From 3f089dca9fc531f8651276a97331192f11976ad0 Mon Sep 17 00:00:00 2001
From: Silas Oettinghaus <sioe@tf.uni-kiel.de>
Date: Fri, 16 Aug 2024 10:07:29 +0200
Subject: [PATCH] DC removal with buffer len

---
 Classes/04_DSP/FFE_DCremoval.m | 72 +++++++++++-----------------------
 1 file changed, 22 insertions(+), 50 deletions(-)

diff --git a/Classes/04_DSP/FFE_DCremoval.m b/Classes/04_DSP/FFE_DCremoval.m
index 7478bff..2fd9055 100644
--- a/Classes/04_DSP/FFE_DCremoval.m
+++ b/Classes/04_DSP/FFE_DCremoval.m
@@ -65,14 +65,12 @@ classdef FFE_DCremoval < handle
 
             % Training Mode
             training = 1;
-            showviz = 0;
-            obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
+            obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training);
 
             % 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);
+            [signal,decision]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,N,training);
 
             % Output Signal
             if obj.decide 
@@ -87,7 +85,7 @@ classdef FFE_DCremoval < handle
 
         end
 
-        function [y,d_hat] = equalize(obj,x,d,mio,epochs,N,training,showviz)
+        function [y,d_hat] = equalize(obj,x,d,mio,epochs,N,training)
 
             arguments
                 obj 
@@ -97,36 +95,14 @@ classdef FFE_DCremoval < handle
                 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
-
             err = 0;
-            e_dc = 0;
+            % e_dc_buffer = NaN(100,1);
+            e_dc_buffer = NaN(10,1);
+            e_dc_est = 0;
 
             for epoch = 1 : epochs
                 symbol = 0;
@@ -135,18 +111,18 @@ classdef FFE_DCremoval < handle
                     symbol = symbol+1;
 
                     U = x(obj.order+sample-1:-1:sample);
-
-                    y(symbol,1) = e_dc + obj.e.' * U;    % Calculating output of LMS  __ * |
-
+                    
+                    y(symbol,1) = e_dc_est + 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
-
+                    
                     if mio ~= 0
                         obj.e = obj.e - (mio * err(symbol) * U) ;    % Weight update rule of LMS
                     else
@@ -154,26 +130,22 @@ classdef FFE_DCremoval < handle
                         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;
+                    if mod(symbol,length(e_dc_buffer)) == 0
+                        e_dc_buffer(1) = e_dc_est - obj.mu_dc * err(symbol);
+                        e_dc_buffer = circshift(e_dc_buffer,1);
+                        e_dc_est = mean(e_dc_buffer,"omitnan");
+                    else
+                        e_dc_buffer(1) = e_dc_est - obj.mu_dc * err(symbol);
+                        e_dc_buffer = circshift(e_dc_buffer,1);
                     end
-
-                    e_dc = e_dc - obj.mu_dc * err(symbol);
-
+    
                     obj.error(epoch,symbol) = err(symbol) * err(symbol)';  % Instantaneous square error
-
+                    
                 end
             end
-
+            
         end
-
+        
     end
 end