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This commit is contained in:
Silas Oettinghaus
492
Classes/04_DSP/Equalizer/FFE_plain.m
Normal file
492
Classes/04_DSP/Equalizer/FFE_plain.m
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classdef FFE_plain < handle
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% Plain feed-forward equalizer with training, DD mode, and BER mu tuning.
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%
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% This class intentionally contains only the core FFE tap adaptation logic.
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% MPI-specific DC tracking, A1/A2 suppression, and delayed update buffers
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% stay in FFE and the MPI_reduction classes.
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properties
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sps
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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
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dd_mode
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mu_dd
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epochs_dd
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dd_len_fraction
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P
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constellation
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decide
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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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end
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methods
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function obj = FFE_plain(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.decide = false
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options.save_debug = 0
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options.optmize_mus = 0
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options.mu_optimization_len = 2^15
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options.plot_mu_optimization = 0
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options.mu_optimization_fignum = 3010
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end
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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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obj.sps = floor(obj.sps);
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obj.order = floor(obj.order);
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obj.dd_len_fraction = min(max(obj.dd_len_fraction,0),1);
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obj.resetState();
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end
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function [X,Noi] = process(obj, X, D)
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X = X.normalize("mode","rms");
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obj.constellation = unique(D.signal);
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obj.resetState();
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if obj.optmize_mus
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obj.optimizeMus(X.signal,D.signal);
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obj.resetState();
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end
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training = true;
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showviz = false;
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obj.equalize(X.signal,D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
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obj.e_tr = obj.e;
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training = false;
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n = X.length;
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if obj.dd_mode
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[signal,decision] = obj.equalize(X.signal,D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
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else
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[signal,decision] = obj.equalize(X.signal,D.signal,0,1,n,training,showviz);
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end
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if obj.decide
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X.signal = decision;
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else
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X.signal = signal;
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end
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X.fs = D.fs;
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X = X.logbookentry([num2str(obj.order),' tap FFE']);
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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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unused_showviz = showviz; %#ok<NASGU>
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x = x(:);
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d = d(:);
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N = obj.validSampleLength(N,x,d);
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n_symbols = N / obj.sps;
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y = zeros(n_symbols,1);
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d_hat = zeros(n_symbols,1);
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if n_symbols == 0
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return
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end
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if isempty(obj.constellation)
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obj.constellation = unique(d);
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end
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decision_constellation = obj.constellation(:);
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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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lambda = mu;
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mask = ones(obj.order,1);
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maincursor_pos = ceil(length(obj.e)/2);
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adaption_code = obj.adaptionCode();
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if mu == 0 || (~obj.dd_mode && ~training)
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epochs = 1;
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end
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debug_enabled = obj.save_debug;
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if debug_enabled
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obj.initializeDebug(n_symbols,training);
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end
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for epoch = 1:epochs
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symbol = 0;
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for sample = 1:obj.sps:N
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symbol = symbol + 1;
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grad = zeros(obj.order,1);
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update = zeros(obj.order,1);
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weight = 0;
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U = x(obj.order+sample-1:-1:sample);
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y(symbol,1) = (obj.e.*mask).' * U;
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if training
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d_hat(symbol,1) = d(symbol);
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else
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[~,symbol_idx] = min(abs(y(symbol) - decision_constellation));
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d_hat(symbol,1) = decision_constellation(symbol_idx);
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end
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err = d_hat(symbol) - y(symbol);
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if mu ~= 0 && (training || obj.dd_mode)
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switch adaption_code
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case 1
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weight = mu / ((U.'*U) + eps);
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grad = err * U;
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update = grad * weight;
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obj.e = obj.e + update;
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case 2
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weight = mu;
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grad = err * U;
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update = grad * weight;
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obj.e = obj.e + update;
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case 3
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denom = lambda + U.' * obj.P * U;
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k = (obj.P * U) / denom;
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update = k * err;
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obj.e = obj.e + update;
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obj.P = (1/lambda) * (obj.P - k * (U.' * obj.P));
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end
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end
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if debug_enabled && epoch == 1
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obj.debug_struct.error_first_epoch(1,symbol) = err * err';
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end
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if debug_enabled && epoch == epochs
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error_power = err * err';
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update_power = update.'*update ./ (sqrt((obj.e.'*obj.e) / obj.order) + eps);
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obj.debug_struct.error(1,symbol) = error_power;
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obj.debug_struct.main_cursor(1,symbol) = abs(obj.e(maincursor_pos));
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obj.debug_struct.mu_nlms(1,symbol) = weight;
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obj.debug_struct.update_gradient(1,symbol) = grad.'*grad;
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if training
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obj.debug_struct.error_tr(1,symbol) = error_power;
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obj.debug_struct.update_tr(1,symbol) = update_power;
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else
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obj.debug_struct.error_dd(1,symbol) = error_power;
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obj.debug_struct.update(1,symbol) = update_power;
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end
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end
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end
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end
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end
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function optimizeMus(obj,x,d)
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[x_opt,d_opt,N_opt] = obj.optimizationSignals(x,d);
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switch obj.adaption_technique
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case adaption_method.lms
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mu_range = [1e-5, 1e-2];
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case adaption_method.nlms
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mu_range = [1e-3, 5e-1];
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case adaption_method.rls
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mu_range = [0.98, 0.99999];
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otherwise
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builtin("error","FFE_plain:InvalidAdaptionTechnique", ...
