753 lines
31 KiB
Matlab
753 lines
31 KiB
Matlab
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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dc_tracking_mu_eff_max
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e_dc
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optimize_dc_tracking_params = 0
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dc_tracking_optimization
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dc_tracking_optimization_iter = 0
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dc_tracking_optimization_len
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dc_tracking_optimization_max_evals
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dc_tracking_optimization_delay_weight
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dc_tracking_optimization_smoothing_len
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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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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.dc_tracking_mu_eff_max = inf
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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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options.optimize_dc_tracking_params = 0
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options.dc_tracking_optimization_len = 2^15
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options.dc_tracking_optimization_max_evals = 20
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options.dc_tracking_optimization_delay_weight = 1e-3
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options.dc_tracking_optimization_smoothing_len = 501
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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.dc_tracking_mu_eff_max = options.dc_tracking_mu_eff_max;
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obj.e_dc = 0;
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obj.optimize_dc_tracking_params = options.optimize_dc_tracking_params;
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obj.dc_tracking_optimization_len = options.dc_tracking_optimization_len;
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obj.dc_tracking_optimization_max_evals = max(1, ...
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floor(options.dc_tracking_optimization_max_evals));
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obj.dc_tracking_optimization_delay_weight = ...
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max(0,options.dc_tracking_optimization_delay_weight);
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obj.dc_tracking_optimization_smoothing_len = max(1, ...
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floor(options.dc_tracking_optimization_smoothing_len));
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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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if obj.optimize_dc_tracking_params
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obj.optimizeDcTrackingParams(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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~obj.optimize_dc_tracking_params;
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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 optimizeDcTrackingParams(obj,x,d)
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[x_opt,d_opt,N_opt] = obj.optimizationSignals( ...
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x,d,obj.dc_tracking_optimization_len);
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vars = optimizableVariable( ...
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"dc_tracking_mu",[1e-5,1e-1],"Transform","log");
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initial_values = min(max(obj.dc_tracking_mu,1e-5),1e-1);
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initial_names = "dc_tracking_mu";
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if obj.dc_tracking_adaptive_enabled
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vars = [vars, ...
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optimizableVariable("dc_tracking_persistence_gain",[0,2]), ...
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optimizableVariable("dc_tracking_mu_eff_max",[1e-3,3e-1], ...
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"Transform","log")];
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initial_values = [initial_values, ...
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min(max(obj.dc_tracking_persistence_gain,0),2), ...
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min(max(obj.dc_tracking_mu_eff_max,1e-3),3e-1)];
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initial_names = [initial_names, ...
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"dc_tracking_persistence_gain", "dc_tracking_mu_eff_max"];
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end
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initial_x = array2table(initial_values, ...
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"VariableNames",cellstr(initial_names));
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obj.dc_tracking_optimization_iter = 0;
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fprintf("FFE_DCTracking DC opt uses fixed mu_tr=%9.3e, mu_dd=%9.3e on %d samples / %d symbols\n", ...
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obj.mu_tr,obj.mu_dd,N_opt,numel(d_opt));
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old_rng = rng;
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cleanup_rng = onCleanup(@()rng(old_rng));
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rng(42,"twister");
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obj.dc_tracking_optimization = bayesopt( ...
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@(p)obj.dcTrackingObjective(p,x_opt,d_opt),vars, ...
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"MaxObjectiveEvaluations",obj.dc_tracking_optimization_max_evals, ...
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"InitialX",initial_x, ...
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"AcquisitionFunctionName","expected-improvement-plus", ...
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"IsObjectiveDeterministic",true, ...
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"Verbose",0, ...
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"PlotFcn",[]);
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clear cleanup_rng
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best = obj.dc_tracking_optimization.XAtMinObjective;
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obj.dc_tracking_mu = best.dc_tracking_mu;
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if obj.dc_tracking_adaptive_enabled
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obj.dc_tracking_persistence_gain = ...
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best.dc_tracking_persistence_gain;
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obj.dc_tracking_mu_eff_max = best.dc_tracking_mu_eff_max;
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fprintf("\nFFE_DCTracking DC opt done: dc_tracking_mu=%9.3e, persistence_gain=%6.3f, mu_eff_max=%9.3e, objective=%9.3e\n", ...
