MPI FFE with A1 and A2 algorithms
This commit is contained in:
Silas Oettinghaus
@@ -12,7 +12,7 @@ classdef FFE < handle
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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","mu_dc",1.021e-05);
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% "adaption_technique","nlms","dc_tracking_mu",1.021e-05);
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properties
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@@ -32,8 +32,24 @@ classdef FFE < handle
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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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mu_dc
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adaptive_dc_enabled
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% A1 moving-average input suppression
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dc_avg_bufferlength_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_smoothing_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_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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@@ -51,23 +67,29 @@ classdef FFE < handle
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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_params = 0;
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dc_optimization
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dc_optimization_iter = 0;
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dc_optimization_len
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dc_optimization_max_evals
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dc_optimization_delay_weight
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dc_optimization_smoothing_len
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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_alpha = 0.98
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dc_gamma = 1e-6
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dc_mu_min = 1e-6
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dc_mu_max = 3e-1
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dc_mu_eff_min = 0
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dc_mu_eff_max = inf
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dc_power_exponent = 1
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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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@@ -86,8 +108,20 @@ classdef FFE < handle
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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.mu_dc = 0;
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options.adaptive_dc_enabled = false;
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options.dc_avg_bufferlength_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_smoothing_a2 = 0;
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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_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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@@ -96,11 +130,15 @@ classdef FFE < handle
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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_params = 0;
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options.dc_optimization_len = 2^15;
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options.dc_optimization_max_evals = 20;
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options.dc_optimization_delay_weight = 1e-3;
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options.dc_optimization_smoothing_len = 501;
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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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@@ -109,9 +147,34 @@ classdef FFE < handle
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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_level_avg_bufferlength_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_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_smoothing_a2 = min(max(obj.dc_smoothing_a2,0),1);
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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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@@ -135,8 +198,15 @@ classdef FFE < handle
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obj.P = (1/delta) * eye(obj.order);
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end
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if obj.optimize_dc_params
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obj.optimizeDcParams(X.signal,D.signal);
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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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@@ -212,13 +282,56 @@ classdef FFE < handle
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P_err = 0;
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err_prev = 0;
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dc_alpha = obj.dc_alpha;
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dc_gamma = obj.dc_gamma;
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dc_mu_min = obj.dc_mu_min;
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dc_mu_max = obj.dc_mu_max;
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dc_mu_eff_min = obj.dc_mu_eff_min;
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dc_mu_eff_max = obj.dc_mu_eff_max;
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dc_power_exponent = obj.dc_power_exponent;
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dc_tracking_alpha = obj.dc_tracking_alpha;
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dc_tracking_gamma = obj.dc_tracking_gamma;
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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_power_exponent = obj.dc_tracking_power_exponent;
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dc_buffer_enabled = obj.dc_tracking_mu ~= 0 && obj.dc_tracking_buffer_len > 1;
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if dc_buffer_enabled
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e_dc_buffer = NaN(obj.dc_tracking_buffer_len,1);
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end
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ffe_buffer_enabled = obj.ffe_update_buffer_len > 1 && obj.adaption_technique ~= adaption_method.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_bufferlength_a1;
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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(obj.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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n_levels = numel(obj.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_err_buffer = NaN(n_levels,obj.dc_level_avg_bufferlength_a2);
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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_avg_bufferlength_a2;
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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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@@ -228,8 +341,14 @@ classdef FFE < handle
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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.mu_dc_eff = NaN(1,n_symbols_debug);
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obj.debug_struct.e_dc_eff = 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_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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@@ -246,14 +365,38 @@ classdef FFE < handle
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for sample = 1 : obj.sps : N
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symbol = symbol+1;
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mu_dc_eff = 0;
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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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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 __ * |
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y_raw = y(symbol);
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if dc_avg_enabled
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dc_avg_input_idx = dc_avg_input_offset_a1 + sample;
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dc_avg_buffer_a1 = circshift(dc_avg_buffer_a1,1);
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dc_avg_buffer_a1(1) = x(dc_avg_input_idx);
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if mod(symbol,dc_avg_update_blocklength_a1) == 0
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dc_avg_est_a1 = mean(dc_avg_buffer_a1,"omitnan");
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end
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end
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if training
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d_hat(symbol,1) = d(symbol);
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if isempty(obj.constellation)
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symbol_idx = NaN;
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else
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[~,symbol_idx] = min(abs(d_hat(symbol) - obj.constellation));
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end
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else
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if ~always_ideal_decision
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[~,symbol_idx] = min(abs(y(symbol) - obj.constellation)); % decision for closest constellation point
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@@ -263,6 +406,35 @@ classdef FFE < handle
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end
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end
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dc_level_symbol_idx = symbol_idx;
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if dc_level_enabled
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mpi_err = y_raw - d_hat(symbol);
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dc_level_mpi_est = dc_level_mpi_est_by_level(symbol_idx);
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dc_level_valid_count = dc_level_valid_count_by_level(symbol_idx);
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dc_level_weight = dc_level_weight_by_level(symbol_idx) * ...
