restructure and organize
This commit is contained in:
Silas Oettinghaus
@@ -1,12 +1,10 @@
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classdef FFE_DCremoval_adaptive_mu < 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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% Eq = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
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% FFE variant for MPI/DC-removal experiments.
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% With dc_buffer_len <= 1, ffe_buffer_len <= 1 and no smoothing, this
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% follows FFE.m semantics so MPI-reduction changes can be isolated.
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properties
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sps % usually 2
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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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@@ -16,28 +14,37 @@ classdef FFE_DCremoval_adaptive_mu < handle
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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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mu_dc
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e_dc
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P
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dc_buffer_len
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adaptive_mu_mode
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ffe_buffer_len
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smoothing_buffer_length
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smoothing_buffer_update
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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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end
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methods
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function obj = FFE_DCremoval_adaptive_mu(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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@@ -45,23 +52,31 @@ classdef FFE_DCremoval_adaptive_mu < handle
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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 = 0.25;
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options.mu_dc = 0.05;
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options.dc_buffer_len = 1;
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options.ffe_buffer_len = 1;
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options.adaptive_mu_mode = 1;
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options.ffe_buffer_len = 1;
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options.smoothing_buffer_length = 0;
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options.smoothing_buffer_update = 0;
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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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end
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assert(options.dc_buffer_len>0);
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assert(options.dc_buffer_len >= 0);
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assert(options.ffe_buffer_len >= 0);
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assert(options.smoothing_buffer_length >= 0);
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if options.smoothing_buffer_length > 0
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assert(options.smoothing_buffer_update > 0);
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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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@@ -69,283 +84,328 @@ classdef FFE_DCremoval_adaptive_mu < handle
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end
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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.dc_buffer_len = floor(obj.dc_buffer_len);
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obj.ffe_buffer_len = floor(obj.ffe_buffer_len);
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obj.smoothing_buffer_length = floor(obj.smoothing_buffer_length);
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obj.smoothing_buffer_update = floor(obj.smoothing_buffer_update);
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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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% if obj.smoothing_buffer_length > 0
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% % Apply A1 filter smoothing
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% % Calculate the moving sum with the window size N1
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% moving_sum = movsum(X.signal, [obj.smoothing_buffer_length,0]);
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%
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% % Initialize the output smoothed signal
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% X.signal = X.signal - (1 / obj.smoothing_buffer_length) * moving_sum;
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% end
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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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% Training Mode
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training = 1;
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obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training);
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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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n = X.length;
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training = 0;
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[signal,decision]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,N,training);
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if obj.dd_mode
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n_dd = obj.ddLength(n);
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obj.equalize(X.signal,D.signal,obj.mu_dd,obj.epochs_dd,n_dd,training,showviz);
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end
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[signal,decision] = obj.applyCurrentTaps(X.signal,n);
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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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X.fs = D.fs;
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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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X = X.logbookentry(lbdesc);
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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_lms, epochs, N, training)
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% Equalize with adaptive DC-removal, VSS, and parallel-buffered DC updates
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% Added: FFE gradient buffering in DD mode (error buffer) with update every obj.dc_buffer_len symbols
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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_lms % LMS step-size (or 0 for NLMS)
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epochs % number of training/DD epochs
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N % number of samples to process
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training % boolean flag: true->training mode, false->DD mode
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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 = 0 %#ok<INUSD>
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end
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if isempty(obj.e)
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obj.e = zeros(obj.order,1);
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end
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% Zero-padding for filter memory
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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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% Initialize storage
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numSymbols = ceil(N/obj.sps);
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y = zeros(numSymbols,1);
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d_hat = zeros(numSymbols,1);
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err = NaN(numSymbols,numel(obj.constellation));
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e_dc_save= zeros(numSymbols,1);
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% DC-adaptation parameters
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P_err = 0; % running error power
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alpha = 0.98; % forgetting factor for error power
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err_prev = 0; % previous error sample for VSS correlation
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gamma_dc = 1e-6; % meta step-size for DC VSS
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mu_min = 1e-6; % lower bound for mu_dc
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mu_max = 3e-1; % upper bound for mu_dc
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% DC removal buffer
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L = obj.dc_buffer_len; % buffer length
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e_dc_buf = NaN(L,1);
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e_dc_est = 0;
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% FFE gradient buffer (DD mode only)
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L_grad = obj.ffe_buffer_len; % buffer length
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if ~training
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% each column holds one past gradient of length obj.order
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grad_buf = NaN(obj.order, L_grad);
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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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smth_buffer = zeros(1, obj.smoothing_buffer_length);
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smth_mean = 0;
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% Main loop
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for epoch = 1:epochs
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s = 0;
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for sample = 1:obj.sps:N
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s = s + 1;
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mask = ones(obj.order,1);
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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_buffer_enabled = obj.mu_dc ~= 0 && obj.dc_buffer_len > 1;
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adaptive_dc_enabled = dc_buffer_enabled && obj.adaptive_mu_mode;
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if dc_buffer_enabled
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e_dc_buffer = NaN(obj.dc_buffer_len,1);
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end
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ffe_buffer_enabled = ~training && obj.ffe_buffer_len > 1 && ...
