoded;
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ml_mlse_results.metrics.SNR = NaN;
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ml_mlse_results.metrics.SNR_level = NaN;
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ml_mlse_results.metrics.STD = NaN;
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ml_mlse_results.metrics.STD_level = NaN;
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ml_mlse_results.metrics.STDrx = NaN;
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ml_mlse_results.metrics.STDrx_level = NaN;
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ml_mlse_results.metrics.GMI = NaN;
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ml_mlse_results.metrics.AIR = NaN;
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ml_mlse_results.metrics.EVM = NaN;
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ml_mlse_results.metrics.EVM_level = NaN;
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ml_mlse_results.metrics.Alpha = NaN;
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end
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%% Helper Functions
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function [bits, errors, ber, error_pos, errors_precoded, ber_precoded] = calculateBER(eq_signal_hd, tx_symbols, tx_bits, precode_mode, M, eth_style)
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% Calculate BER based on precoding mode
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mapper = PAMmapper(M, 0, "eth_style", eth_style);
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switch precode_mode
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case db_mode.no_db
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% TX Data is not precoded
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% A) Emulate diff precoding
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eq_signal_hd_precoded = Duobinary().encode(eq_signal_hd, "M", M);
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eq_signal_hd_precoded = Duobinary().decode(eq_signal_hd_precoded, "M", M);
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tx_symbols_precoded = Duobinary().encode(tx_symbols);
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tx_symbols_precoded = Duobinary().decode(tx_symbols_precoded);
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tx_bits_precoded = mapper.demap(tx_symbols_precoded);
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rx_bits = mapper.demap(eq_signal_hd_precoded);
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[~, errors_precoded, ber_precoded, ~] = calc_ber(rx_bits.signal, tx_bits_precoded.signal, "skip_front", 30000, "skip_end", 150, "returnErrorLocation", 1);
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% B) Just determine BER
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rx_bits = mapper.demap(eq_signal_hd);
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[bits, errors, ber, error_pos] = calc_ber(rx_bits.signal, tx_bits.signal, "skip_front", 30000, "skip_end", 150, "returnErrorLocation", 1);
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case db_mode.db_precoded
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% Data is precoded on TX side
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% A) Decode at Rx if no DB targeting was applied
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eq_signal_hd_decoded = Duobinary().encode(eq_signal_hd, "M", M);
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eq_signal_hd_decoded = Duobinary().decode(eq_signal_hd_decoded, "M", M);
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rx_bits_decoded = mapper.demap(eq_signal_hd_decoded);
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[~, errors_precoded, ber_precoded, ~] = calc_ber(rx_bits_decoded.signal, tx_bits.signal, "skip_front", 30000, "skip_end", 150, "returnErrorLocation", 1);
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% B) Omit the Coding by comparing with demapped TX symbol sequence
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tx_bits_demapped = mapper.demap(tx_symbols);
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rx_bits = mapper.demap(eq_signal_hd);
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[bits, errors, ber, error_pos] = calc_ber(rx_bits.signal, tx_bits_demapped.signal, "skip_front", 30000, "skip_end", 150, "returnErrorLocation", 1);
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end
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end
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@@ -1,40 +0,0 @@
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function [eq_, pf_, mlse_, mlse_db_, eq_post] = configureEqualizers(M, len_tr, vnle_order, dfe_order, mu_dc, mu_ffe, mu_dfe, pf_ncoeffs)
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% CONFIGUREEQUALIZERS Creates and configures equalizer objects
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%
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% Inputs:
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% M - PAM level
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% len_tr - Training length
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% vnle_order - Array with orders for VNLE [order1, order2, order3]
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% dfe_order - Array with orders for DFE
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% mu_dc - DC adaptation rate
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% mu_ffe - Array with adaptation rates for FFE [mu1, mu2, mu3]
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% mu_dfe - Adaptation rate for DFE
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% pf_ncoeffs - Number of coefficients for postfilter
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%
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% Outputs:
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% eq_ - Configured EQ object
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% pf_ - Configured Postfilter object
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% mlse_ - Configured MLSE_viterbi object
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% mlse_db_ - Configured MLSE_viterbi object for duobinary
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% eq_post - Configured FFE object for post-processing
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% Configure main equalizer
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eq_ = EQ("Ne", vnle_order, "Nb", dfe_order, ...
