New MPI mitigation schemes // Duobinary // Start of FTN schemes

Classes/04_DSP/Equalizer/FFE_adaptive_decision.m

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+classdef FFE_adaptive_decision < 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
+
+        buffer_length
+
+        constellation
+
+        decide
+    end
+
+    methods
+        function obj = FFE_adaptive_decision(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.buffer_length = 100;
+
+                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)];
+            
+            for epoch = 1 : epochs
+                symbol = 0;
+                % y_buffer = zeros(numel(obj.constellation),500);
+                y_buffer = repmat(obj.constellation,1,obj.buffer_length);
+                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
+                        y_buffer(y_buffer==0) = NaN;
+                        adap_constellation = mean(y_buffer,2,"omitnan");
+                        [~,symbol_idx]  = min(abs(y(symbol) - adap_constellation)); % decision for closest constellation point
+                        d_hat(symbol,1) = obj.constellation(symbol_idx);
+                    end
+
+                    y_buffer(symbol_idx,1) = y(symbol);
+                    y_buffer(symbol_idx,:) = circshift(y_buffer(symbol_idx,:),1);
+
+                    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
+                    
+
+                    obj.error(epoch,symbol) = err(symbol) * err(symbol)';  % Instantaneous square error
+
+                end
+            end
+
+        end
+
+    end
+end
+