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obj.adaption_technique ~= adaption_method.rls;
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if ffe_buffer_enabled
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grad_buffer = NaN(obj.order,obj.ffe_buffer_len);
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end
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if obj.smoothing_buffer_length > 0
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smoothing_buffer = zeros(1,obj.smoothing_buffer_length);
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smoothing_mean = 0;
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end
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P_err = 0;
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alpha = 0.98;
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err_prev = 0;
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gamma_dc = 1e-6;
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mu_min = 1e-6;
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mu_max = 3e-1;
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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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if obj.smoothing_buffer_length > 0
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smth_buffer = circshift(smth_buffer,1,2);
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smth_buffer(1) = x(sample);
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if mod(s, obj.smoothing_buffer_update) == 0
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smth_mean = mean(smth_buffer);
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smoothing_buffer = circshift(smoothing_buffer,1,2);
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smoothing_buffer(1) = x(sample);
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if mod(symbol,obj.smoothing_buffer_update) == 0
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smoothing_mean = mean(smoothing_buffer);
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end
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x(sample:sample+obj.sps-1) = x(sample:sample+obj.sps-1)-smth_mean;
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x(sample:sample+obj.sps-1) = x(sample:sample+obj.sps-1) - smoothing_mean;
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end
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U = x(obj.order+sample-1:-1:sample);
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%-- 1) filter output with DC correction
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y(s) = e_dc_est + obj.e.'*U;
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y(symbol,1) = obj.e_dc + (obj.e.*mask).' * U;
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%-- 2) decision
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if training
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[~, idx] = min(abs(d(s) - obj.constellation));
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d_hat(symbol,1) = d(symbol);
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else
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[~, idx] = min(abs(y(s) - obj.constellation));
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end
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d_hat(s) = obj.constellation(idx);
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%-- 3) error
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e_val = y(s) - d_hat(s);
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if epoch == epochs
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err(s,idx) = e_val;
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true_err(s,idx) = y(s) - d(s);
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end
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%-- 4) tap-weight update: training immediate, DD buffered
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if training
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% immediate update (LMS or NLMS)
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if mu_lms ~= 0
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obj.e = obj.e - mu_lms * e_val * U;
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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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normU = (U.'*U) + eps;
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obj.e = obj.e - e_val * U / normU;
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d_hat(symbol,1) = d(symbol);
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end
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else
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if 0
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% buffer gradient
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if mu_lms ~= 0
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grad = e_val * U;
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else
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end
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err(symbol) = d_hat(symbol) - y(symbol); %#ok<AGROW>
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true_err(symbol) = y(symbol) - d(symbol); %#ok<AGROW,NASGU>
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if training || obj.dd_mode
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switch obj.adaption_technique
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case adaption_method.lms