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"Unsupported FFE adaption technique.");
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end
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vars = optimizableVariable("mu_tr",mu_range,"Transform","log");
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if obj.dd_mode
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vars = [vars, optimizableVariable("mu_dd",mu_range,"Transform","log")];
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end
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obj.mu_optimization_iter = 0;
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fprintf("FFE_plain mu opt uses %d samples / %d symbols\n",N_opt,numel(d_opt));
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obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x_opt,d_opt),vars, ...
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"MaxObjectiveEvaluations",20, ...
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"AcquisitionFunctionName","expected-improvement-plus", ...
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"IsObjectiveDeterministic",false, ...
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"Verbose",0, ...
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"PlotFcn",[]);
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obj.mu_tr = obj.mu_optimization.XAtMinObjective.mu_tr;
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if obj.dd_mode
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obj.mu_dd = obj.mu_optimization.XAtMinObjective.mu_dd;
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fprintf("\nFFE_plain mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e\n", ...
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obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective);
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else
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fprintf("\nFFE_plain mu opt done: mu_tr=%9.3e, BER=%9.3e\n", ...
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obj.mu_tr,obj.mu_optimization.MinObjective);
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end
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if obj.plot_mu_optimization
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obj.plotMuOptimization();
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end
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end
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function objective = muObjective(obj,params,x,d)
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old_state = obj.captureObjectiveState();
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cleanup = onCleanup(@()obj.restoreObjectiveState(old_state));
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obj.save_debug = 0;
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obj.resetState();
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N_tr = min(obj.len_tr,numel(x));
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obj.equalize(x,d,params.mu_tr,obj.epochs_tr,N_tr,true,false);
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if obj.dd_mode
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[signal,~] = obj.equalize(x,d,params.mu_dd,obj.epochs_dd,numel(x),false,false);
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else
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[signal,~] = obj.equalize(x,d,0,1,numel(x),false,false);
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end
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[ber,errors] = obj.berObjective(signal,d);
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objective = ber;
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if ~isfinite(objective)
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objective = inf;
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end
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obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
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if obj.dd_mode
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fprintf("\rFFE_plain mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,params.mu_tr,params.mu_dd,ber,errors);
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else
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fprintf("\rFFE_plain mu opt %02d: mu_tr=%9.3e, BER=%9.3e, errors=%d", ...
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obj.mu_optimization_iter,params.mu_tr,ber,errors);
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end
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clear cleanup
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end
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function [x_opt,d_opt,N_opt] = optimizationSignals(obj,x,d,opt_len)
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if nargin < 4
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opt_len = obj.mu_optimization_len;
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end
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N_available = min(numel(x),numel(d) * obj.sps);
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if isempty(opt_len) || opt_len <= 0 || isinf(opt_len)
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N_opt = N_available;
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else
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N_opt = min(N_available,max(obj.len_tr,opt_len));
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end
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N_opt = obj.sps * floor(N_opt / obj.sps);
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N_opt = max(obj.sps,N_opt);
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n_symbols = N_opt / obj.sps;
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x_opt = x(1:N_opt);
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d_opt = d(1:n_symbols);
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end
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function [ber,errors] = berObjective(~,signal,d)
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M = numel(unique(d));
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mapper = PAMmapper(M,0);
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eq_signal_sd = Signal(signal);
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eq_signal_hd = mapper.quantize(eq_signal_sd);
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tx_symbols = Signal(d);
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rx_bits = mapper.demap(eq_signal_hd);
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tx_bits = mapper.demap(tx_symbols);
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[~,errors,ber,~] = calc_ber(rx_bits.signal,tx_bits.signal, ...
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"skip_front",1000, ...
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"skip_end",0, ...
