1465 lines
68 KiB
Matlab
1465 lines
68 KiB
Matlab
classdef FFE < handle
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% Implementation of plain and simple FFE.
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% 1) Training mode (stable performance when you use NLMS)
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% 2) Decision directed mode
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%LMS: mu in order of 0.001 for acceptable convergence speed
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%NLMS: mu in order of 0.01 for acceptable convergence speed
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%RLS: mu is lambda -> 0.99 -> 1 (has a strong dependency on this! use a loop to find out best values)
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% 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_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",len_tr, ...
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% "mu_dd",1e-1,"mu_tr",0.4,"order",50, ...
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% "sps",2,"decide",0,"optmize_mus",1,"dd_mode",1, ...
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% "adaption_technique","nlms","dc_tracking_mu",1.021e-05);
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properties
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sps % usually 2
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order
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e
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e_tr
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error
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len_tr
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mu_tr
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epochs_tr
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adaption_technique % nlms, lms, rls
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dd_mode % 1 or 0 to set DD-mode on or off
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mu_dd %weight update in dd mode
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epochs_dd
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dd_len_fraction
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% A1 moving-average input suppression
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dc_avg_bufferlength_a1
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dc_avg_update_blocklength_a1
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dc_smoothing_a1
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% A2 level-dependent residual suppression
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dc_level_avg_bufferlength_a2
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dc_level_update_blocklength_a2
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dc_smoothing_a2
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dc_level_decision_mode_a2
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dc_level_weights_a2
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% Adaptive DC-tracking loop
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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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% Delayed FFE tap update
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ffe_update_buffer_len
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e_dc
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P % covariance matrix of rls
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constellation % symbol constellation
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decide %wether to return the (hard) decisions or the result after FFE (soft)
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save_debug = 0;
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debug_struct
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optmize_mus = 0;
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mu_optimization
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mu_optimization_iter = 0;
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mu_optimization_len
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plot_mu_optimization = 0;
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mu_optimization_fignum = 3010;
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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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optimize_a2_level_weights = 0;
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a2_level_weight_optimization
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a2_level_weight_optimization_iter = 0;
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a2_level_weight_optimization_len
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a2_level_weight_optimization_max_evals
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a2_level_weight_max
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a2_level_weight_initial_stats
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end
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properties (Access = private)
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dc_tracking_alpha = 0.98
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dc_tracking_gamma = 1e-6
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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(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_avg_bufferlength_a1 = 0;
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options.dc_avg_update_blocklength_a1 = 0;
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options.dc_smoothing_a1 = 0;
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options.dc_level_avg_bufferlength_a2 = 0;
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options.dc_level_update_blocklength_a2 = 0;
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options.dc_smoothing_a2 = 0;
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options.dc_level_decision_mode_a2 = "residual";
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options.dc_level_weights_a2 = 0;
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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.ffe_update_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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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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options.optimize_a2_level_weights = 0;
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options.a2_level_weight_optimization_len = 2^15;
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options.a2_level_weight_optimization_max_evals = 30;
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options.a2_level_weight_max = 1;
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end
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fn = fieldnames(options);
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for n = 1:numel(fn)
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obj.(fn{n}) = options.(fn{n});
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end
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assert(obj.dc_tracking_buffer_len >= 0);
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assert(obj.ffe_update_buffer_len >= 0);
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assert(obj.dc_avg_bufferlength_a1 >= 0);
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assert(obj.dc_avg_update_blocklength_a1 >= 0);
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assert(obj.dc_level_avg_bufferlength_a2 >= 0);
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assert(obj.dc_level_update_blocklength_a2 >= 0);
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obj.e = zeros(obj.order,1);
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obj.e_dc = 0;
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obj.error = 0;
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obj.a2_level_weight_initial_stats = struct();
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obj.dc_avg_bufferlength_a1 = floor(obj.dc_avg_bufferlength_a1);
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obj.dc_avg_update_blocklength_a1 = floor(obj.dc_avg_update_blocklength_a1);
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obj.dc_smoothing_a1 = min(max(obj.dc_smoothing_a1,0),1);
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obj.dc_level_avg_bufferlength_a2 = floor(obj.dc_level_avg_bufferlength_a2);
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obj.dc_level_update_blocklength_a2 = floor(obj.dc_level_update_blocklength_a2);
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obj.dc_smoothing_a2 = min(max(obj.dc_smoothing_a2,0),1);
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obj.dc_level_decision_mode_a2 = string(obj.dc_level_decision_mode_a2);
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if ~any(obj.dc_level_decision_mode_a2 == ["residual", "tracked_levels"])
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builtin("error","FFE:InvalidA2DecisionMode", ...
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"dc_level_decision_mode_a2 must be 'residual' or 'tracked_levels'.");
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end
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obj.dc_tracking_buffer_len = floor(obj.dc_tracking_buffer_len);
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obj.ffe_update_buffer_len = floor(obj.ffe_update_buffer_len);
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obj.a2_level_weight_max = max(obj.a2_level_weight_max,0);
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if obj.dc_avg_bufferlength_a1 > 1 && (obj.dc_tracking_mu ~= 0 || obj.optimize_dc_tracking_params)
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warning("FFE:DCAvgWithDcTracking", ...
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"A1 moving-average DC suppression and dc_tracking_mu/adaptive DC tracking are alternative DC suppression paths and will be combined.");
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end
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a2_requested = obj.dc_level_avg_bufferlength_a2 > 1 && ...
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(any(obj.dc_level_weights_a2(:) ~= 0) || obj.optimize_a2_level_weights);
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if a2_requested && ...
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(obj.dc_tracking_mu ~= 0 || obj.optimize_dc_tracking_params || obj.dc_avg_bufferlength_a1 > 1)
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warning("FFE:DCLevelAvgWithOtherDcSuppression", ...
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"A2 level-dependent MPI suppression is enabled together with another DC/MPI suppression path; both corrections will be combined.");
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end
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end
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function [X,Noi] = process(obj, X, D)
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% actual processing of the signal (steps 1. - 3.)
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% 1 normalize RMS
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X = X.normalize("mode","rms");
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obj.constellation = unique(D.signal);
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obj.e_dc = 0;
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delta = 0.05;
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obj.P = (1/delta) * eye(obj.order);
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if obj.optmize_mus
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obj.optimizeMus(X.signal,D.signal);
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obj.e = zeros(obj.order,1);
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obj.e_dc = 0;
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obj.P = (1/delta) * eye(obj.order);
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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.e = zeros(obj.order,1);
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obj.e_dc = 0;
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obj.P = (1/delta) * eye(obj.order);
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end
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if obj.optimize_a2_level_weights
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obj.optimizeA2LevelWeights(X.signal,D.signal);
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obj.e = zeros(obj.order,1);
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obj.e_dc = 0;
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obj.P = (1/delta) * eye(obj.order);
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end
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% Training Mode
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training = 1;
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showviz = 0;
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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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% Decision Directed Mode
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n = X.length;
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training = 0;
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showviz = 0;
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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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% Output Signal
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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; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
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lbdesc = [num2str(obj.order),' tap FFE'];
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X = X.logbookentry(lbdesc); % append to logbook
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Noi = X;
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Noi = X - D;
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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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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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lambda = mu;
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n_symbols = ceil(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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err = zeros(n_symbols,1);
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true_err = zeros(n_symbols,1);
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constellation = obj.constellation;
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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:InvalidAdaptionTechnique", ...
