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This commit is contained in:
Silas Oettinghaus
521
Classes/04_DSP/Equalizer/MPI_reduction/FFE_DCTracking.m
Normal file
521
Classes/04_DSP/Equalizer/MPI_reduction/FFE_DCTracking.m
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classdef FFE_DCTracking < FFE_plain
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%FFE_DCTracking Plain FFE with the DC-tracking offset loop.
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%
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% This class reuses the plain FFE process idea and BER mu optimization,
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% but keeps only the DC-tracking path from the larger FFE class. A1, A2,
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% and delayed FFE tap-update buffers are intentionally excluded.
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properties
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dc_tracking_mu
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dc_tracking_adaptive_enabled
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dc_tracking_persistence_gain
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dc_tracking_power_exponent
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dc_tracking_buffer_len
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e_dc
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end
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properties (Access = private)
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dc_tracking_mu_min = 1e-6
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dc_tracking_mu_max = 3e-1
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dc_tracking_mu_eff_min = 0
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dc_tracking_mu_eff_max = inf
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end
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methods
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function obj = FFE_DCTracking(options)
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arguments(Input)
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options.sps = 2
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options.order = 15
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options.len_tr = 4096
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options.mu_tr = 0
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options.epochs_tr = 5
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options.adaption_technique adaption_method = adaption_method.lms
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options.dd_mode = 1
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options.mu_dd = 1e-5
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options.epochs_dd = 5
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options.dd_len_fraction = 1
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options.dc_tracking_mu = 0
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options.dc_tracking_adaptive_enabled = false
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options.dc_tracking_persistence_gain = 0
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options.dc_tracking_power_exponent = 2
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options.dc_tracking_buffer_len = 1
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options.decide = false
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options.save_debug = 0
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options.optmize_mus = 0
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options.mu_optimization_len = 2^15
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options.plot_mu_optimization = 0
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options.mu_optimization_fignum = 3010
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end
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obj@FFE_plain( ...
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"sps",options.sps, ...
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"order",options.order, ...
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"len_tr",options.len_tr, ...
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"mu_tr",options.mu_tr, ...
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"epochs_tr",options.epochs_tr, ...
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"adaption_technique",options.adaption_technique, ...
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"dd_mode",options.dd_mode, ...
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"mu_dd",options.mu_dd, ...
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"epochs_dd",options.epochs_dd, ...
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"dd_len_fraction",options.dd_len_fraction, ...
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"decide",options.decide, ...
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"save_debug",options.save_debug, ...
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"optmize_mus",options.optmize_mus, ...
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"mu_optimization_len",options.mu_optimization_len, ...
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"plot_mu_optimization",options.plot_mu_optimization, ...
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"mu_optimization_fignum",options.mu_optimization_fignum);
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obj.dc_tracking_mu = options.dc_tracking_mu;
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obj.dc_tracking_adaptive_enabled = options.dc_tracking_adaptive_enabled;
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obj.dc_tracking_persistence_gain = options.dc_tracking_persistence_gain;
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obj.dc_tracking_power_exponent = options.dc_tracking_power_exponent;
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obj.dc_tracking_buffer_len = floor(options.dc_tracking_buffer_len);
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obj.e_dc = 0;
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assert(obj.dc_tracking_buffer_len >= 0);
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end
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function [X,Noi] = process(obj,X,D)
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X = X.normalize("mode","rms");
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obj.constellation = unique(D.signal);
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obj.resetTrackingState();
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if obj.optmize_mus
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obj.optimizeMus(X.signal,D.signal);
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obj.resetTrackingState();
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end
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training = true;
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showviz = false;
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obj.equalize(X.signal,D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
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obj.e_tr = obj.e;
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n = X.length;
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training = false;
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if obj.dd_mode
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[signal,decision] = obj.equalize(X.signal,D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
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else
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[signal,decision] = obj.equalize(X.signal,D.signal,0,1,n,training,showviz);
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end
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if obj.decide
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X.signal = decision;
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else
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X.signal = signal;
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end
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X.fs = D.fs;
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X = X.logbookentry([num2str(obj.order),' tap FFE DC tracking']);
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Noi = X - D;
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end
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function optimizeMus(obj,x,d)
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[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d);
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switch obj.adaption_technique
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case adaption_method.lms
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mu_range = [1e-5, 1e-2];
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case adaption_method.nlms
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mu_range = [1e-3, 5e-1];
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case adaption_method.rls
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mu_range = [0.98, 0.99999];
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otherwise
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builtin("error","FFE_DCTracking:InvalidAdaptionTechnique", ...