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obj.dc_tracking_mu,obj.dc_tracking_persistence_gain, ...
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obj.dc_tracking_mu_eff_max, ...
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obj.dc_tracking_optimization.MinObjective);
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else
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fprintf("\nFFE_DCTracking DC opt done: dc_tracking_mu=%9.3e, objective=%9.3e\n", ...
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obj.dc_tracking_mu,obj.dc_tracking_optimization.MinObjective);
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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 objective = dcTrackingObjective(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.applyDcTrackingObjectiveParams(params);
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obj.save_debug = 1;
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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,obj.mu_tr,obj.epochs_tr,N_tr,true,false);
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if obj.dd_mode
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[signal,~] = obj.equalize( ...
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x,d,obj.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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[delay_symbols,delay_corr] = obj.dcTrackingDelayObjective(signal,d);
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delay_penalty = obj.dc_tracking_optimization_delay_weight * ...
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abs(delay_symbols) / max(numel(d),1);
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objective = ber + delay_penalty;
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if ~isfinite(objective)
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objective = inf;
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end
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obj.dc_tracking_optimization_iter = ...
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obj.dc_tracking_optimization_iter + 1;
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if obj.dc_tracking_adaptive_enabled
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fprintf("\rFFE_DCTracking DC opt %02d: dc_tracking_mu=%9.3e, persistence_gain=%6.3f, mu_eff_max=%9.3e, BER=%9.3e, delay=%7.0f, corr=%6.3f, obj=%9.3e, errors=%d", ...
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obj.dc_tracking_optimization_iter,params.dc_tracking_mu, ...
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params.dc_tracking_persistence_gain, ...
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params.dc_tracking_mu_eff_max,ber,delay_symbols,delay_corr, ...
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objective,errors);
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else
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fprintf("\rFFE_DCTracking DC opt %02d: dc_tracking_mu=%9.3e, BER=%9.3e, delay=%7.0f, corr=%6.3f, obj=%9.3e, errors=%d", ...
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obj.dc_tracking_optimization_iter,params.dc_tracking_mu, ...
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ber,delay_symbols,delay_corr,objective,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
|
|
symbol = 0;
|
|
for sample = 1:obj.sps:N
|
|
symbol = symbol + 1;
|
|
grad = zeros(obj.order,1);
|
|
update = zeros(obj.order,1);
|
|
weight = 0;
|
|
dc_tracking_mu_eff = 0;
|
|
|
|
U = x(obj.order+sample-1:-1:sample);
|
|
y(symbol,1) = obj.e_dc + (obj.e.*mask).' * U;
|
|
|
|
if training
|
|
d_hat(symbol,1) = d(symbol);
|
|
else
|
|
[~,symbol_idx] = min(abs(y(symbol) - decision_constellation));
|
|
d_hat(symbol,1) = decision_constellation(symbol_idx);
|
|
end
|
|
|
|
err = d_hat(symbol) - y(symbol);
|
|
|
|
switch adaption_code
|
|
case 1
|
|
weight = mu / ((U.'*U) + eps);
|
|
grad = err * U;
|
|
update = grad * weight;
|
|
|
|
case 2
|
|
weight = mu;
|
|
grad = err * U;
|
|
update = grad * weight;
|
|
|
|
case 3
|
|
denom = lambda + U.' * obj.P * U;
|
|
k = (obj.P * U) / denom;
|
|
update = k * err;
|
|
end
|
|
|
|
obj.e = obj.e + update;
|
|
|
|
if adaption_is_rls
|
|
obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
|
|
end
|
|
|
|
if dc_tracking_enabled
|
|
[obj.e_dc,dc_tracking_mu_eff,dc_tracking_err_buffer, ...
|
|
dc_tracking_err_buffer_pos,dc_tracking_err_sum, ...
|
|
dc_tracking_abs_err_sum,dc_tracking_valid_count] = ...
|
|
obj.updateDcTracking( ...
|
|
err,dc_tracking_mu,dc_tracking_base_mu_eff, ...
|
|
dc_tracking_persistence_gain,dc_tracking_use_persistence, ...
|
|
dc_buffer_enabled,dc_tracking_mu_eff_min,dc_tracking_mu_eff_max, ...