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"Unsupported FFE adaption technique.");
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end
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adaption_is_rls = adaption_code == 3;
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if training
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mask = ones(obj.order,1);
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else
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mask = zeros(obj.order,1);
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mask(900:end) = 1;
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mask(ceil(length(obj.e)/2)) = 1;
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end
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mask = ones(obj.order,1);
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maincursor_pos=ceil(length(obj.e)/2);
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always_ideal_decision = 0;
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grad =0;
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weight = 0;
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update = 0;
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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_min = obj.dc_tracking_mu_min;
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dc_tracking_mu_max = obj.dc_tracking_mu_max;
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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,dc_tracking_mu_min),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,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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end
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ffe_buffer_enabled = obj.ffe_update_buffer_len > 1 && ~adaption_is_rls;
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if ffe_buffer_enabled
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ffe_update_buffer = NaN(obj.order,obj.ffe_update_buffer_len);
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end
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dc_avg_enabled = obj.dc_avg_bufferlength_a1 > 1;
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if dc_avg_enabled
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dc_avg_buffer_a1 = NaN(obj.dc_avg_bufferlength_a1,1);
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dc_avg_est_a1 = 0;
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dc_avg_update_blocklength_a1 = obj.dc_avg_update_blocklength_a1;
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if dc_avg_update_blocklength_a1 <= 0
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dc_avg_update_blocklength_a1 = obj.dc_avg_bufferlength_a1;
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end
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dc_avg_update_blocklength_a1 = max(1,floor(dc_avg_update_blocklength_a1));
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dc_avg_input_offset_a1 = floor(obj.order/2);
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% Hardware-like A1 is causal; dc_smoothing_a1 is reserved for offline variants.
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end
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dc_level_enabled = obj.dc_level_avg_bufferlength_a2 > 1 && any(obj.dc_level_weights_a2(:) ~= 0);
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if dc_level_enabled
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if isempty(constellation)
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builtin("error","FFE:MissingConstellation", ...
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"A2 level-dependent MPI suppression requires obj.constellation to be set.");
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end
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dc_level_decision_mode_a2 = obj.dc_level_decision_mode_a2;
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dc_level_track_decision_a2 = dc_level_decision_mode_a2 == "tracked_levels";
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n_levels = numel(constellation);
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if isscalar(obj.dc_level_weights_a2)
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dc_level_weight_by_level = repmat(obj.dc_level_weights_a2,n_levels,1);
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elseif numel(obj.dc_level_weights_a2) == n_levels
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dc_level_weight_by_level = obj.dc_level_weights_a2(:);
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else
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builtin("error","FFE:InvalidDCLevelWeights", ...
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"dc_level_weights_a2 must be scalar or have one entry per constellation level.");
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end
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dc_level_buffer_len_a2 = obj.dc_level_avg_bufferlength_a2;
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dc_level_err_buffer = NaN(n_levels,dc_level_buffer_len_a2);
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dc_level_err_buffer_pos_by_level = zeros(n_levels,1);
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dc_level_err_sum_by_level = zeros(n_levels,1);
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dc_level_buffer_valid_count_by_level = zeros(n_levels,1);
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dc_level_mpi_est_by_level = zeros(n_levels,1);
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dc_level_valid_count_by_level = zeros(n_levels,1);
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dc_level_update_blocklength_a2 = obj.dc_level_update_blocklength_a2;
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if dc_level_update_blocklength_a2 <= 0
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dc_level_update_blocklength_a2 = dc_level_buffer_len_a2;
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end
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dc_level_update_blocklength_a2 = max(1,floor(dc_level_update_blocklength_a2));
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dc_level_window_future_fraction = obj.dc_smoothing_a2; %#ok<NASGU> % reserved for delayed/offline A2 variants
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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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n_symbols_debug = n_symbols;
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obj.debug_struct.error = NaN(1,n_symbols_debug);
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obj.debug_struct.error_first_epoch = NaN(1,n_symbols_debug);
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obj.debug_struct.main_cursor = NaN(1,n_symbols_debug);
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obj.debug_struct.mu_nlms = NaN(1,n_symbols_debug);
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obj.debug_struct.update_gradient = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_tracking_mu_eff = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_tracking_est = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_avg_offset = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_level_mpi_est = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_level_weight = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_level_valid_count = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_level_symbol_idx = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_level_decision_level = NaN(1,n_symbols_debug);
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obj.debug_struct.dc_level_y_raw = NaN(1,n_symbols_debug);
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if training
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obj.debug_struct.error_tr = NaN(1,n_symbols_debug);
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obj.debug_struct.update_tr = NaN(1,n_symbols_debug);
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else
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obj.debug_struct.error_dd = NaN(1,n_symbols_debug);
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obj.debug_struct.update = NaN(1,n_symbols_debug);
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end
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end
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for epoch = 1 : epochs
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symbol = 0;
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% obj.e_dc = 0;
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for sample = 1 : obj.sps : N
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symbol = symbol+1;
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dc_tracking_mu_eff = 0;
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dc_avg_offset = 0;
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dc_level_mpi_est = 0;
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dc_level_weight = 0;
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dc_level_valid_count = 0;
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dc_level_symbol_idx = NaN;
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dc_level_decision_level = NaN;
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U = x(obj.order+sample-1:-1:sample);
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if dc_avg_enabled
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dc_avg_offset = dc_avg_est_a1;
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U = U - dc_avg_offset;
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end
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y(symbol,1) = obj.e_dc + (obj.e.*mask).' * U; % Calculating output of LMS __ * |
|
||
y_raw = y(symbol);
|
||
|
||
if dc_avg_enabled
|
||
dc_avg_input_idx = dc_avg_input_offset_a1 + sample;
|
||
dc_avg_buffer_a1 = circshift(dc_avg_buffer_a1,1);
|
||
dc_avg_buffer_a1(1) = x(dc_avg_input_idx);
|
||
if mod(symbol,dc_avg_update_blocklength_a1) == 0
|
||
dc_avg_est_a1 = mean(dc_avg_buffer_a1,"omitnan");
|
||
end
|
||
end
|
||
|
||
if training
|
||
d_hat(symbol,1) = d(symbol);
|
||
if isempty(constellation)
|
||
symbol_idx = NaN;
|
||
else
|
||
[~,symbol_idx] = min(abs(d_hat(symbol) - constellation));
|
||
dc_level_decision_level = constellation(symbol_idx);
|
||
end
|
||
else
|
||
if ~always_ideal_decision
|
||
if dc_level_enabled && dc_level_track_decision_a2
|
||
% Tracked-level A2 moves the decision constellation; y itself stays unchanged.
|
||
dc_level_ramp_by_level = min(dc_level_valid_count_by_level / dc_level_buffer_len_a2,1);
|
||
dc_level_decision_constellation = constellation + ...
|
||
dc_level_weight_by_level .* dc_level_ramp_by_level .* dc_level_mpi_est_by_level;
|
||
[~,symbol_idx] = min(abs(y(symbol) - dc_level_decision_constellation));
|
||
dc_level_decision_level = dc_level_decision_constellation(symbol_idx);
|
||
else
|
||
[~,symbol_idx] = min(abs(y(symbol) - constellation)); % decision for closest constellation point
|
||
dc_level_decision_level = constellation(symbol_idx);
|
||
end
|
||
d_hat(symbol,1) = constellation(symbol_idx);
|
||
else
|
||
d_hat(symbol,1) = d(symbol);
|
||
[~,symbol_idx] = min(abs(d_hat(symbol) - constellation));
|
||
dc_level_decision_level = constellation(symbol_idx);
|
||
end
|
||
end
|
||
|
||
dc_level_symbol_idx = symbol_idx;
|
||
if dc_level_enabled
|
||
dc_level_mpi_est = dc_level_mpi_est_by_level(symbol_idx);
|
||
dc_level_valid_count = dc_level_valid_count_by_level(symbol_idx);
|
||
dc_level_weight = dc_level_weight_by_level(symbol_idx) * ...