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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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case adaption_method.nlms
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normU = (U.'*U) + eps;
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grad = e_val * U / normU;
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end
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% shift and insert
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grad_buf = circshift(grad_buf, 1, 2);
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grad_buf(:,1) = grad;
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% update once every L symbols
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if mod(s, L_grad) == 0
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avg_grad = mean(grad_buf, 2, 'omitnan');
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if mu_lms ~= 0
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obj.e = obj.e - mu_lms * avg_grad;
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else
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obj.e = obj.e - avg_grad;
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end
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end
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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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case adaption_method.rls
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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(symbol);
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end
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end
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%-- 5) DC adaptation
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if obj.mu_dc ~= 0
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if obj.adaptive_mu_mode
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% VSS for mu_dc
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delta_mu = gamma_dc * e_val * err_prev * (U.'*U);
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obj.mu_dc = min(max(obj.mu_dc + delta_mu, mu_min), mu_max);
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err_prev = e_val;
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% DC buffer update & periodic estimate -- new "P_err" is "e_val^2"
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P_err = alpha*P_err + (1-alpha)*e_val^2;
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mu_dc_norm = obj.mu_dc / (P_err + eps);
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if ffe_buffer_enabled
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grad_buffer = circshift(grad_buffer,1,2);
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grad_buffer(:,1) = update;
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if mod(symbol,obj.ffe_buffer_len) == 0
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obj.e = obj.e + mean(grad_buffer,2,"omitnan");
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end
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else
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% DC buffer update & periodic estimate
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% P_err = alpha*P_err + (1-alpha)*e_val^2;
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% mu_dc_norm = obj.mu_dc / (P_err + eps);
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mu_dc_norm = obj.mu_dc;
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obj.e = obj.e + update;
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end
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e_dc_buf = circshift(e_dc_buf, 1);
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e_dc_buf(1) = e_dc_est - mu_dc_norm * e_val;
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if mod(s, L) == 0
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e_dc_est = median(e_dc_buf, 'omitnan');
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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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P_err_save(s) = P_err;
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% Pcorr_save(s) = e_val * err_prev;
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Ucorr_save(s) = (U.'*U);
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mu_dc_save(s) = mu_dc_norm;
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e_dc_save(s) = e_dc_est;
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if obj.mu_dc ~= 0
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if adaptive_dc_enabled
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delta_mu = gamma_dc * err(symbol) * err_prev * (U.'*U);
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obj.mu_dc = min(max(obj.mu_dc + delta_mu,mu_min),mu_max);
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err_prev = err(symbol);
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P_err = alpha*P_err + (1-alpha)*err(symbol)^2;
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mu_dc_eff = obj.mu_dc / (P_err + eps);
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else
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mu_dc_eff = obj.mu_dc;
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end
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if dc_buffer_enabled
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e_dc_buffer = circshift(e_dc_buffer,1);