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"training_length", len_tr, "training_loops", 5, "dd_loops", 5, ...
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"K", 2, "DCmu", mu_dc, "DDmu", [mu_ffe mu_dfe], ...
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"DFEmu", 0.005, "FFEmu", 0, "plotfinal", 0, "ideal_dfe", 1);
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% Configure postfilter
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pf_ = Postfilter("ncoeff", pf_ncoeffs, "useBurg", 1);
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% Configure MLSE objects
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mlse_ = MLSE_viterbi("duobinary_output", 0, 'M', M, ...
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'trellis_states', PAMmapper(M,0).levels);
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mlse_db_ = MLSE_viterbi("DIR", [1,1], "duobinary_output", 0, ...
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"M", M, "trellis_states", PAMmapper(M,0).levels);
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% Configure post-FFE
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eq_post = FFE("epochs_tr", 5, "epochs_dd", 5, "len_tr", 4096*2, ...
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"mu_dd", 1e-4, "mu_tr", 0, "order", 2001, ...
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"sps", 1, "decide", 0);
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end
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@@ -16,9 +16,9 @@ Scpe_sig = Scpe_sig.resample("fs_out", 2*fsym);
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[Scpe_sig, ~] = Scpe_sig.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
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% Apply Gaussian filter
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% Scpe_sig = Filter('filtdegree', 4, "f_cutoff", Symbols.fs.*0.6, ...
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% "fs", Scpe_sig.fs, "filterType", filtertypes.gaussian, ...
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% "active", true).process(Scpe_sig);
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Scpe_sig = Filter('filtdegree', 4, "f_cutoff", Symbols.fs.*0.6, ...
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"fs", Scpe_sig.fs, "filterType", filtertypes.gaussian, ...
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"active", true).process(Scpe_sig);
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% Remove DC offset
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Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
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@@ -209,6 +209,7 @@ wh = submit_options.wh;
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wh.addValueToStorageByLinIdx(val.vnle_package, 'vnle_package', jobIndex);
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wh.addValueToStorageByLinIdx(val.dbtgt_package,'dbtgt_package',jobIndex);
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wh.addValueToStorageByLinIdx(val.dbenc_package,'dbenc_package',jobIndex);
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wh.addValueToStorageByLinIdx(val.mlmlse_package,'mlmlse_package',jobIndex);
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end
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end
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function p = setupParallelPool(numWorkers, idleTimeout)
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@@ -1,43 +1,97 @@
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function beautifyBERplot(options)
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% BEAUTIFYBERPLOT Enhances BER-style plots for publication-quality figures.
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% Supports automatic smoothing and trend-line overlay.
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%
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% Usage examples:
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% beautifyBERplot; % default
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% beautifyBERplot("polyfit",1); % add polynomial fit
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% beautifyBERplot("polyfit",1,"fitmethod","pchip") % piecewise cubic fit
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%
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% Supported fitmethod options: 'polyfit', 'smoothingspline', 'loess', 'pchip'
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arguments
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options.logscale = 1
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options.logscale (1,1) logical = 1
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options.polyfit (1,1) logical = 0
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options.polyorder (1,1) double = 2
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options.fitmethod (1,1) string = "polyfit" % choose fit type
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end
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% BEAUTIFYBERPLOT Enhances a BER plot for publication-quality figures.
