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From: chandrakanthv on 17 Mar 2010 09:22
Hello everyone,

I am chandrakanth as can be seen by the user name :). I have a query
regarding the implementation of Noise cancellation using NLMS. I have
followed the theory from "Statistical DSP by Monson Hayes" I am not able
to get the noise cancellation waveforms given in the text book. I am not
able to debug the code for the results. Can anyone help me with it. I am
pasting the code here. It is written in primitive way with no logical loops
etc but still the results do not match. So if possible please go through it
and let me know the problem.

Thanks and Regards,
Chandrkanth.

N = 1000;
g = randn(1,N);


v1(1) = 0;
v2(1) = 0;

for j = 2:1:N

v1(j) = 0.8*v1(j-1) + g(j);
v2(j) = -0.6*v2(j-1) + g(j);
end

sig1 = d + v1(1:N);

%sig1 = d + v1(1:1000);
x = v2;


%-------------------------------- NLMS Algorithm
--------------------------
for n = 13:1:N
e_sq1(13) = 0;
we1(13)= 0;
we2(13) = 0;
we3(13)= 0;
we4(13) = 0;
we5(13)= 0;
we6(13) = 0;
we7(13)= 0;
we8(13) = 0;
we9(13)= 0;
we10(13) = 0;
we11(13)= 0;
we12(13) = 0;

x_est1(n) = we1(n) * x(n-1) + we2(n) * x(n-2)+ we3(n) * x(n-3) + we4(n) *
x(n-4)+ we5(n) * x(n-5) + we6(n) * x(n-6) + we7(n) * x(n-7) + we8(n) *
x(n-8)+ we9(n) * x(n-9) + we10(n) * x(n-10)+ we11(n) * x(n-11) + we12(n) *
x(n-12);
e1(n) = sig1(n) - x_est1(n);
e_sq(n+1) = e(n) * e(n) + e_sq(n) ;
% mean_er(n) = e_sq(n+1)/n;

err(n) = d(n) - e1(n);
err_sq(n) = (err(n)*err(n));

x_norm1 = x(n-1)*x(n-1);
x_conj1 = conj(x(n-1));

x_norm2 = x(n-2)*x(n-2);
x_conj2 = conj(x(n-2));

x_norm3 = x(n-3)*x(n-3);
x_conj3 = conj(x(n-3));

x_norm4 = x(n-4)*x(n-4);
x_conj4 = conj(x(n-4));

x_norm5 = x(n-5)*x(n-5);
x_conj5 = conj(x(n-5));

x_norm6 = x(n-6)*x(n-6);
x_conj6 = conj(x(n-6));

x_norm7 = x(n-7)*x(n-7);
x_norm8 = x(n-8)*x(n-8);
x_norm9 = x(n-9)*x(n-9);
x_norm10 = x(n-10)*x(n-10);
x_norm11 = x(n-11)*x(n-11);
x_norm12 = x(n-12)*x(n-12);


den = x_norm1 + x_norm2 + x_norm3 + x_norm4 + x_norm5 + x_norm6 + x_norm7 +
x_norm8 + x_norm9 + x_norm10 + x_norm11 + x_norm12;

we1(n+1) = we1(n) + 0.25 * (e1(n)*(x(n-1)/(den)));
we2(n+1) = we2(n) + 0.25 * (e1(n)*(x(n-2)/(den)));
we3(n+1) = we3(n) + 0.25 * (e1(n)*(x(n-3)/(den)));
we4(n+1) = we4(n) + 0.25 * (e1(n)*(x(n-4)/(den)));
we5(n+1) = we5(n) + 0.25 * (e1(n)*(x(n-5)/(den)));
we6(n+1) = we6(n) + 0.25 * (e1(n)*(x(n-6)/(den)));
we7(n+1) = we7(n) + 0.25 * (e1(n)*(x(n-7)/(den)));
we8(n+1) = we8(n) + 0.25 * (e1(n)*(x(n-8)/(den)));
we9(n+1) = we9(n) + 0.25 * (e1(n)*(x(n-9)/(den)));
we10(n+1) = we10(n) + 0.25 * (e1(n)*(x(n-10)/(den)));
we11(n+1) = we11(n) + 0.25 * (e1(n)*(x(n-11)/(den)));
we12(n+1) = we12(n) + 0.25 * (e1(n)*(x(n-12)/(den)));

end
%-------------------------------- NLMS Algorithm
--------------------------
figure(2)
subplot(4,1,1)
plot(d);
axis([0 N -3 3])
subplot(4,1,2)
plot(sig1);
axis([0 N -6 6])
subplot(4,1,3)
plot(x);
axis([0 N -6 6])
subplot(4,1,4)
plot(e1);
axis([0 N -3 3])


figure(3)
plot(err_sq);




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