|
|
symbol,dc_tracking_err_buffer,dc_tracking_err_buffer_pos, ...
|
|
dc_tracking_err_sum,dc_tracking_abs_err_sum, ...
|
|
dc_tracking_valid_count);
|
|
end
|
|
|
|
if debug_enabled && epoch == epochs
|
|
error_power = err * err';
|
|
update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
|
|
obj.debug_struct.error(1,symbol) = error_power;
|
|
obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
|
|
obj.debug_struct.mu_nlms(1,symbol) = weight;
|
|
obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
|
|
obj.debug_struct.dc_tracking_mu_eff(1,symbol) = dc_tracking_mu_eff;
|
|
obj.debug_struct.dc_tracking_est(1,symbol) = obj.e_dc;
|
|
|
|
if training
|
|
obj.debug_struct.error_tr(1,symbol) = error_power;
|
|
obj.debug_struct.update_tr(1,symbol) = update_power;
|
|
else
|
|
obj.debug_struct.error_dd(1,symbol) = error_power;
|
|
obj.debug_struct.update(1,symbol) = update_power;
|
|
end
|
|
end
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
methods (Access = private)
|
|
function resetTrackingState(obj)
|
|
obj.e = zeros(obj.order,1);
|
|
obj.e_tr = zeros(obj.order,1);
|
|
obj.error = 0;
|
|
obj.e_dc = 0;
|
|
obj.P = (1/0.05) * eye(obj.order);
|
|
obj.debug_struct = struct();
|
|
end
|
|
|
|
function state = captureTrackingObjectiveState(obj)
|
|
state.e = obj.e;
|
|
state.e_tr = obj.e_tr;
|
|
state.error = obj.error;
|
|
state.e_dc = obj.e_dc;
|
|
state.P = obj.P;
|
|
state.save_debug = obj.save_debug;
|
|
state.debug_struct = obj.debug_struct;
|
|
state.dc_tracking_mu = obj.dc_tracking_mu;
|
|
state.dc_tracking_persistence_gain = ...
|
|
obj.dc_tracking_persistence_gain;
|
|
state.dc_tracking_mu_eff_max = obj.dc_tracking_mu_eff_max;
|
|
end
|
|
|
|
function restoreTrackingObjectiveState(obj,state)
|
|
obj.e = state.e;
|
|
obj.e_tr = state.e_tr;
|
|
obj.error = state.error;
|
|
obj.e_dc = state.e_dc;
|
|
obj.P = state.P;
|
|
obj.save_debug = state.save_debug;
|
|
obj.debug_struct = state.debug_struct;
|
|
obj.dc_tracking_mu = state.dc_tracking_mu;
|
|
obj.dc_tracking_persistence_gain = ...
|
|
state.dc_tracking_persistence_gain;
|
|
obj.dc_tracking_mu_eff_max = state.dc_tracking_mu_eff_max;
|
|
end
|
|
|
|
function applyDcTrackingObjectiveParams(obj,params)
|
|
var_names = string(params.Properties.VariableNames);
|
|
if any(var_names == "dc_tracking_mu")
|
|
obj.dc_tracking_mu = params.dc_tracking_mu;
|
|
end
|
|
if any(var_names == "dc_tracking_persistence_gain")
|
|
obj.dc_tracking_persistence_gain = ...
|
|
params.dc_tracking_persistence_gain;
|
|
end
|
|
if any(var_names == "dc_tracking_mu_eff_max")
|
|
obj.dc_tracking_mu_eff_max = params.dc_tracking_mu_eff_max;
|
|
end
|
|
end
|
|
|
|
function [delay_symbols,delay_corr] = ...
|
|
dcTrackingDelayObjective(obj,signal,d)
|
|
delay_symbols = 0;
|
|
delay_corr = 0;
|
|
if ~isfield(obj.debug_struct,"dc_tracking_est") || ...
|
|
isempty(obj.debug_struct.dc_tracking_est)
|
|
return
|
|
end
|
|
|
|
smooth_len = obj.dc_tracking_optimization_smoothing_len;
|
|
dc_tracking_est_s = movmean( ...
