auswertungsfiles und algos for ECOC 2025 rush...
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Silas Oettinghaus
240
Classes/04_DSP/Equalizer/FFE_DCremoval_level.m
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240
Classes/04_DSP/Equalizer/FFE_DCremoval_level.m
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classdef FFE_DCremoval_level < 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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KF_meas_noise = 0;
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KF_process_noise = 0;
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KF_state_cov = 0;
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end
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methods
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function obj = FFE_DCremoval_level(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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obj.dc_buffer_len = floor(obj.dc_buffer_len);
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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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% 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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Noi = X - D;
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end
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function [y, d_hat, logs] = equalize(obj, x, d, mu_lms, epochs, N, training)
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% Equalize with Kalman-based DC removal; training epochs estimate KF noise parameters
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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 or DD epochs
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N % number of samples to process
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training % true => training mode (tap-training + noise estimation)
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end
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% Zero-pad for filter memory
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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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numSym = ceil(N/obj.sps);
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% Pre-allocate outputs
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y = zeros(numSym,1);
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d_hat = zeros(numSym,1);
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% --- Training: estimate noise stats and train taps ---
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if training
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% Pre-allocate error accumulator
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totalTrain = epochs * numSym;
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trainErrs = zeros(totalTrain,1);
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te_idx = 0;
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for ep = 1:epochs
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s = 0;
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for n = 1:obj.sps:N
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s = s + 1;
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U = x(obj.order + n - 1 : -1 : n);
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% Equalizer output (no DC correction yet)
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y(s) = obj.e.' * U;
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% Decision based on known symbol
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[~, idx] = min(abs(d(s) - obj.constellation));
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d_hat(s) = obj.constellation(idx);
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% Instantaneous error
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e_n = y(s) - d_hat(s);
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% Collect error for noise estimation
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te_idx = te_idx + 1;
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trainErrs(te_idx) = e_n;
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% Tap-weight update (LMS or NLMS)
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if mu_lms ~= 0
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obj.e = obj.e - mu_lms * e_n * U;
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else
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normU = (U.'*U) + eps;
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obj.e = obj.e - e_n * U / normU;
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end
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end
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end
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% Estimate measurement noise R and process noise Q
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R_est = var(trainErrs(1:te_idx));
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Q_est = 1e-3 * R_est; % Q/R ratio = 1e-3 (tune as needed)
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% Store into object for DD pass
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obj.KF_meas_noise = R_est;
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obj.KF_process_noise = Q_est;
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obj.KF_state_cov = 5*R_est; % or 5*R_est for a more “eager” start
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% No Kalman in training; return
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logs = struct();
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return;
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end
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% --- Decision-Directed with Kalman DC tracking ---
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% Initialize Kalman state
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x_est = 0;
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P = obj.KF_state_cov; % initial P (tune in obj; e.g. 1)
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% Logging containers
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logs.y_raw = zeros(numSym,1);
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logs.y_corr = zeros(numSym,1);
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logs.err = zeros(numSym,1);
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logs.K_gain = zeros(numSym,1);
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logs.x_est = zeros(numSym,1);
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logs.P = zeros(numSym,1);
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logs.normU = zeros(numSym,1);
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logs.tap_norm = zeros(numSym,1);
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for ep = 1:epochs
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s = 0;
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for n = 1:obj.sps:N
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s = s + 1;
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U = x(obj.order + n - 1 : -1 : n);
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% 1) Kalman prediction
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P = P + obj.KF_process_noise;
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x_prior = x_est;
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% 2) raw equalizer output
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y_raw = obj.e.' * U;
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logs.y_raw(s) = y_raw;
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% 3) DC-corrected output
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y_corr = y_raw + x_prior;
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y(s) = y_corr;
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logs.y_corr(s) = y_corr;
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% 4) decision-directed symbol
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[~, idx] = min(abs(y_corr - obj.constellation));
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d_hat(s) = obj.constellation(idx);
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% 5) error
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e_n = y_corr - d_hat(s);
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logs.err(s) = e_n;
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% 6) tap-weight update (LMS/NLMS)
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if mu_lms ~= 0
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obj.e = obj.e - mu_lms * e_n * U;
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else
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normU = U.' * U + eps;
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logs.normU(s) = normU;
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obj.e = obj.e - e_n * U / normU;
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end
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logs.tap_norm(s) = norm(obj.e);
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% 7) Kalman update
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K_gain = P / (P + obj.KF_meas_noise);
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x_est = x_prior + K_gain * (e_n - x_prior);
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P = (1 - K_gain) * P;
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% 8) log Kalman state
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logs.K_gain(s) = K_gain;
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logs.x_est(s) = x_est;
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logs.P(s) = P;
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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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