|
||
min(dc_level_valid_count / dc_level_buffer_len_a2,1);
|
||
|
||
if dc_level_track_decision_a2
|
||
dc_level_buffer_value = y_raw;
|
||
y(symbol,1) = y_raw;
|
||
if isnan(dc_level_decision_level)
|
||
dc_level_decision_level = constellation(symbol_idx) + ...
|
||
dc_level_weight * dc_level_mpi_est;
|
||
end
|
||
else
|
||
mpi_err = y_raw - d_hat(symbol);
|
||
dc_level_buffer_value = mpi_err;
|
||
y(symbol,1) = y_raw - dc_level_weight * dc_level_mpi_est;
|
||
|
||
if training
|
||
d_hat(symbol,1) = d(symbol);
|
||
else
|
||
if ~always_ideal_decision
|
||
[~,symbol_idx] = min(abs(y(symbol) - constellation));
|
||
d_hat(symbol,1) = constellation(symbol_idx);
|
||
dc_level_decision_level = constellation(symbol_idx);
|
||
else
|
||
d_hat(symbol,1) = d(symbol);
|
||
[~,symbol_idx] = min(abs(d_hat(symbol) - constellation));
|
||
dc_level_decision_level = constellation(symbol_idx);
|
||
end
|
||
end
|
||
end
|
||
|
||
dc_level_err_buffer_pos = dc_level_err_buffer_pos_by_level(dc_level_symbol_idx) + 1;
|
||
if dc_level_err_buffer_pos > dc_level_buffer_len_a2
|
||
dc_level_err_buffer_pos = 1;
|
||
end
|
||
dc_level_err_buffer_pos_by_level(dc_level_symbol_idx) = dc_level_err_buffer_pos;
|
||
|
||
dc_level_old_err = dc_level_err_buffer(dc_level_symbol_idx,dc_level_err_buffer_pos);
|
||
if isfinite(dc_level_old_err)
|
||
dc_level_err_sum_by_level(dc_level_symbol_idx) = ...
|
||
dc_level_err_sum_by_level(dc_level_symbol_idx) - dc_level_old_err;
|
||
dc_level_buffer_valid_count_by_level(dc_level_symbol_idx) = ...
|
||
dc_level_buffer_valid_count_by_level(dc_level_symbol_idx) - 1;
|
||
end
|
||
|
||
if isfinite(dc_level_buffer_value)
|
||
dc_level_err_buffer(dc_level_symbol_idx,dc_level_err_buffer_pos) = dc_level_buffer_value;
|
||
dc_level_err_sum_by_level(dc_level_symbol_idx) = ...
|
||
dc_level_err_sum_by_level(dc_level_symbol_idx) + dc_level_buffer_value;
|
||
dc_level_buffer_valid_count_by_level(dc_level_symbol_idx) = ...
|
||
dc_level_buffer_valid_count_by_level(dc_level_symbol_idx) + 1;
|
||
else
|
||
dc_level_err_buffer(dc_level_symbol_idx,dc_level_err_buffer_pos) = NaN;
|
||
end
|
||
|
||
if mod(symbol,dc_level_update_blocklength_a2) == 0
|
||
if dc_level_update_blocklength_a2 == 1
|
||
dc_level_valid_count_by_level(dc_level_symbol_idx) = ...
|
||
dc_level_buffer_valid_count_by_level(dc_level_symbol_idx);
|
||
if dc_level_valid_count_by_level(dc_level_symbol_idx) == 0
|
||
dc_level_mpi_est_by_level(dc_level_symbol_idx) = 0;
|
||
else
|
||
dc_level_mpi_est_by_level(dc_level_symbol_idx) = ...
|
||
dc_level_err_sum_by_level(dc_level_symbol_idx) / ...
|
||
dc_level_valid_count_by_level(dc_level_symbol_idx);
|
||
if dc_level_track_decision_a2
|
||
dc_level_mpi_est_by_level(dc_level_symbol_idx) = ...
|
||
dc_level_mpi_est_by_level(dc_level_symbol_idx) - ...
|
||
constellation(dc_level_symbol_idx);
|
||
end
|
||
end
|
||
else
|
||
dc_level_valid_count_by_level = dc_level_buffer_valid_count_by_level;
|
||
dc_level_has_valid = dc_level_valid_count_by_level > 0;
|
||
dc_level_mpi_est_by_level(:) = 0;
|
||
dc_level_mpi_est_by_level(dc_level_has_valid) = ...
|
||
dc_level_err_sum_by_level(dc_level_has_valid) ./ ...
|
||
dc_level_valid_count_by_level(dc_level_has_valid);
|
||
if dc_level_track_decision_a2
|
||
dc_level_mpi_est_by_level(dc_level_has_valid) = ...
|
||
dc_level_mpi_est_by_level(dc_level_has_valid) - ...
|
||
constellation(dc_level_has_valid);
|
||
end
|
||
end
|
||
end
|
||
end
|
||
|
||
% err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
|
||
err(symbol) = d_hat(symbol) - y(symbol); % Instantaneous error
|
||
|
||
true_err(symbol) = y(symbol) - d(symbol); % Instantaneous error
|
||
|
||
if 1 %training || obj.dd_mode
|
||
switch adaption_code
|
||
|
||
case 1
|
||
|
||
% mu used as update weight (suggestion: 0.01-0.05; bit higher during tr)
|
||
normU = ((U.'*U)) + eps;
|
||
weight = mu / normU;
|
||
grad = err(symbol) * U;
|
||
update = grad * weight;
|
||
|
||
|
||
case 2
|
||
|
||
% mu used as update weight (suggestion: 0.001)
|
||
weight = mu;
|
||
grad = err(symbol) * U;
|
||
update = grad * weight;
|
||
|
||
|
||
|
||
case 3
|
||
|
||
|
||
% RLS‐Gain:
|
||
denom = lambda + U.' * obj.P * U;
|
||
k = (obj.P * U) / denom;
|
||
|
||
% Gewichtsupdate:
|
||
update = k * err(symbol);
|
||
|
||
% P-Matrix‐Update:
|
||
|
||
|
||
end
|
||
|
||
if ffe_buffer_enabled
|
||
ffe_update_buffer = circshift(ffe_update_buffer,1,2);
|
||
ffe_update_buffer(:,1) = update;
|
||
if mod(symbol,obj.ffe_update_buffer_len) == 0
|
||
obj.e = obj.e + mean(ffe_update_buffer,2,"omitnan");
|
||
end
|
||
else
|
||
obj.e = obj.e + update;
|
||
end
|
||
|
||
if adaption_is_rls
|
||
obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
|
||
end
|
||
|
||
if dc_tracking_enabled
|
||
if dc_buffer_enabled
|
||
dc_tracking_err_buffer_pos = dc_tracking_err_buffer_pos + 1;
|
||
if dc_tracking_err_buffer_pos > obj.dc_tracking_buffer_len
|
||
dc_tracking_err_buffer_pos = 1;
|
||
end
|
||
|
||
dc_tracking_old_err = dc_tracking_err_buffer(dc_tracking_err_buffer_pos);
|
||
if isfinite(dc_tracking_old_err)
|
||
dc_tracking_err_sum = dc_tracking_err_sum - dc_tracking_old_err;
|
||
dc_tracking_valid_count = dc_tracking_valid_count - 1;
|
||
if dc_tracking_use_persistence
|
||
dc_tracking_abs_err_sum = dc_tracking_abs_err_sum - abs(dc_tracking_old_err);
|
||
end
|
||
end
|
||
|
||
dc_tracking_new_err = err(symbol);
|
||
if isfinite(dc_tracking_new_err)
|
||
dc_tracking_err_buffer(dc_tracking_err_buffer_pos) = dc_tracking_new_err;
|
||
dc_tracking_err_sum = dc_tracking_err_sum + dc_tracking_new_err;
|
||
dc_tracking_valid_count = dc_tracking_valid_count + 1;
|
||
if dc_tracking_use_persistence
|
||
dc_tracking_abs_err_sum = dc_tracking_abs_err_sum + abs(dc_tracking_new_err);
|
||
end
|
||
else
|
||
dc_tracking_err_buffer(dc_tracking_err_buffer_pos) = NaN;
|
||
end
|
||
|
||
if mod(symbol,obj.dc_tracking_buffer_len) == 0
|
||
if dc_tracking_valid_count == 0
|
||
dc_tracking_err_mean = 0;
|
||
if dc_tracking_use_persistence
|
||
dc_tracking_err_abs_mean = 0;
|
||
end
|
||
else
|
||
dc_tracking_err_mean = dc_tracking_err_sum / dc_tracking_valid_count;
|
||
if dc_tracking_use_persistence
|
||
dc_tracking_err_abs_mean = dc_tracking_abs_err_sum / dc_tracking_valid_count;
|
||
end
|
||
end
|
||
|
||
if dc_tracking_use_persistence
|
||
dc_tracking_persistence_scale = abs(dc_tracking_err_mean) / (dc_tracking_err_abs_mean + eps);
|
||
dc_tracking_persistence_scale = min(max(dc_tracking_persistence_scale,0),1);
|
||
dc_tracking_mu_eff = dc_tracking_mu * ...