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"Unsupported FFE adaption technique.");
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end
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vars = optimizableVariable("mu_tr",mu_range,"Transform","log");
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if obj.dd_mode
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vars = [vars, optimizableVariable("mu_dd",mu_range,"Transform","log")];
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end
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optimize_dc_tracking_mu = obj.dc_tracking_mu ~= 0;
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if optimize_dc_tracking_mu
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vars = [vars, optimizableVariable("dc_tracking_mu",[1e-5,1e-1],"Transform","log")];
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end
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obj.mu_optimization_iter = 0;
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fprintf("FFE_DCTracking mu opt uses %d samples / %d symbols\n",N_opt,numel(d_opt));
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obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x_opt,d_opt),vars, ...
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"MaxObjectiveEvaluations",20, ...
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"AcquisitionFunctionName","expected-improvement-plus", ...
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"IsObjectiveDeterministic",false, ...
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"Verbose",0, ...
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"PlotFcn",[]);
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obj.mu_tr = obj.mu_optimization.XAtMinObjective.mu_tr;
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if obj.dd_mode
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obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
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end
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if optimize_dc_tracking_mu
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obj.dc_tracking_mu = obj.mu_optimization.XAtMinObjective.dc_tracking_mu;
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end
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if obj.dd_mode && optimize_dc_tracking_mu
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fprintf("\nFFE_DCTracking mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e\n", ...
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obj.mu_tr,obj.mu_dd,obj.dc_tracking_mu,obj.mu_optimization.MinObjective);
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elseif obj.dd_mode
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fprintf("\nFFE_DCTracking mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e\n", ...
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obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective);
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elseif optimize_dc_tracking_mu
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fprintf("\nFFE_DCTracking mu opt done: mu_tr=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e\n", ...
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obj.mu_tr,obj.dc_tracking_mu,obj.mu_optimization.MinObjective);
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else
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fprintf("\nFFE_DCTracking mu opt done: mu_tr=%9.3e, BER=%9.3e\n", ...
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obj.mu_tr,obj.mu_optimization.MinObjective);
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end
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if obj.plot_mu_optimization
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obj.plotMuOptimization();
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end
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end
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function objective = muObjective(obj,params,x,d)
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old_state = obj.captureTrackingObjectiveState();
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cleanup = onCleanup(@()obj.restoreTrackingObjectiveState(old_state));
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obj.save_debug = 0;
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if any(strcmp(params.Properties.VariableNames,"dc_tracking_mu"))
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obj.dc_tracking_mu = params.dc_tracking_mu;
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end
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obj.resetTrackingState();
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N_tr = min(obj.len_tr,numel(x));
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obj.equalize(x,d,params.mu_tr,obj.epochs_tr,N_tr,true,false);
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if obj.dd_mode
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[signal,~] = obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),false,false);
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else
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[signal,~] = obj.equalize(x,d,0,1,numel(x),false,false);
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end
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[ber,errors] = obj.berObjective(signal,d);
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objective = ber;
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if ~isfinite(objective)
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objective = inf;
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end
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obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
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has_dc_tracking_mu = any(strcmp(params.Properties.VariableNames,"dc_tracking_mu"));
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if obj.dd_mode && has_dc_tracking_mu
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fprintf("\rFFE_DCTracking mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,params.mu_tr,params.mu_dd,params.dc_tracking_mu,ber,errors);
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elseif obj.dd_mode
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fprintf("\rFFE_DCTracking mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,params.mu_tr,params.mu_dd,ber,errors);
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elseif has_dc_tracking_mu
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fprintf("\rFFE_DCTracking mu opt %02d: mu_tr=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,params.mu_tr,params.dc_tracking_mu,ber,errors);
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else
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fprintf("\rFFE_DCTracking mu opt %02d: mu_tr=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,params.mu_tr,ber,errors);
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end
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clear cleanup
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end
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function [y,d_hat] = equalize(obj,x,d,mu,epochs,N,training,showviz)
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arguments
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obj
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x
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d
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mu
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epochs
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N