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min(dc_level_valid_count / obj.dc_level_avg_bufferlength_a2,1);
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y(symbol,1) = y_raw - dc_level_weight * dc_level_mpi_est;
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if training
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d_hat(symbol,1) = d(symbol);
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else
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if ~always_ideal_decision
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[~,symbol_idx] = min(abs(y(symbol) - obj.constellation));
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d_hat(symbol,1) = obj.constellation(symbol_idx);
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else
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d_hat(symbol,1) = d(symbol);
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end
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end
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dc_level_err_buffer(dc_level_symbol_idx,:) = circshift(dc_level_err_buffer(dc_level_symbol_idx,:),1,2);
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dc_level_err_buffer(dc_level_symbol_idx,1) = mpi_err;
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if mod(symbol,dc_level_update_blocklength_a2) == 0
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dc_level_valid_count_by_level = sum(isfinite(dc_level_err_buffer),2);
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dc_level_mpi_est_by_level = mean(dc_level_err_buffer,2,"omitnan");
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dc_level_mpi_est_by_level(dc_level_valid_count_by_level == 0) = 0;
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end
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end
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% err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
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err(symbol) = d_hat(symbol) - y(symbol); % Instantaneous error
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@@ -278,7 +450,6 @@ classdef FFE < handle
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weight = mu / normU;
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grad = err(symbol) * U;
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update = grad * weight;
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obj.e = obj.e + update;
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case adaption_method.lms
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@@ -287,7 +458,6 @@ classdef FFE < handle
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weight = mu;
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grad = err(symbol) * U;
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update = grad * weight;
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obj.e = obj.e + update;
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@@ -300,27 +470,47 @@ classdef FFE < handle
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% Gewichtsupdate:
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update = k * err(symbol);
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obj.e = obj.e + update;
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% P-Matrix‐Update:
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obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
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end
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if obj.mu_dc ~= 0
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if obj.adaptive_dc_enabled
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delta_mu = dc_gamma * err(symbol) * err_prev * (U.'*U);
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obj.mu_dc = min(max(obj.mu_dc + delta_mu,dc_mu_min),dc_mu_max);
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if ffe_buffer_enabled
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ffe_update_buffer = circshift(ffe_update_buffer,1,2);
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ffe_update_buffer(:,1) = update;
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if mod(symbol,obj.ffe_update_buffer_len) == 0
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obj.e = obj.e + mean(ffe_update_buffer,2,"omitnan");
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end
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else
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obj.e = obj.e + update;
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end