|
||
(1 + dc_tracking_persistence_gain * dc_tracking_persistence_scale);
|
||
dc_tracking_mu_eff = min(max(dc_tracking_mu_eff,dc_tracking_mu_eff_min),dc_tracking_mu_eff_max);
|
||
else
|
||
dc_tracking_mu_eff = dc_tracking_base_mu_eff;
|
||
end
|
||
obj.e_dc = obj.e_dc + dc_tracking_mu_eff * dc_tracking_err_mean;
|
||
end
|
||
else
|
||
dc_tracking_err_mean = err(symbol);
|
||
if isfinite(dc_tracking_err_mean)
|
||
if dc_tracking_use_persistence
|
||
dc_tracking_err_abs_mean = abs(dc_tracking_err_mean);
|
||
dc_tracking_persistence_scale = abs(dc_tracking_err_mean) / ...
|
||
(dc_tracking_err_abs_mean + eps);
|
||
else
|
||
dc_tracking_mu_eff = dc_tracking_base_mu_eff;
|
||
end
|
||
else
|
||
dc_tracking_err_mean = 0;
|
||
if dc_tracking_use_persistence
|
||
dc_tracking_persistence_scale = 0;
|
||
else
|
||
dc_tracking_mu_eff = dc_tracking_base_mu_eff;
|
||
end
|
||
end
|
||
if dc_tracking_use_persistence
|
||
dc_tracking_persistence_scale = min(max(dc_tracking_persistence_scale,0),1);
|
||
dc_tracking_mu_eff = dc_tracking_mu * ...
|
||
(1 + dc_tracking_persistence_gain * dc_tracking_persistence_scale);
|
||
dc_tracking_mu_eff = min(max(dc_tracking_mu_eff,dc_tracking_mu_eff_min),dc_tracking_mu_eff_max);
|
||
end
|
||
obj.e_dc = obj.e_dc + dc_tracking_mu_eff * dc_tracking_err_mean;
|
||
end
|
||
end
|
||
end
|
||
|
||
%
|
||
% if debug_enabled && epoch == 1
|
||
% obj.debug_struct.error_first_epoch(1,symbol) = err(symbol) * err(symbol)';
|
||
% end
|
||
|
||
if debug_enabled && epoch == epochs
|
||
error_power = err(symbol) * err(symbol)';
|
||
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;
|
||
obj.debug_struct.dc_avg_offset(1,symbol) = dc_avg_offset;
|
||
obj.debug_struct.dc_level_mpi_est(1,symbol) = dc_level_mpi_est;
|
||
obj.debug_struct.dc_level_weight(1,symbol) = dc_level_weight;
|
||
obj.debug_struct.dc_level_valid_count(1,symbol) = dc_level_valid_count;
|
||
obj.debug_struct.dc_level_symbol_idx(1,symbol) = dc_level_symbol_idx;
|
||
obj.debug_struct.dc_level_decision_level(1,symbol) = dc_level_decision_level;
|
||
obj.debug_struct.dc_level_y_raw(1,symbol) = y_raw;
|
||
|
||
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
|
||
|
||
function optimizeMus(obj,x,d)
|
||
[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d);
|
||
|
||
switch obj.adaption_technique
|
||
case adaption_method.lms
|
||
mu_range = [1e-5, 1e-2];
|
||
case adaption_method.nlms
|
||
mu_range = [1e-3, 5e-1];
|
||
case adaption_method.rls
|
||
mu_range = [0.98, 0.99999];
|
||
end
|
||
dc_tracking_mu_range = [1e-5, 1e-1];
|
||
|
||
mu_tr_var = optimizableVariable("mu_tr",mu_range,"Transform","log");
|
||
vars = mu_tr_var;
|
||
if obj.dd_mode
|
||
vars = [vars, optimizableVariable("mu_dd",mu_range,"Transform","log")];
|
||
end
|
||
optimize_dc_tracking_mu = obj.dc_tracking_mu ~= 0;
|
||
if optimize_dc_tracking_mu
|
||
vars = [vars, optimizableVariable("dc_tracking_mu",dc_tracking_mu_range,"Transform","log")];
|
||
end
|
||
obj.mu_optimization_iter = 0;
|
||
fprintf("FFE mu opt uses %d samples / %d symbols\n",N_opt,numel(d_opt));
|
||
obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x_opt,d_opt),vars, ...
|
||
"MaxObjectiveEvaluations",20, ...
|
||
"AcquisitionFunctionName","expected-improvement-plus", ...
|
||
"IsObjectiveDeterministic",false, ...
|
||
"Verbose",0, ...
|
||
"PlotFcn",[]);
|
||
obj.mu_tr = obj.mu_optimization.XAtMinObjective.mu_tr;
|
||
if obj.dd_mode
|
||
obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
|
||
end
|
||
if optimize_dc_tracking_mu
|
||
obj.dc_tracking_mu = obj.mu_optimization.XAtMinObjective.dc_tracking_mu;
|
||
end
|
||
if obj.dd_mode && optimize_dc_tracking_mu
|
||
fprintf("\nFFE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e\n", ...
|
||
obj.mu_tr,obj.mu_dd,obj.dc_tracking_mu,obj.mu_optimization.MinObjective);
|
||
elseif obj.dd_mode
|
||
fprintf("\nFFE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e\n", ...
|
||
obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective);
|
||
elseif optimize_dc_tracking_mu
|
||
fprintf("\nFFE mu opt done: mu_tr=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e\n", ...
|
||
obj.mu_tr,obj.dc_tracking_mu,obj.mu_optimization.MinObjective);
|
||
else
|
||
fprintf("\nFFE mu opt done: mu_tr=%9.3e, BER=%9.3e\n", ...