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"returnErrorLocation",1);
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end
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function plotMuOptimization(obj)
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if isempty(obj.mu_optimization)
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return
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end
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X = obj.mu_optimization.XTrace;
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objective = obj.mu_optimization.ObjectiveTrace;
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objective = objective(:);
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valid = isfinite(objective);
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if isempty(X) || ~any(valid)
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return
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end
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var_names = X.Properties.VariableNames;
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n_vars = numel(var_names);
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eval_idx = (1:numel(objective)).';
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objective_plot = obj.positiveObjectiveForLogPlot(objective);
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best_plot = obj.positiveObjectiveForLogPlot(cummin(objective));
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figure(obj.mu_optimization_fignum);
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clf;
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t = tiledlayout(2,2,"TileSpacing","compact","Padding","compact");
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title(t,"FFE_plain Bayesian mu optimization");
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nexttile;
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h_candidate = semilogy(eval_idx,objective_plot,"o-","DisplayName","candidate");
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obj.addOptimizationDataTips(h_candidate,X,objective,objective_plot,eval_idx,var_names);
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hold on;
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h_best_trace = semilogy(eval_idx,best_plot,"k-","LineWidth",1.2,"DisplayName","best so far");
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h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
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h_best_trace.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Best BER",best_plot);
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grid on;
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xlabel("Evaluation");
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ylabel("BER");
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legend("Location","best");
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if n_vars < 2
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return
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end
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pairs = nchoosek(1:n_vars,2);
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n_pair_plots = min(size(pairs,1),3);
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[~,best_idx] = min(objective);
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for pair_idx = 1:n_pair_plots
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nexttile;
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x_name = var_names{pairs(pair_idx,1)};
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y_name = var_names{pairs(pair_idx,2)};
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x_data = X.(x_name);
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y_data = X.(y_name);
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c_data = log10(objective_plot);
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h_scatter = scatter(log10(x_data),log10(y_data),35,c_data,"filled");
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obj.addOptimizationDataTips(h_scatter,X,objective,objective_plot,eval_idx,var_names);
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hold on;
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h_best = plot(log10(x_data(best_idx)),log10(y_data(best_idx)),"kp", ...
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"MarkerSize",12, ...
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"MarkerFaceColor","y", ...
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"DisplayName","best");
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obj.addOptimizationDataTips(h_best,X(best_idx,:),objective(best_idx),objective_plot(best_idx),eval_idx(best_idx),var_names);
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grid on;
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xlabel("log10(" + string(x_name) + ")");
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ylabel("log10(" + string(y_name) + ")");
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cb = colorbar;
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cb.Label.String = "log10(BER)";
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title(string(x_name) + " vs " + string(y_name));
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end
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end
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function addOptimizationDataTips(~,plot_handle,X,objective,objective_plot,eval_idx,var_names)
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plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("Evaluation",eval_idx);
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plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER",objective);
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plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow("BER shown",objective_plot);
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for var_idx = 1:numel(var_names)
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var_name = var_names{var_idx};
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plot_handle.DataTipTemplate.DataTipRows(end+1) = dataTipTextRow(var_name,X.(var_name));
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end
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end
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function objective_plot = positiveObjectiveForLogPlot(~,objective)
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objective_plot = objective;
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positive_values = objective(isfinite(objective) & objective > 0);
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if isempty(positive_values)
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floor_value = 1e-12;
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else
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floor_value = min(positive_values) / 10;
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end
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objective_plot(~isfinite(objective_plot) | objective_plot <= 0) = floor_value;
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end
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function state = captureObjectiveState(obj)
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state.e = obj.e;
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state.e_tr = obj.e_tr;
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state.error = obj.error;
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state.P = obj.P;
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state.save_debug = obj.save_debug;
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state.debug_struct = obj.debug_struct;
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end
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function restoreObjectiveState(obj,state)
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obj.e = state.e;
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obj.e_tr = state.e_tr;
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obj.error = state.error;
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obj.P = state.P;
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obj.save_debug = state.save_debug;
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obj.debug_struct = state.debug_struct;
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end
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end
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methods (Access = private)
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function resetState(obj)
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obj.e = zeros(obj.order,1);
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obj.e_tr = zeros(obj.order,1);
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obj.error = 0;
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obj.P = (1/0.05) * eye(obj.order);
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obj.debug_struct = struct();
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end
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function N = validSampleLength(obj,N,x,d)
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N_available = min(numel(x),numel(d) * obj.sps);
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N = min(N,N_available);
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N = obj.sps * floor(N / obj.sps);
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N = max(0,N);
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end
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function adaption_code = adaptionCode(obj)
|
||||
if obj.adaption_technique == adaption_method.nlms
|
||||
adaption_code = 1;
|
||||
elseif obj.adaption_technique == adaption_method.lms
|
||||
adaption_code = 2;
|
||||
elseif obj.adaption_technique == adaption_method.rls
|
||||
adaption_code = 3;
|
||||
else
|
||||
builtin("error","FFE_plain:InvalidAdaptionTechnique", ...
|
||||
"Unsupported FFE adaption technique.");
|
||||
end
|
||||
end
|
||||
|
||||
function initializeDebug(obj,n_symbols,training)
|
||||
obj.debug_struct.error = NaN(1,n_symbols);
|
||||
obj.debug_struct.error_first_epoch = NaN(1,n_symbols);
|
||||
obj.debug_struct.main_cursor = NaN(1,n_symbols);
|
||||
obj.debug_struct.mu_nlms = NaN(1,n_symbols);
|
||||
obj.debug_struct.update_gradient = NaN(1,n_symbols);
|
||||
|
||||
if training
|
||||
obj.debug_struct.error_tr = NaN(1,n_symbols);
|
||||
obj.debug_struct.update_tr = NaN(1,n_symbols);
|
||||
else
|
||||
obj.debug_struct.error_dd = NaN(1,n_symbols);
|
||||
obj.debug_struct.update = NaN(1,n_symbols);
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
Reference in New Issue
Block a user