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e_dc_buffer(1) = obj.e_dc + mu_dc_eff * err(symbol);
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if mod(symbol,obj.dc_buffer_len) == 0
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obj.e_dc = median(e_dc_buffer,"omitnan");
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end
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else
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obj.e_dc = obj.e_dc + mu_dc_eff * err(symbol);
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end
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end
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end
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% store instantaneous squared error
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obj.error(epoch, s) = e_val^2;
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if obj.save_debug
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obj.debug_struct.error(epoch,symbol) = err(symbol) * err(symbol)';
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if training
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obj.debug_struct.error_tr(epoch,symbol) = err(symbol) * err(symbol)';
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obj.debug_struct.update_tr(epoch,symbol) = update.'*update ./ rms(obj.e);
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end
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end
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% obj.error(epoch,symbol) = err(symbol) * err(symbol)';
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end
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end
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% Optional plotting in DD mode (uncomment if needed)
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if 0%~training
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constellation = unique(d);
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lvlcol = cbrewer2('Paired', numel(constellation)*2);
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lvlcol = lvlcol(2:2:end, :);
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true_err(true_err==0) = NaN;
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true_errmoverr = movsum(true_err, 4096, 'omitnan');
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true_errmoverr = true_errmoverr./rms(true_errmoverr);
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moverr = movsum(err, [100,100], 'omitnan');
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moverr = moverr./rms(moverr);
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|
||||
figure(500); clf
|
||||
hold on
|
||||
% 1st subplot: true_errmoverr
|
||||
% subplot(2,2,1); hold on
|
||||
% for k = 1:4
|
||||
% scatter(1:numSymbols, true_errmoverr(:,k), 1, lvlcol(k,:), '.');
|
||||
% end
|
||||
|
||||
% scatter(1:numSymbols, Ucorr_save./rms(Ucorr_save), 1, lvlcol(1,:), '.','DisplayName','Ucorr_save');
|
||||
% scatter(1:numSymbols, Pcorr_save./rms(Pcorr_save), 1, lvlcol(1,:), '.','DisplayName','P_corr');
|
||||
% scatter(1:numSymbols, P_err_save, 1, lvlcol(1,:), '.','DisplayName','P_err');
|
||||
% scatter(1:numSymbols, mu_dc_save, 1, lvlcol(2,:), '.','DisplayName','adapted value of $\mu_{DC}$');
|
||||
% scatter(1:numSymbols, sum(moverr,2,'omitnan'), 1, lvlcol(1,:), '.','DisplayName','Mov Error $\hat{d}$ - x over all levels');
|
||||
scatter(1:numSymbols, sum(e_dc_save,2,'omitnan'), 1, lvlcol(2,:), '.','DisplayName','Est. Error that is subtracted');
|
||||
title('Moving Sum Error');
|
||||
hold off
|
||||
legend
|
||||
|
||||
% 2nd subplot: moverr
|
||||
subplot(2,2,2); hold on
|
||||
for k = 1:4
|
||||
scatter(1:numSymbols, moverr(:,k), 1, lvlcol(k,:), '.');
|
||||
end
|
||||
title('Moving Sum Error');
|
||||
hold off
|
||||
legend
|
||||
|
||||
% 3rd subplot: err
|
||||
subplot(2,2,3); hold on
|
||||
for k = 1:4
|
||||
scatter(1:numSymbols, err(:,k), 1, lvlcol(k,:), '.');
|
||||
end
|
||||
title('Error');
|
||||
hold off
|
||||
legend
|
||||
|
||||
% 4th subplot: err + obj.constellation'
|
||||
subplot(2,2,4); hold on
|
||||
for k = 1:4
|
||||
scatter(1:numSymbols, err(:,k) + obj.constellation(k), 1, lvlcol(k,:), '.');
|
||||
end
|
||||
yline(obj.constellation, '--k');
|
||||
title('Error + Constellation');
|
||||
hold off
|
||||
legend
|
||||
|
||||
sgtitle('Error Analysis Subplots');
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
function [y,d_hat] = applyCurrentTaps(obj,x,N)
|
||||
x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
|
||||
for sample = 1 : obj.sps : N
|
||||
symbol = (sample - 1) / obj.sps + 1;
|
||||
U = x(obj.order+sample-1:-1:sample);
|
||||
y(symbol,1) = obj.e_dc + obj.e.' * U;
|
||||
[~,symbol_idx] = min(abs(y(symbol) - obj.constellation));
|
||||
d_hat(symbol,1) = obj.constellation(symbol_idx);
|
||||
end
|
||||
end
|
||||
|
||||
function N_dd = ddLength(obj,N)
|
||||
if isempty(obj.dd_len_fraction) || obj.dd_len_fraction <= 0 || obj.dd_len_fraction >= 1
|
||||
N_dd = N;
|
||||
return
|
||||
end
|
||||
|
||||
N_dd = floor(N * obj.dd_len_fraction);
|
||||
N_dd = max(obj.sps,N_dd);
|
||||
N_dd = min(N,N_dd);
|
||||
end
|
||||
|
||||
function optimizeMus(obj,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
|
||||
mu_dc_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")];
|
||||
end
|
||||
obj.mu_optimization_iter = 0;
|
||||
obj.mu_optimization = bayesopt(@(p)obj.muObjective(p,x,d),vars, ...
|
||||
"MaxObjectiveEvaluations",10, ...