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if obj.adaption_technique == adaption_method.rls
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obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
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end
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if obj.dc_tracking_mu ~= 0
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if obj.dc_tracking_adaptive_enabled
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delta_mu = dc_tracking_gamma * err(symbol) * err_prev * (U.'*U);
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obj.dc_tracking_mu = min(max(obj.dc_tracking_mu + delta_mu,dc_tracking_mu_min),dc_tracking_mu_max);
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err_prev = err(symbol);
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P_err = dc_alpha*P_err + (1-dc_alpha)*err(symbol)^2;
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mu_dc_eff = obj.mu_dc / ((P_err + eps)^dc_power_exponent);
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P_err = dc_tracking_alpha*P_err + (1-dc_tracking_alpha)*err(symbol)^2;
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dc_tracking_mu_eff = obj.dc_tracking_mu / ((P_err + eps)^dc_tracking_power_exponent);
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else
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mu_dc_eff = obj.mu_dc;
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dc_tracking_mu_eff = obj.dc_tracking_mu;
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end
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mu_dc_eff = min(max(mu_dc_eff,dc_mu_eff_min),dc_mu_eff_max);
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obj.e_dc = obj.e_dc + mu_dc_eff * err(symbol);
|
||||
dc_tracking_mu_eff = min(max(dc_tracking_mu_eff,dc_tracking_mu_eff_min),dc_tracking_mu_eff_max);
|
||||
if dc_buffer_enabled
|
||||
e_dc_buffer(1) = obj.e_dc + dc_tracking_mu_eff * err(symbol);
|
||||
e_dc_buffer = circshift(e_dc_buffer,1);
|
||||
if mod(symbol,obj.dc_tracking_buffer_len) == 0
|
||||
obj.e_dc = mean(e_dc_buffer,"omitnan");
|
||||
end
|
||||
else
|
||||
obj.e_dc = obj.e_dc + dc_tracking_mu_eff * err(symbol);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
@@ -336,8 +526,14 @@ classdef FFE < handle
|
||||
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.mu_dc_eff(1,symbol) = mu_dc_eff;
|
||||
obj.debug_struct.e_dc_eff(1,symbol) = obj.e_dc;
|
||||
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_y_raw(1,symbol) = y_raw;
|
||||
|
||||
if training
|
||||
obj.debug_struct.error_tr(1,symbol) = error_power;
|
||||
@@ -364,16 +560,16 @@ classdef FFE < handle
|
||||
case adaption_method.rls
|
||||
mu_range = [0.98, 0.99999];
|
||||
end
|
||||
mu_dc_range = [1e-5, 1e-1];
|
||||
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_mu_dc = obj.mu_dc ~= 0;
|
||||
if optimize_mu_dc
|
||||
vars = [vars, optimizableVariable("mu_dc",mu_dc_range,"Transform","log")];
|
||||
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));
|
||||
@@ -387,18 +583,18 @@ classdef FFE < handle
|
||||
if obj.dd_mode
|
||||
obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
|
||||
end
|
||||
if optimize_mu_dc
|
||||
obj.mu_dc = obj.mu_optimization.XAtMinObjective.mu_dc;
|
||||
if optimize_dc_tracking_mu
|
||||
obj.dc_tracking_mu = obj.mu_optimization.XAtMinObjective.dc_tracking_mu;
|
||||
end
|
||||
if obj.dd_mode && optimize_mu_dc
|
||||
fprintf("\nFFE mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, mu_dc=%9.3e, BER=%9.3e\n", ...
|
||||
obj.mu_tr,obj.mu_dd,obj.mu_dc,obj.mu_optimization.MinObjective);
|
||||
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_mu_dc
|
||||
fprintf("\nFFE mu opt done: mu_tr=%9.3e, mu_dc=%9.3e, BER=%9.3e\n", ...
|
||||
obj.mu_tr,obj.mu_dc,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);
|
||||
@@ -409,43 +605,99 @@ classdef FFE < handle
|
||||
end
|
||||
end
|
||||
|
||||
function optimizeDcParams(obj,x,d)
|
||||
[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d,obj.dc_optimization_len);
|
||||
function optimizeDcTrackingParams(obj,x,d)
|
||||
[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d,obj.dc_tracking_optimization_len);
|
||||
|
||||
vars = optimizableVariable("mu_dc",[1e-5,1e-1],"Transform","log");
|
||||
if obj.adaptive_dc_enabled
|
||||
vars = optimizableVariable("dc_tracking_mu",[1e-5,1e-1],"Transform","log");
|
||||
if obj.dc_tracking_adaptive_enabled
|
||||
vars = [vars, ...
|
||||
optimizableVariable("dc_alpha",[0.85,0.995]), ...
|
||||
optimizableVariable("dc_gamma",[1e-7,3e-5],"Transform","log"), ...
|
||||
optimizableVariable("dc_power_exponent",[0,1]), ...