|
||
obj.mu_tr,obj.mu_optimization.MinObjective);
|
||
end
|
||
|
||
if obj.plot_mu_optimization
|
||
obj.plotMuOptimization();
|
||
end
|
||
end
|
||
|
||
function optimizeDcTrackingParams(obj,x,d)
|
||
[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d,obj.dc_tracking_optimization_len);
|
||
|
||
vars = optimizableVariable("dc_tracking_mu",[1e-5,1e-1],"Transform","log");
|
||
if obj.dc_tracking_adaptive_enabled
|
||
vars = [vars, ...
|
||
optimizableVariable("dc_tracking_persistence_gain",[0,2]), ...
|
||
optimizableVariable("dc_tracking_mu_eff_max",[1e-3,3e-1],"Transform","log")];
|
||
end
|
||
|
||
obj.dc_tracking_optimization_iter = 0;
|
||
fprintf("FFE DC opt uses fixed mu_tr=%9.3e, mu_dd=%9.3e on %d samples / %d symbols\n", ...
|
||
obj.mu_tr,obj.mu_dd,N_opt,numel(d_opt));
|
||
obj.dc_tracking_optimization = bayesopt(@(p)obj.dcTrackingObjective(p,x_opt,d_opt),vars, ...
|
||
"MaxObjectiveEvaluations",obj.dc_tracking_optimization_max_evals, ...
|
||
"AcquisitionFunctionName","expected-improvement-plus", ...
|
||
"IsObjectiveDeterministic",false, ...
|
||
"Verbose",0, ...
|
||
"PlotFcn",[]);
|
||
|
||
best = obj.dc_tracking_optimization.XAtMinObjective;
|
||
obj.dc_tracking_mu = best.dc_tracking_mu;
|
||
if obj.dc_tracking_adaptive_enabled
|
||
obj.dc_tracking_persistence_gain = best.dc_tracking_persistence_gain;
|
||
obj.dc_tracking_mu_eff_max = best.dc_tracking_mu_eff_max;
|
||
fprintf("\nFFE DC opt done: dc_tracking_mu=%9.3e, persistence_gain=%6.3f, mu_eff_max=%9.3e, objective=%9.3e\n", ...
|
||
obj.dc_tracking_mu,obj.dc_tracking_persistence_gain,obj.dc_tracking_mu_eff_max,obj.dc_tracking_optimization.MinObjective);
|
||
else
|
||
fprintf("\nFFE DC opt done: dc_tracking_mu=%9.3e, objective=%9.3e\n", ...
|
||
obj.dc_tracking_mu,obj.dc_tracking_optimization.MinObjective);
|
||
end
|
||
end
|
||
|
||
function optimizeA2LevelWeights(obj,x,d)
|
||
if obj.dc_level_avg_bufferlength_a2 <= 1
|
||
warning("FFE:A2OptimizationDisabled", ...
|
||
"A2 level-weight optimization requires dc_level_avg_bufferlength_a2 > 1.");
|
||
return
|
||
end
|
||
if obj.a2_level_weight_max <= 0
|
||
warning("FFE:A2OptimizationDisabled", ...
|
||
"A2 level-weight optimization requires a2_level_weight_max > 0.");
|
||
return
|
||
end
|
||
if isempty(obj.constellation)
|
||
obj.constellation = unique(d);
|
||
end
|
||
|
||
[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d,obj.a2_level_weight_optimization_len);
|
||
n_levels = numel(obj.constellation);
|
||
var_names = compose("dc_level_weight_a2_%d",1:n_levels);
|
||
vars_cell = cell(1,n_levels);
|
||
for level_idx = 1:n_levels
|
||
vars_cell{level_idx} = optimizableVariable(var_names(level_idx),[0,obj.a2_level_weight_max]);
|
||
end
|
||
vars = [vars_cell{:}];
|
||
|
||
[initial_weights,stats] = obj.a2LevelWeightInitialGuess(x_opt,d_opt);
|
||
current_weights = obj.expandA2LevelWeights(n_levels);
|
||
initial_matrix = [zeros(1,n_levels); initial_weights(:).'];
|
||
if any(current_weights ~= 0)
|
||
initial_matrix = [initial_matrix; current_weights(:).'];
|
||
end
|
||
initial_matrix = min(max(initial_matrix,0),obj.a2_level_weight_max);
|
||
initial_matrix = unique(initial_matrix,"rows","stable");
|
||
initial_x = array2table(initial_matrix,"VariableNames",cellstr(var_names));
|
||
[baseline_ber,baseline_errors] = obj.a2LevelWeightBer(initial_x(1,:),x_opt,d_opt);
|
||
[x_val,d_val,N_val] = obj.a2ValidationSignals(x,d,N_opt);
|
||
stats.baseline_ber = baseline_ber;
|
||
stats.baseline_errors = baseline_errors;
|
||
obj.a2_level_weight_initial_stats = stats;
|
||
|
||
obj.a2_level_weight_optimization_iter = 0;
|
||
max_evals = max(obj.a2_level_weight_optimization_max_evals,height(initial_x));
|
||
fprintf("FFE A2 opt uses fixed mu_tr=%9.3e, mu_dd=%9.3e on %d samples / %d symbols\n", ...
|
||
obj.mu_tr,obj.mu_dd,N_opt,numel(d_opt));
|
||
fprintf("FFE A2 opt validation uses %d samples / %d symbols\n",N_val,numel(d_val));
|
||
fprintf("FFE A2 opt init: baseline BER=%9.3e (%d errors), var_slope=%9.3e, weights=%s\n", ...
|
||
baseline_ber,baseline_errors,stats.variance_slope,mat2str(initial_weights(:).',3));
|
||
|
||
old_rng = rng;
|
||
cleanup_rng = onCleanup(@()rng(old_rng));
|
||
rng(42,"twister");
|
||
obj.a2_level_weight_optimization = bayesopt(@(p)obj.a2LevelWeightObjective(p,x_opt,d_opt,baseline_ber),vars, ...
|
||
"MaxObjectiveEvaluations",max_evals, ...
|
||
"InitialX",initial_x, ...
|
||
"AcquisitionFunctionName","expected-improvement-plus", ...
|
||
"IsObjectiveDeterministic",true, ...
|
||
"Verbose",0, ...
|
||
"PlotFcn",[]);
|
||
clear cleanup_rng
|
||
|
||
[best,best_validation_ber,best_validation_errors] = obj.selectA2LevelWeightsByValidation( ...
|
||
obj.a2_level_weight_optimization.XTrace, ...
|
||
obj.a2_level_weight_optimization.ObjectiveTrace, ...
|
||
x_val,d_val,initial_x);
|
||
best_opt = obj.a2_level_weight_optimization.XAtMinObjective;
|
||
best_opt_weights = obj.a2LevelWeightsFromParams(best_opt);
|
||
obj.dc_level_weights_a2 = obj.a2LevelWeightsFromParams(best);
|
||
obj.a2_level_weight_initial_stats.validation_ber = best_validation_ber;
|
||
obj.a2_level_weight_initial_stats.validation_errors = best_validation_errors;
|
||
obj.a2_level_weight_initial_stats.validation_weights = obj.dc_level_weights_a2;
|
||
fprintf("\nFFE A2 opt done: opt_weights=%s, opt_obj=%9.3e, validation_weights=%s, validation_BER=%9.3e (%d errors)\n", ...
|
||
mat2str(best_opt_weights(:).',3),obj.a2_level_weight_optimization.MinObjective, ...
