Mean Square Error with ML Estimate

Hello,

I've been struggling with an code in my Detection and Estimation theory course.

I have a signal of know frequency w0 given by:

y(t)=Asin(w0*t) + Sigma(t) where Sigma(t) is a noisy sequence of i.i.d random variables with mean zero and variance a^2

We need to estimate the parameter A when the noise is gaussian.

The question is to compute the mean square error (MSE) defined as MSE(AEst)=E(AEst-A)^2 by averaging simulation results over 5,000 independent runs. (Assuming A=1 and w0=2*pi*0.2)

Can anyone help me in approaching this problem ?