What is the variance of the product of a complex Gaussian matrix and vector?

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The discussion revolves around calculating the variance of the product of a complex Gaussian matrix A and a complex Gaussian vector b. It is established that A is an n x m random matrix with complex Gaussian entries of zero mean and unit variance, while b is an n x 1 random vector with complex Gaussian entries of zero mean and variance s. To find the variance of the product Ab, one must first determine the variance of the product of two independent Gaussian variables and then consider the distribution of the sum of several such products. The central limit theorem can be applied for large matrices to approximate the sum's distribution. Understanding these statistical properties is crucial for accurately determining the variance of the product.
nikozm
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Hi,

Assuming that A is a n x m random matrix and each of its entries are complex Gaussian with zero mean and unit-variance. Also, assume that b is a n x1 random vector and its entries are complex Gaussian with zero mean and variance=s. Then, what would be the variance of their product Ab?

Any help would be useful.

Thanks
 
I think that you have first to know the variance of a product of two gaussians. More precisely, if X and Y are two independant random variables with distribution N(0,s) and N(0,s'), then what is the distribution of XY ? There are formulae in the litterature (Google it). Once you have obtained the distribution Z of XY, you have to know the distribution of the sum of several variable Z' of the same type (theoretically, this is the convolution product of the variables). I am almost certain that Z, and the sum of the variables Z', will have zero mean. The variance should be given in the litterature, if the sum of the Z' is a known distribution. If your matrix is large, then you can use the central limit theorem to approximate the sum of the Z'.
 
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