|
||||
optimizableVariable("dc_mu_eff_max",[1e-3,3e-1],"Transform","log")];
|
||||
optimizableVariable("dc_tracking_alpha",[0.85,0.995]), ...
|
||||
optimizableVariable("dc_tracking_gamma",[1e-7,3e-5],"Transform","log"), ...
|
||||
optimizableVariable("dc_tracking_power_exponent",[0,2]), ...
|
||||
optimizableVariable("dc_tracking_mu_eff_max",[1e-3,3e-1],"Transform","log")];
|
||||
end
|
||||
|
||||
obj.dc_optimization_iter = 0;
|
||||
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_optimization = bayesopt(@(p)obj.dcObjective(p,x_opt,d_opt),vars, ...
|
||||
"MaxObjectiveEvaluations",obj.dc_optimization_max_evals, ...
|
||||
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_optimization.XAtMinObjective;
|
||||
obj.mu_dc = best.mu_dc;
|
||||
if obj.adaptive_dc_enabled
|
||||
obj.dc_alpha = best.dc_alpha;
|
||||
obj.dc_gamma = best.dc_gamma;
|
||||
obj.dc_power_exponent = best.dc_power_exponent;
|
||||
obj.dc_mu_eff_max = best.dc_mu_eff_max;
|
||||
fprintf("\nFFE DC opt done: mu_dc=%9.3e, alpha=%6.3f, gamma=%9.3e, p=%5.2f, mu_eff_max=%9.3e, objective=%9.3e\n", ...
|
||||
obj.mu_dc,obj.dc_alpha,obj.dc_gamma,obj.dc_power_exponent,obj.dc_mu_eff_max,obj.dc_optimization.MinObjective);
|
||||
best = obj.dc_tracking_optimization.XAtMinObjective;
|
||||
obj.dc_tracking_mu = best.dc_tracking_mu;
|
||||
if obj.dc_tracking_adaptive_enabled
|
||||
obj.dc_tracking_alpha = best.dc_tracking_alpha;
|
||||
obj.dc_tracking_gamma = best.dc_tracking_gamma;
|
||||
obj.dc_tracking_power_exponent = best.dc_tracking_power_exponent;
|
||||
obj.dc_tracking_mu_eff_max = best.dc_tracking_mu_eff_max;
|
||||
fprintf("\nFFE DC opt done: dc_tracking_mu=%9.3e, alpha=%6.3f, gamma=%9.3e, p=%5.2f, mu_eff_max=%9.3e, objective=%9.3e\n", ...
|
||||
obj.dc_tracking_mu,obj.dc_tracking_alpha,obj.dc_tracking_gamma,obj.dc_tracking_power_exponent,obj.dc_tracking_mu_eff_max,obj.dc_tracking_optimization.MinObjective);
|
||||
else
|
||||
fprintf("\nFFE DC opt done: mu_dc=%9.3e, objective=%9.3e\n", ...
|
||||
obj.mu_dc,obj.dc_optimization.MinObjective);
|
||||
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 = 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));
|
||||
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 init: var_slope=%9.3e, weights=%s\n", ...
|
||||
stats.variance_slope,mat2str(initial_weights(:).',3));
|
||||
|
||||
obj.a2_level_weight_optimization = bayesopt(@(p)obj.a2LevelWeightObjective(p,x_opt,d_opt),vars, ...
|
||||
"MaxObjectiveEvaluations",max_evals, ...
|
||||
"InitialX",initial_x, ...
|
||||
"AcquisitionFunctionName","expected-improvement-plus", ...
|
||||
"IsObjectiveDeterministic",false, ...
|
||||
"Verbose",0, ...
|
||||
"PlotFcn",[]);
|
||||
|
||||
best = obj.a2_level_weight_optimization.XAtMinObjective;
|
||||
obj.dc_level_weights_a2 = obj.a2LevelWeightsFromParams(best);
|
||||
fprintf("\nFFE A2 opt done: weights=%s, BER=%9.3e\n", ...