|
||
mat2str(obj.dc_level_weights_a2(:).',3),best_validation_ber,best_validation_errors);
|
||
end
|
||
|
||
function [x_opt,d_opt,N_opt] = optimizationSignals(obj,x,d,opt_len)
|
||
if nargin < 4
|
||
opt_len = obj.mu_optimization_len;
|
||
end
|
||
|
||
N_available = min(numel(x),numel(d) * obj.sps);
|
||
|
||
if isempty(opt_len) || opt_len <= 0 || isinf(opt_len)
|
||
N_opt = N_available;
|
||
else
|
||
N_opt = min(N_available,max(obj.len_tr,opt_len));
|
||
end
|
||
|
||
N_opt = obj.sps * floor(N_opt / obj.sps);
|
||
N_opt = max(obj.sps,N_opt);
|
||
|
||
n_symbols = N_opt / obj.sps;
|
||
x_opt = x(1:N_opt);
|
||
d_opt = d(1:n_symbols);
|
||
end
|
||
|
||
function [x_val,d_val,N_val] = a2ValidationSignals(obj,x,d,N_opt)
|
||
N_available = min(numel(x),numel(d) * obj.sps);
|
||
N_val = min(N_opt,N_available);
|
||
if N_available <= N_opt
|
||
[x_val,d_val,N_val] = obj.optimizationSignals(x,d,N_opt);
|
||
return
|
||
end
|
||
|
||
start_symbol = floor((N_available - N_val) / obj.sps) + 1;
|
||
start_sample = (start_symbol - 1) * obj.sps + 1;
|
||
N_val = obj.sps * floor((N_available - start_sample + 1) / obj.sps);
|
||
N_val = max(obj.sps,N_val);
|
||
n_symbols = N_val / obj.sps;
|
||
x_val = x(start_sample:start_sample+N_val-1);
|
||
d_val = d(start_symbol:start_symbol+n_symbols-1);
|
||
end
|
||
|
||
function [best_params,best_ber,best_errors] = selectA2LevelWeightsByValidation(obj,x_trace,objective_trace,x_val,d_val,initial_x)
|
||
objective_trace = objective_trace(:);
|
||
objective_trace(~isfinite(objective_trace)) = inf;
|
||
[~,sort_idx] = sort(objective_trace,"ascend");
|
||
n_trace_candidates = min(8,numel(sort_idx));
|
||
candidate_x = x_trace(sort_idx(1:n_trace_candidates),:);
|
||
candidate_x = [initial_x; candidate_x];
|
||
candidate_x = unique(candidate_x,"rows","stable");
|
||
|
||
n_candidates = height(candidate_x);
|
||
validation_ber = inf(n_candidates,1);
|
||
validation_errors = nan(n_candidates,1);
|
||
for candidate_idx = 1:n_candidates
|
||
[validation_ber(candidate_idx),validation_errors(candidate_idx)] = ...
|
||
obj.a2LevelWeightBer(candidate_x(candidate_idx,:),x_val,d_val);
|
||
end
|
||
|
||
[best_ber,best_idx] = min(validation_ber);
|
||
best_errors = validation_errors(best_idx);
|
||
best_params = candidate_x(best_idx,:);
|
||
fprintf("FFE A2 validation: checked %d candidates, best weights=%s, BER=%9.3e, errors=%d\n", ...
|
||
n_candidates,mat2str(obj.a2LevelWeightsFromParams(best_params).',3),best_ber,best_errors);
|
||
end
|
||
|
||
function objective = muObjective(obj,params,x,d)
|
||
old_debug = obj.save_debug;
|
||
old_dc_tracking_mu = obj.dc_tracking_mu;
|
||
obj.save_debug = 0;
|
||
has_dc_tracking_mu = any(strcmp(params.Properties.VariableNames,"dc_tracking_mu"));
|
||
if has_dc_tracking_mu
|
||
obj.dc_tracking_mu = params.dc_tracking_mu;
|
||
end
|
||
obj.e = zeros(obj.order,1);
|
||
obj.e_dc = 0;
|
||
obj.P = (1/0.05) * eye(obj.order);
|
||
obj.debug_struct = struct();
|
||
N_tr = min(obj.len_tr,numel(x));
|
||
obj.equalize(x,d,params.mu_tr,obj.epochs_tr,N_tr,1,0);
|
||
if obj.dd_mode
|
||
[signal,~] = obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),0,0);
|
||
else
|
||
[signal,~] = obj.equalize(x,d,0,1,numel(x),0,0);
|
||
end
|
||
|
||
[ber,errors] = obj.berObjective(signal,d);
|
||
objective = ber;
|
||
if ~isfinite(objective)
|
||
objective = inf;
|
||
end
|
||
obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
|
||
if obj.dd_mode && has_dc_tracking_mu
|
||
fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e, errors=%d", ...
|
||
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,params.dc_tracking_mu,ber,errors);
|
||
elseif obj.dd_mode
|
||
fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e, errors=%d", ...
|
||
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,ber,errors);
|
||
elseif has_dc_tracking_mu
|
||
fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, dc_tracking_mu=%9.3e, BER=%9.3e, errors=%d", ...
|
||
obj.mu_optimization_iter,params.mu_tr,params.dc_tracking_mu,ber,errors);
|
||
else
|
||
fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, BER=%9.3e, errors=%d", ...
|
||
obj.mu_optimization_iter,params.mu_tr,ber,errors);
|
||
end
|
||
obj.save_debug = old_debug;
|
||
obj.dc_tracking_mu = old_dc_tracking_mu;
|
||
end
|
||
|
||
function objective = a2LevelWeightObjective(obj,params,x,d,baseline_ber)
|
||
if nargin < 5 || ~isfinite(baseline_ber)
|
||
baseline_ber = inf;
|
||
end
|
||
|
||
[ber,errors] = obj.a2LevelWeightBer(params,x,d);
|
||
objective = ber;
|
||
if isfinite(baseline_ber)
|
||
objective = objective + max(0,ber - baseline_ber);
|
||
end
|
||
if ~isfinite(objective)
|
||
objective = inf;
|
||
end
|
||
|
||
obj.a2_level_weight_optimization_iter = obj.a2_level_weight_optimization_iter + 1;
|
||
weights = obj.a2LevelWeightsFromParams(params);
|
||
fprintf("\rFFE A2 opt %02d: weights=%s, BER=%9.3e, obj=%9.3e, errors=%d", ...
