Let x be a Nx1 vector that contains pseudorandom samples drawn from standard normal distribution. This represents N samples taken from a standard normal distribution at time ‘t’.
The correlation of the sequence x is given by E[xx H]
This means, we need to compute the matrix xx H M (M is a very large number) times, sum up all the matrices, and divide the resultant matrix by M. We can see that as M tends to infinity, we get a diagonal matrix as output. If the variance of the noise distribution is 1, then we get an identity matrix.
Z=10;
N=1000000
Y=zeros(Z,Z);
for i=0:N-1
x=randn(Z,1);
X=x*x';
Y=Y+X;
end
Y./N
tweaks I used to prepare a template for one of our lab reports recently. Most of the help was received from 
) figuring out how to get it done.
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