|
||||
mat2str(obj.dc_level_weights_a2(:).',3),obj.a2_level_weight_optimization.MinObjective);
|
||||
end
|
||||
|
||||
function [x_opt,d_opt,N_opt] = optimizationSignals(obj,x,d,opt_len)
|
||||
if nargin < 4
|
||||
opt_len = obj.mu_optimization_len;
|
||||
@@ -469,20 +721,22 @@ classdef FFE < handle
|
||||
|
||||
function objective = muObjective(obj,params,x,d)
|
||||
old_debug = obj.save_debug;
|
||||
old_mu_dc = obj.mu_dc;
|
||||
old_dc_tracking_mu = obj.dc_tracking_mu;
|
||||
obj.save_debug = 0;
|
||||
has_mu_dc = any(strcmp(params.Properties.VariableNames,"mu_dc"));
|
||||
if has_mu_dc
|
||||
obj.mu_dc = params.mu_dc;
|
||||
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));
|
||||
[signal,~] = obj.equalize(x,d,params.mu_tr,obj.epochs_tr,N_tr,1,0);
|
||||
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);
|
||||
@@ -491,28 +745,58 @@ classdef FFE < handle
|
||||
objective = inf;
|
||||
end
|
||||
obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
|
||||
if obj.dd_mode && has_mu_dc
|
||||
fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, mu_dc=%9.3e, BER=%9.3e, errors=%d", ...
|
||||
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,params.mu_dc,ber,errors);
|
||||
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_mu_dc
|
||||
fprintf("\rFFE mu opt %02d: mu_tr=%9.3e, mu_dc=%9.3e, BER=%9.3e, errors=%d", ...
|
||||
obj.mu_optimization_iter,params.mu_tr,params.mu_dc,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.mu_dc = old_mu_dc;
|
||||
obj.dc_tracking_mu = old_dc_tracking_mu;
|
||||
end
|
||||
|
||||
function objective = dcObjective(obj,params,x,d)
|
||||
function objective = a2LevelWeightObjective(obj,params,x,d)
|
||||
state = obj.captureObjectiveState();
|
||||
cleanup = onCleanup(@()obj.restoreObjectiveState(state));
|
||||
|
||||
obj.applyDcObjectiveParams(params);
|
||||
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);
|
||||
objective = ber;
|
||||
if ~isfinite(objective)
|
||||
objective = inf;
|
||||
end
|
||||
|
||||
obj.a2_level_weight_optimization_iter = obj.a2_level_weight_optimization_iter + 1;
|
||||
fprintf("\rFFE A2 opt %02d: weights=%s, BER=%9.3e, errors=%d", ...
|
||||
obj.a2_level_weight_optimization_iter,mat2str(obj.dc_level_weights_a2(:).',3),ber,errors);
|
||||
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;
|
||||
@@ -520,60 +804,165 @@ classdef FFE < handle
|
||||
obj.debug_struct = struct();
|
||||
|
||||
N_tr = min(obj.len_tr,numel(x));
|
||||
[signal,~] = obj.equalize(x,d,obj.mu_tr,obj.epochs_tr,N_tr,1,0);
|
||||
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.dcDelayObjective(signal,d);
|
||||
delay_penalty = obj.dc_optimization_delay_weight * abs(delay_symbols) / max(numel(d),1);
|
||||
[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_optimization_iter = obj.dc_optimization_iter + 1;
|
||||
if obj.adaptive_dc_enabled
|
||||
fprintf("\rFFE DC opt %02d: mu_dc=%9.3e, alpha=%6.3f, gamma=%9.3e, p=%5.2f, mu_eff_max=%9.3e, BER=%9.3e, delay=%7.0f, corr=%6.3f, obj=%9.3e, errors=%d", ...
|
||||
obj.dc_optimization_iter,params.mu_dc,params.dc_alpha,params.dc_gamma,params.dc_power_exponent,params.dc_mu_eff_max, ...
|
||||
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, alpha=%6.3f, gamma=%9.3e, p=%5.2f, 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_alpha,params.dc_tracking_gamma,params.dc_tracking_power_exponent,params.dc_tracking_mu_eff_max, ...