|
||
obj.a2_level_weight_optimization_iter,mat2str(weights(:).',3),ber,objective,errors);
|
||
end
|
||
|
||
function [ber,errors] = a2LevelWeightBer(obj,params,x,d)
|
||
state = obj.captureObjectiveState();
|
||
cleanup = onCleanup(@()obj.restoreObjectiveState(state));
|
||
|
||
obj.dc_level_weights_a2 = obj.a2LevelWeightsFromParams(params);
|
||
obj.save_debug = 0;
|
||
obj.e = zeros(obj.order,1);
|
||
obj.e_dc = 0;
|
||
obj.P = (1/0.05) * eye(obj.order);
|
||
obj.debug_struct = struct();
|
||
|
||
N_tr = min(obj.len_tr,numel(x));
|
||
obj.equalize(x,d,obj.mu_tr,obj.epochs_tr,N_tr,1,0);
|
||
if obj.dd_mode
|
||
[signal,~] = obj.equalize(x,d,obj.mu_dd,obj.epochs_dd,numel(x),0,0);
|
||
else
|
||
[signal,~] = obj.equalize(x,d,0,1,numel(x),0,0);
|
||
end
|
||
|
||
[ber,errors] = obj.berObjective(signal,d);
|
||
end
|
||
|
||
function objective = dcTrackingObjective(obj,params,x,d)
|
||
state = obj.captureObjectiveState();
|
||
cleanup = onCleanup(@()obj.restoreObjectiveState(state));
|
||
|
||
obj.applyDcTrackingObjectiveParams(params);
|
||
obj.save_debug = 1;
|
||
obj.e = zeros(obj.order,1);
|
||
obj.e_dc = 0;
|
||
obj.P = (1/0.05) * eye(obj.order);
|
||
obj.debug_struct = struct();
|
||
|
||
N_tr = min(obj.len_tr,numel(x));
|
||
obj.equalize(x,d,obj.mu_tr,obj.epochs_tr,N_tr,1,0);
|
||
if obj.dd_mode
|
||
[signal,~] = obj.equalize(x,d,obj.mu_dd,obj.epochs_dd,numel(x),0,0);
|
||
else
|
||
[signal,~] = obj.equalize(x,d,0,1,numel(x),0,0);
|
||
end
|
||
|
||
[ber,errors] = obj.berObjective(signal,d);
|
||
[delay_symbols,delay_corr] = obj.dcTrackingDelayObjective(signal,d);
|
||
delay_penalty = obj.dc_tracking_optimization_delay_weight * abs(delay_symbols) / max(numel(d),1);
|
||
objective = ber + delay_penalty;
|
||
if ~isfinite(objective)
|
||
objective = inf;
|
||
end
|
||
|
||
obj.dc_tracking_optimization_iter = obj.dc_tracking_optimization_iter + 1;
|
||
if obj.dc_tracking_adaptive_enabled
|
||
fprintf("\rFFE 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", ...
|
||
obj.dc_tracking_optimization_iter,params.dc_tracking_mu,params.dc_tracking_persistence_gain,params.dc_tracking_mu_eff_max, ...
|
||
ber,delay_symbols,delay_corr,objective,errors);
|
||
else
|
||
fprintf("\rFFE DC opt %02d: dc_tracking_mu=%9.3e, BER=%9.3e, delay=%7.0f, corr=%6.3f, obj=%9.3e, errors=%d", ...
|
||
obj.dc_tracking_optimization_iter,params.dc_tracking_mu,ber,delay_symbols,delay_corr,objective,errors);
|
||
end
|
||
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 weights = a2LevelWeightsFromParams(~,params)
|
||
var_names = string(params.Properties.VariableNames);
|
||
weight_names = var_names(startsWith(var_names,"dc_level_weight_a2_"));
|
||
weight_idx = extractAfter(weight_names,"dc_level_weight_a2_");
|
||
weight_idx = str2double(weight_idx);
|
||
[weight_idx,sort_idx] = sort(weight_idx);
|
||
weight_names = weight_names(sort_idx);
|
||
weights = zeros(numel(weight_names),1);
|
||
for n = 1:numel(weight_names)
|
||
weights(weight_idx(n),1) = params.(char(weight_names(n)));
|
||
end
|
||
end
|
||
|
||
function weights = expandA2LevelWeights(obj,n_levels)
|
||
if isscalar(obj.dc_level_weights_a2)
|
||
weights = repmat(obj.dc_level_weights_a2,n_levels,1);
|
||
elseif numel(obj.dc_level_weights_a2) == n_levels
|
||
weights = obj.dc_level_weights_a2(:);
|
||
else
|
||
builtin("error","FFE:InvalidDCLevelWeights", ...
|
||
"dc_level_weights_a2 must be scalar or have one entry per constellation level.");
|
||
end
|
||
weights = min(max(weights,0),obj.a2_level_weight_max);
|
||
end
|
||
|
||
function [weights,stats] = a2LevelWeightInitialGuess(obj,x,d)
|
||
levels = obj.constellation(:);
|
||
if isempty(levels)
|
||
levels = unique(d(:));
|
||
end
|
||
n_levels = numel(levels);
|
||
n_symbols = min(numel(d),floor(numel(x) / obj.sps));
|
||
d = d(1:n_symbols);
|
||
rx_symbols = x(1:obj.sps:obj.sps*n_symbols);
|
||
rx_symbols = rx_symbols(:);
|
||
|
||
level_mean = NaN(n_levels,1);
|
||
level_variance = NaN(n_levels,1);
|
||
for level_idx = 1:n_levels
|
||
level_samples = rx_symbols(d == levels(level_idx));
|
||
level_mean(level_idx) = mean(level_samples,"omitnan");
|
||
level_variance(level_idx) = var(level_samples,0,"omitnan");
|
||
end
|
||
|
||
valid = isfinite(level_mean) & isfinite(level_variance);
|
||
variance_fit = level_variance;
|
||
slope_axis = "mean";
|
||
if nnz(valid) >= 2
|
||
if numel(unique(level_mean(valid))) >= 2
|
||
p = polyfit(level_mean(valid),level_variance(valid),1);
|
||
variance_fit = polyval(p,level_mean);
|
||
else
|
||
slope_axis = "level";
|
||
level_axis = (1:n_levels).';
|
||
p = polyfit(level_axis(valid),level_variance(valid),1);
|
||
variance_fit = polyval(p,(1:n_levels).');
|
||
end
|
||
else
|
||
p = [0, mean(level_variance(valid),"omitnan")];
|
||
end
|
||
|
||
if any(valid)
|
||
baseline = min(variance_fit(valid),[],"omitnan");
|
||
else
|
||
baseline = 0;
|
||
end
|
||
variance_score = max(variance_fit - baseline,0);
|
||
finite_score = isfinite(variance_score);
|
||
if ~any(finite_score) || max(variance_score(finite_score),[],"omitnan") <= 0
|
||
if any(valid)
|
||
baseline = min(level_variance(valid),[],"omitnan");
|
||
else
|
||
baseline = 0;
|
||
end
|
||
variance_score = max(level_variance - baseline,0);
|
||
finite_score = isfinite(variance_score);
|
||
end
|
||
|
||
weights = zeros(n_levels,1);
|
||
usable_score = valid & finite_score;
|
||
if any(usable_score)
|
||
max_score = max(variance_score(usable_score),[],"omitnan");
|
||
else
|
||
max_score = 0;
|
||
end
|
||
if isfinite(max_score) && max_score > 0
|
||
initial_weight_ceiling = min(0.7,obj.a2_level_weight_max);
|
||
weights(usable_score) = initial_weight_ceiling * variance_score(usable_score) ./ max_score;
|
||
end
|
||
weights = min(max(weights,0),obj.a2_level_weight_max);
|
||
|
||
stats = struct( ...
|
||
"levels",levels, ...
|
||
"mean",level_mean, ...
|
||
"variance",level_variance, ...
|
||
"variance_fit",variance_fit(:), ...
|
||
"variance_slope",p(1), ...
|
||
"slope_axis",slope_axis, ...
