fig_adaptive_linear_equalizer.m

另一类信道自适应线性均衡源码

% MATLAB script for Illustrative Problem 12, Chapter 6.
echo on
N=500;			        	% length of the information sequence
K=5;
actual_isi=[0.05 -0.063 0.088 -0.126 -0.25 0.9047 0.25 0 0.126 0.038 0.088];
sigma=0.01;
delta=0.115;
Num_of_realizations=1000;
mse_av=zeros(1,N-2*K);
for j=1:Num_of_realizations,   		% compute the average over a number of realizations
  % the information sequence
  for i=1:N,
    if (rand<0.5),
      info(i)=-1;
    else
      info(i)=1;
    end;
    echo off ;
  end;
  if (j==1) ; echo on ; end 
  % the channel output
  y=filter(actual_isi,1,info);
  for i=1:2:N, [noise(i) noise(i+1)]=gngauss(sigma); end;
  y=y+noise;
  % now the equalization part follows
  estimated_c=[0 0 0 0 0 1 0 0 0 0 0];	% initial estimate of ISI
  for k=1:N-2*K,
    y_k=y(k:k+2*K);
    z_k=estimated_c*y_k.';
    e_k=info(k)-z_k;
    estimated_c=estimated_c+delta*e_k*y_k;
    mse(k)=e_k^2;
    echo off ;
  end;
  if (j==1) ; echo on ; end 
  mse_av=mse_av+mse;
  echo off ;
end;
echo on ;
mse_av=mse_av/Num_of_realizations;	% mean squared error versus iterations 
% plotting commands follow
FONTSIZE=0.315;         % 单位为厘米;  
LINEWIDTH_AXES=1.2;%140/42;  % corresponding to 0.140mm 
LINEWIDTH_WAVE=0.5;%180/42;  % corresponding to 0.180mm
figNumber=figure('Name','adaptive_linear_equalizer', 'Visible','on');
set(gcf,'Color','white');
plot(20*log10(abs(mse_av)),'red','LineWidth',LINEWIDTH_WAVE);  
xlabel('Iteration Number','FontUnits','centimeters','FontName','Times New Roman','FontSize',FONTSIZE);  
ylabel('Output Estimation Error in dB','FontUnits','centimeters','FontName','Times New Roman','FontSize',FONTSIZE);
title('\bf Learning Curve','Color','blue','FontUnits','centimeters','FontName','Times New Roman','FontSize',FONTSIZE*1.2);
set(gca,'XColor','blue','YColor','blue','FontUnits','centimeters','FontName','Times New Roman','FontSize',FONTSIZE,'LineWidth',LINEWIDTH_AXES);
     

set(figNumber,'PaperPosition',[0.634518 6.34518 20.3046*0.8 15.2284*0.8]);     %Shrink the figure size  


% plot(20*log10(abs(mse_av))) ;
% title('Learning Curve') ;
% xlabel('Iteration Number') ;
% ylabel('Output Estimation Error in dB') ;