Analytical Derivation of the Inverse Moments of One-Sided Correlated Gram Matrices with Applications

Abstract

This paper addresses the development of analytical tools for the computation of the inverse moments of random Gram matrices with one side correlation. Such a question is mainly driven by applications in signal processing and wireless communications wherein such matrices naturally arise. In particular, we derive closed-form expressions for the inverse moments and show that the obtained results can help approximate several performance metrics such as the average estimation error corresponding to the best linear unbiased estimator (BLUE) and the linear minimum mean square error (LMMSE) estimator or also other loss functions used to measure the accuracy of covariance matrix estimates.