|
||||
ber,delay_symbols,delay_corr,objective,errors);
|
||||
else
|
||||
fprintf("\rFFE DC opt %02d: mu_dc=%9.3e, BER=%9.3e, delay=%7.0f, corr=%6.3f, obj=%9.3e, errors=%d", ...
|
||||
obj.dc_optimization_iter,params.mu_dc,ber,delay_symbols,delay_corr,objective,errors);
|
||||
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 applyDcObjectiveParams(obj,params)
|
||||
function applyDcTrackingObjectiveParams(obj,params)
|
||||
var_names = string(params.Properties.VariableNames);
|
||||
if any(var_names == "mu_dc")
|
||||
obj.mu_dc = params.mu_dc;
|
||||
if any(var_names == "dc_tracking_mu")
|
||||
obj.dc_tracking_mu = params.dc_tracking_mu;
|
||||
end
|
||||
if any(var_names == "dc_alpha")
|
||||
obj.dc_alpha = params.dc_alpha;
|
||||
if any(var_names == "dc_tracking_alpha")
|
||||
obj.dc_tracking_alpha = params.dc_tracking_alpha;
|
||||
end
|
||||
if any(var_names == "dc_gamma")
|
||||
obj.dc_gamma = params.dc_gamma;
|
||||
if any(var_names == "dc_tracking_gamma")
|
||||
obj.dc_tracking_gamma = params.dc_tracking_gamma;
|
||||
end
|
||||
if any(var_names == "dc_power_exponent")
|
||||
obj.dc_power_exponent = params.dc_power_exponent;
|
||||
if any(var_names == "dc_tracking_power_exponent")
|
||||
obj.dc_tracking_power_exponent = params.dc_tracking_power_exponent;
|
||||
end
|
||||
if any(var_names == "dc_mu_eff_max")
|
||||
obj.dc_mu_eff_max = params.dc_mu_eff_max;
|
||||
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 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.mu_dc = obj.mu_dc;
|
||||
state.dc_alpha = obj.dc_alpha;
|
||||
state.dc_gamma = obj.dc_gamma;
|
||||
state.dc_power_exponent = obj.dc_power_exponent;
|
||||
state.dc_mu_eff_max = obj.dc_mu_eff_max;
|
||||
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_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)
|
||||
@@ -582,11 +971,13 @@ classdef FFE < handle
|
||||
obj.P = state.P;
|
||||
obj.save_debug = state.save_debug;
|
||||
obj.debug_struct = state.debug_struct;
|
||||
obj.mu_dc = state.mu_dc;
|
||||
obj.dc_alpha = state.dc_alpha;
|
||||
obj.dc_gamma = state.dc_gamma;
|
||||
obj.dc_power_exponent = state.dc_power_exponent;
|
||||
obj.dc_mu_eff_max = state.dc_mu_eff_max;
|
||||
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_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)
|
||||
@@ -603,23 +994,23 @@ classdef FFE < handle
|
||||
"returnErrorLocation",1);
|
||||
end
|
||||
|
||||
function [delay_symbols,delay_corr] = dcDelayObjective(obj,signal,d)
|
||||
function [delay_symbols,delay_corr] = dcTrackingDelayObjective(obj,signal,d)
|
||||
delay_symbols = 0;
|
||||
delay_corr = 0;
|
||||
if ~isfield(obj.debug_struct,"e_dc_eff") || isempty(obj.debug_struct.e_dc_eff)
|
||||
if ~isfield(obj.debug_struct,"dc_tracking_est") || isempty(obj.debug_struct.dc_tracking_est)
|
||||
return
|
||||
end
|
||||
|
||||
smooth_len = obj.dc_optimization_smoothing_len;
|
||||
e_dc_eff_s = movmean(obj.debug_struct.e_dc_eff(:),smooth_len,"omitnan");
|
||||
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(e_dc_eff_s),numel(avg_lvl_dc));
|
||||
xcorr_len = min(numel(dc_tracking_est_s),numel(avg_lvl_dc));
|
||||
if xcorr_len < 2
|
||||
return
|
||||
end
|
||||
|
||||
inv_dc_xcorr = -e_dc_eff_s(1:xcorr_len);
|
||||
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");
|
||||
|
||||
Reference in New Issue
Block a user