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% Set line properties for all current plot lines
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lines = findall(gca, 'Type', 'Line');
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markers = {'o', 's', 'd', '^', 'v', '>', '<', 'p', 'h'}; % Define marker styles
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num_markers = length(markers);
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% --- find all line objects in current axes
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lines = findall(gca, 'Type', 'Line');
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markers = {'o', 's', 'd', '^', 'v', '>', '<', 'p', 'h'};
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num_markers = length(markers);
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% --- style all lines consistently
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for i = 1:length(lines)
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lines(i).LineWidth = 1.1;
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lines(i).LineStyle = '-';
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if string(lines(i).Marker) == "none"
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lines(i).Marker = markers{mod(i-1, num_markers) + 1};
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end
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lines(i).MarkerSize = 4;
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lines(i).MarkerFaceColor = lines(i).Color;
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end
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% --- optional smoothing/fitting overlay
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if options.polyfit
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hold on
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for i = 1:length(lines)
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lines(i).LineWidth = 1.3; % Thicker line width
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lines(i).LineStyle = '-'; % Solid lines for simplicity
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if string(lines(i).Marker) == "none"
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lines(i).Marker = markers{mod(i-1, num_markers) + 1}; % Assign markers cyclically
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x = lines(i).XData;
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y = lines(i).YData;
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valid = isfinite(x) & isfinite(y);
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if sum(valid) < options.polyorder + 1
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continue;
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end
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lines(i).MarkerSize = 7; % Marker size
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lines(i).MarkerFaceColor = lines(i).Color; % Use line color for marker face
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lines(i).MarkerEdgeColor = 'white';
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xf = linspace(min(x(valid)), max(x(valid)), 200);
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% ----- choose fitting method -----
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switch lower(options.fitmethod)
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case "polyfit"
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p = polyfit(x(valid), y(valid), options.polyorder);
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yf = polyval(p, xf);
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case "smoothingspline"
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try
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f = fit(x(valid)', y(valid)', 'smoothingspline');
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yf = feval(f, xf);
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catch
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yf = interp1(x(valid), y(valid), xf, 'pchip');
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end
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case "loess"
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yf = smooth(x(valid), y(valid), 0.2, 'loess');
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yf = interp1(x(valid), yf, xf, 'linear', 'extrap');
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case "pchip"
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yf = interp1(x(valid), y(valid), xf, 'pchip');
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otherwise
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warning('Unknown fitmethod "%s". Using polyfit.', options.fitmethod);
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p = polyfit(x(valid), y(valid), options.polyorder);
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yf = polyval(p, xf);
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end
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% --- lightened color for fit overlay
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lightcol = lines(i).Color + 0.4 * (1 - lines(i).Color);
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lightcol(lightcol > 1) = 1;
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plot(xf, yf, '-', 'Color', lightcol, ...
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'LineWidth', 0.7, 'Marker', 'none', ...
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'HandleVisibility','off');
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end
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|
||||
% Change all text interpreters to LaTeX
|
||||
set(findall(gca, '-property', 'Interpreter'), 'Interpreter', 'latex');
|
||||
|
||||
% Set figure background to white
|
||||
set(gcf, 'Color', 'w');
|
||||
|
||||
|
||||
% Set logarithmic scale for y-axis, but only if it makes sense.
|
||||
% If this is not always desired, you could condition this on the presence of lines or data.
|
||||
if options.logscale
|
||||
set(gca, 'YScale', 'log');
|
||||
end
|
||||
|
||||
% Customize grid and box appearance
|
||||
set(gca, 'Box', 'on', 'LineWidth', 0.8); % Thicker border
|
||||
grid on;
|
||||
% grid minor;
|
||||
|
||||
% Adjust font size and style for better readability
|
||||
set(gca, 'FontSize', 10, 'FontName', 'Times New Roman');
|
||||
hold off
|
||||
end
|
||||
|
||||
% --- axis scaling and cosmetics
|
||||
if options.logscale
|
||||
set(gca, 'YScale', 'log');
|
||||
end
|
||||
|
||||
set(findall(gca, '-property', 'Interpreter'), 'Interpreter', 'latex');
|
||||
set(gcf, 'Color', 'w');
|
||||
set(gca, 'Box', 'on', 'LineWidth', 0.8);
|
||||
grid on;
|
||||
set(gca, 'FontSize', 10, 'FontName', 'Times New Roman');
|
||||
|
||||
end
|
||||
|
||||
Reference in New Issue
Block a user