|
||||
"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_mu_dc
|
||||
obj.mu_dc = obj.mu_optimization.XAtMinObjective.mu_dc;
|
||||
end
|
||||
objective_db = 10*log10(obj.mu_optimization.MinObjective);
|
||||
if obj.dd_mode && optimize_mu_dc
|
||||
fprintf("\nFFE_DCremoval_adaptive_mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, mu_dc=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB\n", ...
|
||||
obj.mu_tr,obj.mu_dd,obj.mu_dc,obj.mu_optimization.MinObjective,objective_db);
|
||||
elseif obj.dd_mode
|
||||
fprintf("\nFFE_DCremoval_adaptive_mu opt done: mu_tr=%9.3e, mu_dd=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB\n", ...
|
||||
obj.mu_tr,obj.mu_dd,obj.mu_optimization.MinObjective,objective_db);
|
||||
elseif optimize_mu_dc
|
||||
fprintf("\nFFE_DCremoval_adaptive_mu opt done: mu_tr=%9.3e, mu_dc=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB\n", ...
|
||||
obj.mu_tr,obj.mu_dc,obj.mu_optimization.MinObjective,objective_db);
|
||||
else
|
||||
fprintf("\nFFE_DCremoval_adaptive_mu opt done: mu_tr=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB\n", ...
|
||||
obj.mu_tr,obj.mu_optimization.MinObjective,objective_db);
|
||||
end
|
||||
end
|
||||
|
||||
function objective = muObjective(obj,params,x,d)
|
||||
old_debug = obj.save_debug;
|
||||
old_mu_dc = obj.mu_dc;
|
||||
obj.save_debug = 1;
|
||||
if isprop(params,"mu_dc")
|
||||
obj.mu_dc = params.mu_dc;
|
||||
end
|
||||
obj.e = zeros(obj.order,1);
|
||||
obj.e_dc = 0;
|
||||
obj.P = (1/0.05) * eye(obj.order);
|
||||
obj.debug_struct = struct();
|
||||
obj.equalize(x,d,params.mu_tr,obj.epochs_tr,obj.len_tr,1,0);
|
||||
if obj.dd_mode
|
||||
obj.equalize(x,d,params.mu_dd,obj.epochs_dd,obj.ddLength(numel(x)),0,0);
|
||||
objective = mean(obj.debug_struct.error(end,:),"omitnan");
|
||||
else
|
||||
objective = mean(obj.debug_struct.error_tr(end,:),"omitnan");
|
||||
end
|
||||
if ~isfinite(objective)
|
||||
objective = inf;
|
||||
end
|
||||
objective_db = 10*log10(objective);
|
||||
obj.mu_optimization_iter = obj.mu_optimization_iter + 1;
|
||||
optimize_mu_dc = isprop(params,"mu_dc");
|
||||
if obj.dd_mode && optimize_mu_dc
|
||||
fprintf("\rFFE_DCremoval_adaptive_mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, mu_dc=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB", ...
|
||||
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,params.mu_dc,objective,objective_db);
|
||||
elseif obj.dd_mode
|
||||
fprintf("\rFFE_DCremoval_adaptive_mu opt %02d: mu_tr=%9.3e, mu_dd=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB", ...
|
||||
obj.mu_optimization_iter,params.mu_tr,params.mu_dd,objective,objective_db);
|
||||
elseif optimize_mu_dc
|
||||
fprintf("\rFFE_DCremoval_adaptive_mu opt %02d: mu_tr=%9.3e, mu_dc=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB", ...
|
||||
obj.mu_optimization_iter,params.mu_tr,params.mu_dc,objective,objective_db);
|
||||
else
|
||||
fprintf("\rFFE_DCremoval_adaptive_mu opt %02d: mu_tr=%9.3e, MSE=%9.3e, MSE_dB=%7.2f dB", ...
|
||||
obj.mu_optimization_iter,params.mu_tr,objective,objective_db);
|
||||
end
|
||||
obj.save_debug = old_debug;
|
||||
obj.mu_dc = old_mu_dc;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
Reference in New Issue
Block a user