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training
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showviz
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end
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unused_showviz = showviz; %#ok<NASGU>
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x = x(:);
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d = d(:);
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N = obj.validSampleLength(N,x,d);
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n_symbols = N / obj.sps;
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y = zeros(n_symbols,1);
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d_hat = zeros(n_symbols,1);
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if n_symbols == 0
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return
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end
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if isempty(obj.constellation)
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obj.constellation = unique(d);
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end
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decision_constellation = obj.constellation(:);
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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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lambda = mu;
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mask = ones(obj.order,1);
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maincursor_pos = ceil(length(obj.e)/2);
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adaption_code = obj.adaptionCode();
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adaption_is_rls = adaption_code == 3;
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if mu == 0 || (~obj.dd_mode && ~training)
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epochs = 1;
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end
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dc_tracking_mu_eff_min = obj.dc_tracking_mu_eff_min;
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dc_tracking_mu_eff_max = obj.dc_tracking_mu_eff_max;
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dc_tracking_persistence_gain = 0;
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if obj.dc_tracking_adaptive_enabled
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dc_tracking_persistence_gain = obj.dc_tracking_persistence_gain;
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end
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dc_tracking_enabled = obj.dc_tracking_mu ~= 0;
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if dc_tracking_enabled
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obj.dc_tracking_mu = min(max(obj.dc_tracking_mu, ...
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obj.dc_tracking_mu_min),obj.dc_tracking_mu_max);
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end
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dc_tracking_mu = obj.dc_tracking_mu;
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dc_tracking_use_persistence = dc_tracking_persistence_gain > 0;
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dc_tracking_base_mu_eff = min(max(dc_tracking_mu, ...
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dc_tracking_mu_eff_min),dc_tracking_mu_eff_max);
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dc_buffer_enabled = dc_tracking_enabled && obj.dc_tracking_buffer_len > 1;
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if dc_buffer_enabled
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dc_tracking_err_buffer = NaN(obj.dc_tracking_buffer_len,1);
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dc_tracking_err_buffer_pos = 0;
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dc_tracking_err_sum = 0;
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dc_tracking_abs_err_sum = 0;
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dc_tracking_valid_count = 0;
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else
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dc_tracking_err_buffer = [];
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dc_tracking_err_buffer_pos = 0;
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dc_tracking_err_sum = 0;
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dc_tracking_abs_err_sum = 0;
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dc_tracking_valid_count = 0;
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end
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debug_enabled = obj.save_debug;
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if debug_enabled
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obj.initializeTrackingDebug(n_symbols,training);
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end
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for epoch = 1:epochs
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symbol = 0;
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for sample = 1:obj.sps:N
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symbol = symbol + 1;
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grad = zeros(obj.order,1);
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update = zeros(obj.order,1);
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weight = 0;
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dc_tracking_mu_eff = 0;
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U = x(obj.order+sample-1:-1:sample);
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y(symbol,1) = obj.e_dc + (obj.e.*mask).' * U;
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if training
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d_hat(symbol,1) = d(symbol);
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else
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[~,symbol_idx] = min(abs(y(symbol) - decision_constellation));
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d_hat(symbol,1) = decision_constellation(symbol_idx);
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end
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err = d_hat(symbol) - y(symbol);
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switch adaption_code
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case 1
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weight = mu / ((U.'*U) + eps);
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grad = err * U;
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update = grad * weight;
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case 2
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weight = mu;
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grad = err * U;
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update = grad * weight;
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case 3
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denom = lambda + U.' * obj.P * U;
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k = (obj.P * U) / denom;
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update = k * err;
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end
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obj.e = obj.e + update;
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if adaption_is_rls
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obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
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end
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if dc_tracking_enabled
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[obj.e_dc,dc_tracking_mu_eff,dc_tracking_err_buffer, ...