|
|
obj.debug_struct.dc_tracking_est(:),smooth_len,"omitnan");
|
|
avg_lvl_dc = obj.averageLevelTrace(signal,d,smooth_len);
|
|
|
|
xcorr_len = min(numel(dc_tracking_est_s),numel(avg_lvl_dc));
|
|
if xcorr_len < 2
|
|
return
|
|
end
|
|
|
|
inv_dc_xcorr = -dc_tracking_est_s(1:xcorr_len);
|
|
avg_lvl_xcorr = avg_lvl_dc(1:xcorr_len);
|
|
inv_dc_xcorr = fillmissing( ...
|
|
inv_dc_xcorr,"linear","EndValues","nearest");
|
|
avg_lvl_xcorr = fillmissing( ...
|
|
avg_lvl_xcorr,"linear","EndValues","nearest");
|
|
inv_dc_xcorr = inv_dc_xcorr - mean(inv_dc_xcorr,"omitnan");
|
|
avg_lvl_xcorr = avg_lvl_xcorr - mean(avg_lvl_xcorr,"omitnan");
|
|
|
|
if rms(inv_dc_xcorr) <= eps || rms(avg_lvl_xcorr) <= eps
|
|
return
|
|
end
|
|
|
|
[dc_level_xcorr,dc_level_lags] = xcorr( ...
|
|
inv_dc_xcorr,avg_lvl_xcorr,"coeff");
|
|
[delay_corr,delay_idx] = max(dc_level_xcorr);
|
|
delay_symbols = dc_level_lags(delay_idx);
|
|
if ~isfinite(delay_corr)
|
|
delay_corr = 0;
|
|
delay_symbols = 0;
|
|
end
|
|
end
|
|
|
|
function avg_lvl_dc = averageLevelTrace(obj,signal,d,smooth_len)
|
|
signal = signal(:);
|
|
d = d(:);
|
|
n_symbols = min(numel(signal),numel(d));
|
|
signal = signal(1:n_symbols);
|
|
d = d(1:n_symbols);
|
|
levels = unique(d);
|
|
avg_for_lvl = NaN(numel(levels),n_symbols);
|
|
|
|
for level_idx = 1:numel(levels)
|
|
level_mask = d == levels(level_idx);
|
|
level_samples = signal(level_mask);
|
|
if isempty(level_samples)
|
|
continue
|
|
end
|
|
|
|
smooth_window = min(smooth_len,numel(level_samples));
|
|
avg_for_lvl(level_idx,level_mask) = movmean( ...
|
|
level_samples,smooth_window,"omitnan","Endpoints","shrink");
|
|
avg_for_lvl(level_idx,:) = obj.interpolateMissingAverage( ...
|
|
avg_for_lvl(level_idx,:));
|
|
end
|
|
|
|
avg_lvl_dc = mean(avg_for_lvl,1,"omitnan").';
|
|
end
|
|
|
|
function level_average = interpolateMissingAverage(~,level_average)
|
|
valid_samples = isfinite(level_average);
|
|
if nnz(valid_samples) == 0
|
|
return
|
|
elseif nnz(valid_samples) == 1
|
|
level_average(:) = level_average(valid_samples);
|
|
return
|
|
end
|
|
|
|
t = 1:numel(level_average);
|
|
level_average(~valid_samples) = interp1( ...
|
|
t(valid_samples),level_average(valid_samples), ...
|
|
t(~valid_samples),"linear","extrap");
|
|
end
|
|
|
|
function N = validSampleLength(obj,N,x,d)
|
|
N_available = min(numel(x),numel(d) * obj.sps);
|
|
N = min(N,N_available);
|
|
N = obj.sps * floor(N / obj.sps);
|
|
N = max(0,N);
|
|
end
|
|
|
|
function adaption_code = adaptionCode(obj)
|
|
if obj.adaption_technique == adaption_method.nlms
|
|
adaption_code = 1;
|
|
elseif obj.adaption_technique == adaption_method.lms
|
|
adaption_code = 2;
|
|
elseif obj.adaption_technique == adaption_method.rls
|
|
adaption_code = 3;
|
|
else
|
|
builtin("error","FFE_DCTracking:InvalidAdaptionTechnique", ...
|
|
"Unsupported FFE adaption technique.");
|
|
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
|