159 lines
4.7 KiB
Matlab
159 lines
4.7 KiB
Matlab
classdef FFE_DCremoval < 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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properties
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sps % usually 2
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order
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e
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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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mu_dd
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epochs_dd
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mu_dc
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dc_buffer_len
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constellation
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decide
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end
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methods
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function obj = FFE_DCremoval(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.mu_dd = 1e-5;
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options.epochs_dd = 5;
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options.mu_dc = 0.05;
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options.dc_buffer_len = 1;
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options.decide = false;
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end
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assert(options.dc_buffer_len>0);
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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.e = zeros(obj.order,1);
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obj.error = 0;
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end
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function [X] = 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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% 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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% Decision Directed Mode
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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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% Output Signal
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if obj.decide
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X.signal = decision;
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else
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X.signal = signal;
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end
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X.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
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lbdesc = [num2str(obj.order),' tap FFE'];
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X = X.logbookentry(lbdesc); % append to logbook
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end
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function [y,d_hat] = equalize(obj,x,d,mio,epochs,N,training)
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arguments
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obj
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x
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d
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mio
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epochs
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N
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training
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end
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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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err = 0;
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e_dc_buffer = NaN(obj.dc_buffer_len,1);
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e_dc_est = 0;
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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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U = x(obj.order+sample-1:-1:sample);
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y(symbol,1) = e_dc_est + obj.e.' * U; % Calculating output of LMS __ * |
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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) - obj.constellation)); % decision for closest constellation point
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d_hat(symbol,1) = obj.constellation(symbol_idx);
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end
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err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
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if mio ~= 0
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obj.e = obj.e - (mio * err(symbol) * U) ; % Weight update rule of LMS
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else
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normalizationfactor = (U.' * U);
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obj.e = obj.e - err(symbol) * U / normalizationfactor; % Weight update rule of NLMS
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end
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%Update the dc estimation every n-th symbol. This is a
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%trivial implementation of parallel EQ´s where the
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%errors are not apparent in every step. See Silas OFC
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%2023 "MPI mitigation adaptive DC removal
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if mod(symbol,length(e_dc_buffer)) == 0
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e_dc_buffer(1) = e_dc_est - obj.mu_dc * err(symbol);
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e_dc_buffer = circshift(e_dc_buffer,1);
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e_dc_est = mean(e_dc_buffer,"omitnan");
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else
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e_dc_buffer(1) = e_dc_est - obj.mu_dc * err(symbol);
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e_dc_buffer = circshift(e_dc_buffer,1);
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end
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obj.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
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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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