|
||
"initial_weights",weights);
|
||
end
|
||
|
||
function [e_dc_next,stats] = dcTrackingBlockUpdate(obj,e_dc_current,err_block,options)
|
||
arguments
|
||
obj
|
||
e_dc_current (1,1) double
|
||
err_block (:,1) double
|
||
options.mu_dc (1,1) double = NaN
|
||
options.persistence_gain (1,1) double = 0
|
||
options.mu_eff_min (1,1) double = NaN
|
||
options.mu_eff_max (1,1) double = NaN
|
||
end
|
||
|
||
if isnan(options.mu_dc)
|
||
options.mu_dc = obj.dc_tracking_mu;
|
||
end
|
||
if isnan(options.mu_eff_min)
|
||
options.mu_eff_min = obj.dc_tracking_mu_eff_min;
|
||
end
|
||
if isnan(options.mu_eff_max)
|
||
options.mu_eff_max = obj.dc_tracking_mu_eff_max;
|
||
end
|
||
|
||
valid_err = err_block(isfinite(err_block));
|
||
if isempty(valid_err)
|
||
err_mean = 0;
|
||
err_abs_mean = 0;
|
||
else
|
||
err_mean = mean(valid_err,"omitnan");
|
||
err_abs_mean = mean(abs(valid_err),"omitnan");
|
||
end
|
||
|
||
persistence_scale = abs(err_mean) / (err_abs_mean + eps);
|
||
persistence_scale = min(max(persistence_scale,0),1);
|
||
mu_eff = options.mu_dc * (1 + max(options.persistence_gain,0) * persistence_scale);
|
||
mu_eff = min(max(mu_eff,options.mu_eff_min),options.mu_eff_max);
|
||
update = mu_eff * err_mean;
|
||
e_dc_next = e_dc_current + update;
|
||
|
||
stats = struct( ...
|
||
"err_mean",err_mean, ...
|
||
"err_abs_mean",err_abs_mean, ...
|
||
"persistence_scale",persistence_scale, ...
|
||
"mu_eff",mu_eff, ...
|
||
"update",update);
|
||
end
|
||
|
||
function state = captureObjectiveState(obj)
|
||
state.e = obj.e;
|
||
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_alpha = obj.dc_tracking_alpha;
|
||
state.dc_tracking_gamma = obj.dc_tracking_gamma;
|
||
state.dc_tracking_persistence_gain = obj.dc_tracking_persistence_gain;
|
||
state.dc_tracking_power_exponent = obj.dc_tracking_power_exponent;
|
||
state.dc_tracking_mu_eff_max = obj.dc_tracking_mu_eff_max;
|
||
state.dc_level_weights_a2 = obj.dc_level_weights_a2;
|
||
state.a2_level_weight_initial_stats = obj.a2_level_weight_initial_stats;
|
||
end
|
||
|
||
function restoreObjectiveState(obj,state)
|
||
obj.e = state.e;
|
||
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_alpha = state.dc_tracking_alpha;
|
||
obj.dc_tracking_gamma = state.dc_tracking_gamma;
|
||
obj.dc_tracking_persistence_gain = state.dc_tracking_persistence_gain;
|
||
obj.dc_tracking_power_exponent = state.dc_tracking_power_exponent;
|
||
obj.dc_tracking_mu_eff_max = state.dc_tracking_mu_eff_max;
|
||
obj.dc_level_weights_a2 = state.dc_level_weights_a2;
|
||
obj.a2_level_weight_initial_stats = state.a2_level_weight_initial_stats;
|
||
end
|
||
|
||
function [ber,errors] = berObjective(~,signal,d)
|
||
M = numel(unique(d));
|
||
mapper = PAMmapper(M,0);
|
||
eq_signal_sd = Signal(signal);
|
||
eq_signal_hd = mapper.quantize(eq_signal_sd);
|
||
tx_symbols = Signal(d);
|
||
rx_bits = mapper.demap(eq_signal_hd);
|
||
tx_bits = mapper.demap(tx_symbols);
|
||
[~,errors,ber,~] = calc_ber(rx_bits.signal,tx_bits.signal, ...
|
||
"skip_front",1000, ...
|
||
"skip_end",0, ...
|
||
"returnErrorLocation",1);
|
||
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 plotMuOptimization(obj)
|
||
if isempty(obj.mu_optimization)
|
||
return
|
||
end
|
||
|
||
X = obj.mu_optimization.XTrace;
|
||
objective = obj.mu_optimization.ObjectiveTrace;
|
||
objective = objective(:);
|
||
valid = isfinite(objective);
|
||
|
||
if isempty(X) || ~any(valid)
|
||
return
|
||
end
|
||
|
||
var_names = X.Properties.VariableNames;
|
||
n_vars = numel(var_names);
|
||
eval_idx = (1:numel(objective)).';
|
||
objective_plot = obj.positiveObjectiveForLogPlot(objective);
|
||
best_plot = obj.positiveObjectiveForLogPlot(cummin(objective));
|
||
|
||
figure(obj.mu_optimization_fignum);
|
||
clf;
|
||
t = tiledlayout(2,2,"TileSpacing","compact","Padding","compact");
|
||
title(t,"FFE Bayesian mu optimization");
|
||
|
||
nexttile;
|
||
h_candidate = semilogy(eval_idx,objective_plot,"o-","DisplayName","candidate");
|
||
obj.addOptimizationDataTips(h_candidate,X,objective,objective_plot,eval_idx,var_names);
|
||
hold on;
|
||
h_best_trace = semilogy(eval_idx,best_plot,"k-","LineWidth",1.2,"DisplayName","best so far");
|
||
h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
|
||
h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Best BER",best_plot);
|
||
grid on;
|
||
xlabel("Evaluation");
|
||
ylabel("BER");
|
||
legend("Location","best");
|
||
|
||
if n_vars < 2
|
||
return
|
||
end
|
||
|
||
pairs = nchoosek(1:n_vars,2);
|
||
n_pair_plots = min(size(pairs,1),3);
|
||
[~,best_idx] = min(objective);
|
||
|
||
for pair_idx = 1:n_pair_plots
|
||
nexttile;
|
||
x_name = var_names{pairs(pair_idx,1)};
|
||
y_name = var_names{pairs(pair_idx,2)};
|
||
x_data = X.(x_name);
|
||
y_data = X.(y_name);
|
||
c_data = log10(objective_plot);
|
||
|
||
h_scatter = scatter(log10(x_data),log10(y_data),35,c_data,"filled");
|
||
obj.addOptimizationDataTips(h_scatter,X,objective,objective_plot,eval_idx,var_names);
|
||
hold on;
|
||
h_best = plot(log10(x_data(best_idx)),log10(y_data(best_idx)),"kp", ...
|
||
"MarkerSize",12, ...
|
||
"MarkerFaceColor","y", ...
|
||
"DisplayName","best");
|
||
obj.addOptimizationDataTips(h_best,X(best_idx,:),objective(best_idx),objective_plot(best_idx),eval_idx(best_idx),var_names);
|
||
grid on;
|
||
xlabel("log10(" + string(x_name) + ")");
|
||
ylabel("log10(" + string(y_name) + ")");
|
||
cb = colorbar;
|
||
cb.Label.String = "log10(BER)";
|
||
title(string(x_name) + " vs " + string(y_name));
|
||
end
|
||
end
|
||
|
||
function addOptimizationDataTips(~,plot_handle,X,objective,objective_plot,eval_idx,var_names)
|
||
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
|
||
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER",objective);
|
||
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER shown",objective_plot);
|
||
|
||
for var_idx = 1:numel(var_names)
|
||
var_name = var_names{var_idx};
|
||
plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow(var_name,X.(var_name));
|
||
end
|
||
end
|
||
|
||
function objective_plot = positiveObjectiveForLogPlot(~,objective)
|
||
objective_plot = objective;
|
||
positive_values = objective(isfinite(objective) & objective > 0);
|
||
|
||
if isempty(positive_values)
|
||
floor_value = 1e-12;
|
||
else
|
||
floor_value = min(positive_values) / 10;
|
||
end
|
||
|
||
objective_plot(~isfinite(objective_plot) | objective_plot <= 0) = floor_value;
|
||
end
|
||
end
|
||
end
|