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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