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

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SUMMARY

The variance of the product of a complex Gaussian matrix A and a complex Gaussian vector b can be determined by first understanding the variance of the product of two independent Gaussian random variables. Specifically, if X and Y are independent random variables with distributions N(0,s) and N(0,s'), the distribution of their product XY can be derived from existing literature. The resulting distribution Z of XY, along with the sum of several such variables Z', will have a zero mean. For large matrices, the central limit theorem can be applied to approximate the variance of the sum of these variables.

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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 independent 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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