classdef FFE < handle
    % Implementation of plain and simple FFE.
    % 1) Training mode (stable performance when you use NLMS)
    % 2) Decision directed mode

    % 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);

    properties
        sps % usually 2
        order
        e
        error

        len_tr
        mu_tr
        epochs_tr

        mu_dd
        epochs_dd

        constellation

        decide
    end

    methods
        function obj = FFE(options)
            arguments(Input)

                options.sps = 2;
                options.order = 15;

                options.len_tr = 4096;
                options.mu_tr = 0;
                options.epochs_tr = 5;

                options.mu_dd = 1e-5;
                options.epochs_dd = 5;

                options.decide = false;

            end

            fn = fieldnames(options);
            for n = 1:numel(fn)
                obj.(fn{n}) = options.(fn{n});
            end

            obj.e = zeros(obj.order,1);
            obj.error = 0;

        end

        function [X,Noi] = process(obj, X, D)

            % actual processing of the signal (steps 1. - 3.)
            % 1 normalize RMS
            X = X.normalize("mode","rms");

            obj.constellation = unique(D.signal);

            % Training Mode
            training = 1;
            showviz = 0;
            obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);

            % Decision Directed Mode
            n = X.length;
            training = 0;
            showviz = 0;
            [signal,decision]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);

            % Output Signal
            if obj.decide 
                X.signal = decision;
            else
                X.signal = signal;
            end

            X.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
            lbdesc = [num2str(obj.order),' tap FFE'];
            X  = X.logbookentry(lbdesc); % append to logbook

            Noi = X;
            Noi = X - D;
            

        end

        function [y,d_hat] = equalize(obj,x,d,mio,epochs,N,training,showviz)

            arguments
                obj 
                x 
                d 
                mio 
                epochs 
                N 
                training 
                showviz
            end

            x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
            
            for epoch = 1 : epochs
                symbol = 0;
                for sample = 1 : obj.sps : N

                    symbol = symbol+1;

                    U = x(obj.order+sample-1:-1:sample);

                    y(symbol,1) = obj.e.' * U;    % Calculating output of LMS  __ * |

                    if training
                        d_hat(symbol,1) = d(symbol);  
                    else
                        [~,symbol_idx]  = min(abs(y(symbol) - obj.constellation)); % decision for closest constellation point
                        d_hat(symbol,1) = obj.constellation(symbol_idx);
                    end

                    err(symbol) =  y(symbol) - d_hat(symbol);   % Instantaneous error

                    true_err(symbol) =  y(symbol) - d(symbol);   % Instantaneous error

                    if mio ~= 0
                        obj.e = obj.e - (mio * err(symbol) * U) ;    % Weight update rule of LMS
                    else
                        normalizationfactor =  (U.' * U);
                        obj.e = obj.e - err(symbol) * U / normalizationfactor;    % Weight update rule of NLMS
                    end
                    

                    obj.error(epoch,symbol) = err(symbol) * err(symbol)';  % Instantaneous square error

                end
            end

        end

    end
end