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dc_tracking_err_buffer_pos,dc_tracking_err_sum, ...
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dc_tracking_abs_err_sum,dc_tracking_valid_count] = ...
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obj.updateDcTracking( ...
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err,dc_tracking_mu,dc_tracking_base_mu_eff, ...
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dc_tracking_persistence_gain,dc_tracking_use_persistence, ...
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dc_buffer_enabled,dc_tracking_mu_eff_min,dc_tracking_mu_eff_max, ...
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symbol,dc_tracking_err_buffer,dc_tracking_err_buffer_pos, ...
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dc_tracking_err_sum,dc_tracking_abs_err_sum, ...
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dc_tracking_valid_count);
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end
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if debug_enabled && epoch == epochs
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error_power = err * err';
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update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
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obj.debug_struct.error(1,symbol) = error_power;
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obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
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obj.debug_struct.mu_nlms(1,symbol) = weight;
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obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
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obj.debug_struct.dc_tracking_mu_eff(1,symbol) = dc_tracking_mu_eff;
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obj.debug_struct.dc_tracking_est(1,symbol) = obj.e_dc;
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if training
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obj.debug_struct.error_tr(1,symbol) = error_power;
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obj.debug_struct.update_tr(1,symbol) = update_power;
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else
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obj.debug_struct.error_dd(1,symbol) = error_power;
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obj.debug_struct.update(1,symbol) = update_power;
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end
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end
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end
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end
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end
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end
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methods (Access = private)
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function resetTrackingState(obj)
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obj.e = zeros(obj.order,1);
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obj.e_tr = zeros(obj.order,1);
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obj.error = 0;
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obj.e_dc = 0;
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obj.P = (1/0.05) * eye(obj.order);
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obj.debug_struct = struct();
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end
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function state = captureTrackingObjectiveState(obj)
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state.e = obj.e;
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state.e_tr = obj.e_tr;
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state.error = obj.error;
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state.e_dc = obj.e_dc;
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state.P = obj.P;
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state.save_debug = obj.save_debug;
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state.debug_struct = obj.debug_struct;
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state.dc_tracking_mu = obj.dc_tracking_mu;
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end
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function restoreTrackingObjectiveState(obj,state)
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obj.e = state.e;
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obj.e_tr = state.e_tr;
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obj.error = state.error;
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obj.e_dc = state.e_dc;
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obj.P = state.P;
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obj.save_debug = state.save_debug;
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obj.debug_struct = state.debug_struct;
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obj.dc_tracking_mu = state.dc_tracking_mu;
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end
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function N = validSampleLength(obj,N,x,d)
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N_available = min(numel(x),numel(d) * obj.sps);
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N = min(N,N_available);
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N = obj.sps * floor(N / obj.sps);
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N = max(0,N);
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end
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function adaption_code = adaptionCode(obj)
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if obj.adaption_technique == adaption_method.nlms
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adaption_code = 1;
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elseif obj.adaption_technique == adaption_method.lms
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adaption_code = 2;
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elseif obj.adaption_technique == adaption_method.rls
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adaption_code = 3;
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else
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builtin("error","FFE_DCTracking:InvalidAdaptionTechnique", ...
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"Unsupported FFE adaption technique.");
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end
|
||||
end
|
||||
|
||||
function initializeTrackingDebug(obj,n_symbols,training)
|
||||
obj.debug_struct.error = NaN(1,n_symbols);
|
||||
obj.debug_struct.error_first_epoch = NaN(1,n_symbols);
|
||||
obj.debug_struct.main_cursor = NaN(1,n_symbols);
|
||||
obj.debug_struct.mu_nlms = NaN(1,n_symbols);
|
||||
obj.debug_struct.update_gradient = NaN(1,n_symbols);
|
||||
obj.debug_struct.dc_tracking_mu_eff = NaN(1,n_symbols);
|
||||
obj.debug_struct.dc_tracking_est = NaN(1,n_symbols);
|
||||
|
||||
if training
|
||||
obj.debug_struct.error_tr = NaN(1,n_symbols);
|
||||
obj.debug_struct.update_tr = NaN(1,n_symbols);
|
||||
else
|
||||
obj.debug_struct.error_dd = NaN(1,n_symbols);
|
||||
obj.debug_struct.update = NaN(1,n_symbols);
|
||||
end
|
||||
end
|
||||
|
||||
function [e_dc,mu_eff,err_buffer,err_buffer_pos,err_sum,abs_err_sum,valid_count] = ...
