>Just looking at it quickly, your channel's frequency response is fairly
>flat. Try using a channel with a null at some frequency and see if the
>results are still very similar.
>
%%%%%
HI...
I have not looked at ur code as it will take time.
If you are studying equaliser for first time, try to implement both LE and
DFE for channel B of proakis i.e. channel=[0.407 0.815 0.407];
NOw try to implement two cases:
1. When u know channel at RX
2. When u dont knw
First try to do for Uncoded case and get the results given in Proakis's
book.
And then apply coding. For DFE u should get some strange results (i.e.
other than what u generally expect)
Hope this helps.
Feel free to ask any doubt regarding this topic anytime.
Chintan
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Just looking at it quickly, your channel's frequency response is fairly
flat. Try using a channel with a null at some frequency and see if the
results are still very similar.
Reply Start a New Thread
Hi guys
Now I`m studying the Equalizer
I`m coding the linear and dfe EQ
but there is no different
I don`t know what is wrong
can you correct this code or give me some idea
%%%%DFE(Training+Direct) vs. Linear%%%%
clear all;
N=1000; % length of the information sequence
K=5;
actual_isi=[0.2, -0.15, 1.0, 0.21, 0.03];
sigma=0.01;
delta=0.08; %0.08
delta2=0.00008; %0.00008
Num_of_realizations=2000;
mse_av=zeros(1,N-2*K);
mse_avl=mse_av;
for j=1:Num_of_realizations, % Compute the average over a number of
realizations.
% the information sequence
p=0.5;
info=binornd(1,p,1,1000);
infol=info;
% 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;
yl=y;
param = 0;
c0(5) = param; %Precursor Filter Weight
Initialization
c1(5) = param;
c2(5) = param;
c3(5) = param;
c4(5) = param;
d1(5) = param; %Postcursor Filter Weight
Initialization
d2(5) = param;
d3(5) = param;
%Linear Weight
cl0(5) = param;
cl1(5) = param;
cl2(5) = param;
cl3(5) = param;
cl4(5) = param;
end;
% Now the equalization part follows.
%DFE_Training
for k=5:N-2*K, %5:N-2*K
e(k) = info(k) - [c4(k) c3(k) c2(k) c1(k) c0(k) -d1(k) -d2(k)
-d3(k)] * [y(k+4); y(k+3); y(k+2); y(k+1); y(k); info(k-1); info(k-2);
info(k-3)];
xes(k) = [c4(k) c3(k) c2(k) c1(k) c0(k) -d1(k) -d2(k) -d3(k)] *
[y(k+4); y(k+3); y(k+2); y(k+1); y(k); info(k-1); info(k-2); info(k-3)];
if xes(k) > 0.5
xes2(k) = 1;
elseif xes(k) < 0.5
xes2(k) = 0;
end
c4(k+1) = c4(k) + delta * e(k) * y(k+4);
c3(k+1) = c3(k) + delta * e(k) * y(k+3);
c2(k+1) = c2(k) + delta * e(k) * y(k+2);
c1(k+1) = c1(k) + delta * e(k) * y(k+1);
c0(k+1) = c0(k) + delta * e(k) * y(k);
d1(k+1) = d1(k) + delta2 * e(k) * info(k-1);
d2(k+1) = d2(k) + delta2 * e(k) * info(k-2);
d3(k+1) = d3(k) + delta2 * e(k) * info(k-3);
%mse(k)=e(k)^2;
if k>=500
mu1 = 0.000001; %0.000001
mu2 = 0.00002; %0.00002
e(k) = xes2(k) - [c4(k) c3(k) c2(k) c1(k) c0(k) -d1(k) -d2(k) -d3(k)]
* [y(k+4); y(k+3); y(k+2); y(k+1); y(k); xes2(k-1); xes2(k-2); xes2(k-3)];
xes(k+1) = [c4(k) c3(k) c2(k) c1(k) c0(k) -d1(k) -d2(k) -d3(k)] *
[y(k+4); y(k+3); y(k+2); y(k+1); y(k); xes2(k-1); xes2(k-2); xes2(k-3)];
if xes(k+1) > 0.5
xes2(k+1) = 1;
elseif xes(k+1) < 0.5
xes2(k+1) = 0;
end;
c4(k+1) = c4(k) + mu1 * e(k) * y(k+4);
c3(k+1) = c3(k) + mu1 * e(k) * y(k+3);
c2(k+1) = c2(k) + mu1 * e(k) * y(k+2);
c1(k+1) = c1(k) + mu1 * e(k) * y(k+1);
c0(k+1) = c0(k) + mu1 * e(k) * y(k);
d1(k+1) = d1(k) + mu2 * e(k) * xes(k-1);
d2(k+1) = d2(k) + mu2 * e(k) * xes(k-2);
d3(k+1) = d3(k) + mu2 * e(k) * xes(k-3);
%mse(k)=e(k)^2;
end;
mse(k)=e(k)^2;
end;
for i = 1:N-2*K
error2(i) = (info(i) - xes(i))^2;
end;
%%%Direct
%%%Linear
for k=5:N-2*K
el(k) = infol(k) - [cl4(k) cl3(k) cl2(k) cl1(k) cl0(k)] * [yl(k+4);
yl(k+3); yl(k+2); yl(k+1); yl(k);];
xesl(k) = [cl4(k) cl3(k) cl2(k) cl1(k) cl0(k)] * [yl(k+4); yl(k+3);
yl(k+2); yl(k+1); yl(k);];
if xesl(k) > 0.5
xes2l(k) = 1;
elseif xesl(k) < 0.5
xes2l(k) = 0;
end
cl4(k+1) = cl4(k) + delta * el(k) * yl(k+4);
cl3(k+1) = cl3(k) + delta * el(k) * yl(k+3);
cl2(k+1) = cl2(k) + delta * el(k) * yl(k+2);
cl1(k+1) = cl1(k) + delta * el(k) * yl(k+1);
cl0(k+1) = cl0(k) + delta * el(k) * yl(k);
msel(k)=el(k)^2;
end;
for i = 1:N-2*K
errorl(i) = (infol(i) - xesl(i))^2;
end;
mse_av=mse_av+mse;
mse_avl=mse_avl+msel;
mse_av=mse_av/Num_of_realizations;
mse_avl=mse_avl/Num_of_realizations;% mean-squared error versus iterations
% Plotting commands follow.
figure
semilogy((abs(error2)),'b');title('DFE Error curve') ;
grid on; hold on;
figure
semilogy((abs(errorl)),'r');title('Linear Error curve') ;
grid on; hold on;
figure
semilogy((abs(mse_av)),'b');
grid on; hold on;
semilogy((abs(mse_avl)),'r') ;
title('Error curve') ;
xlabel('Samples')
ylabel('Error value')
legend('DFE','Linear'); %'error2','errorl2','DFE','Linear'
grid on; hold on;
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