imdd_silas/Classes/04_DSP/FFE_FFDCAVG.m

classdef FFE_FFDCAVG < 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

        mu_buff

        constellation

        decide
    end

    methods
        function obj = FFE_FFDCAVG(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.mu_buff = 0;

                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] = 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
            

        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)];

            if showviz
                f = figure(111);
                subplot(2,2,1:2);
                hold on
                a = scatter(1:numel(x),x,1,'.');
                a2 = scatter(1,1,1,'.');
                a3 = scatter(1,1,2,'.');
                a4 = xline(1);
                ylim([-3 3])
                xlim([0 length(x)]);
                % subplot(2,2,3)
                % dplot = x(1:1+500);
                % b = scatter(1:numel(dplot),dplot,5,'x');
                % xline(1)
                % xline(obj.order)
                % ylim([-3 3])
                % xlim([0 500]);
                subplot(2,2,3:4)
                c = stem(obj.e);
                ylim([-1 1])
                drawnow
            end

            
            
            for epoch = 1 : epochs
                symbol = 0;
                err_buffer = zeros(numel(obj.constellation),50);
                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
                        [~,symbol_idx]  = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point
                        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

                    err_buffer(symbol_idx,1) = err(symbol);
                    err_buffer(symbol_idx,:) = circshift(err_buffer(symbol_idx,:),1);
                        
                    y(symbol) = y(symbol) - obj.mu_buff*mean(err_buffer(symbol_idx,:));

                    if training
                        [~,symbol_idx]  = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point
                        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
        
                    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
                    
                    if mod(sample,100) == 1 && showviz
                        a2.XData = 1:2*numel(y);
                        a2.YData = repelem(y, 2);
                        a3.XData = 1:2*numel(d_hat);
                        a3.YData = repelem(d_hat, 2);
                        a4.Value = sample;
                        % b.YData = x(symbol:symbol+500);
                        c.YData = obj.e;
                        drawnow;
                    end

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

                end
            end

        end

    end
end