|
||||
updateDcTracking(obj,err_current,mu_dc,base_mu_eff,persistence_gain, ...
|
||||
use_persistence,buffer_enabled,mu_eff_min,mu_eff_max,symbol, ...
|
||||
err_buffer,err_buffer_pos,err_sum,abs_err_sum,valid_count)
|
||||
|
||||
mu_eff = 0;
|
||||
e_dc = obj.e_dc;
|
||||
|
||||
if buffer_enabled
|
||||
err_buffer_pos = err_buffer_pos + 1;
|
||||
if err_buffer_pos > obj.dc_tracking_buffer_len
|
||||
err_buffer_pos = 1;
|
||||
end
|
||||
|
||||
old_err = err_buffer(err_buffer_pos);
|
||||
if isfinite(old_err)
|
||||
err_sum = err_sum - old_err;
|
||||
valid_count = valid_count - 1;
|
||||
if use_persistence
|
||||
abs_err_sum = abs_err_sum - abs(old_err);
|
||||
end
|
||||
end
|
||||
|
||||
if isfinite(err_current)
|
||||
err_buffer(err_buffer_pos) = err_current;
|
||||
err_sum = err_sum + err_current;
|
||||
valid_count = valid_count + 1;
|
||||
if use_persistence
|
||||
abs_err_sum = abs_err_sum + abs(err_current);
|
||||
end
|
||||
else
|
||||
err_buffer(err_buffer_pos) = NaN;
|
||||
end
|
||||
|
||||
if mod(symbol,obj.dc_tracking_buffer_len) == 0
|
||||
if valid_count == 0
|
||||
err_mean = 0;
|
||||
err_abs_mean = 0;
|
||||
else
|
||||
err_mean = err_sum / valid_count;
|
||||
err_abs_mean = abs_err_sum / valid_count;
|
||||
end
|
||||
|
||||
mu_eff = obj.effectiveDcMu(mu_dc,base_mu_eff,persistence_gain, ...
|
||||
use_persistence,err_mean,err_abs_mean,mu_eff_min,mu_eff_max);
|
||||
e_dc = e_dc + mu_eff * err_mean;
|
||||
end
|
||||
else
|
||||
if isfinite(err_current)
|
||||
err_mean = err_current;
|
||||
err_abs_mean = abs(err_current);
|
||||
else
|
||||
err_mean = 0;
|
||||
err_abs_mean = 0;
|
||||
end
|
||||
|
||||
mu_eff = obj.effectiveDcMu(mu_dc,base_mu_eff,persistence_gain, ...
|
||||
use_persistence,err_mean,err_abs_mean,mu_eff_min,mu_eff_max);
|
||||
e_dc = e_dc + mu_eff * err_mean;
|
||||
end
|
||||
end
|
||||
|
||||
function mu_eff = effectiveDcMu(~,mu_dc,base_mu_eff,persistence_gain, ...
|
||||
use_persistence,err_mean,err_abs_mean,mu_eff_min,mu_eff_max)
|
||||
|
||||
if use_persistence
|
||||
persistence_scale = abs(err_mean) / (err_abs_mean + eps);
|
||||
persistence_scale = min(max(persistence_scale,0),1);
|
||||
mu_eff = mu_dc * (1 + persistence_gain * persistence_scale);
|
||||
mu_eff = min(max(mu_eff,mu_eff_min),mu_eff_max);
|
||||
else
|
||||
mu_eff = base_mu_eff;
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
Reference in New Issue
Block a user