Channel covariance estimation in multiuser massive MIMO systems with an approach based on infinite dimensional Hilbert spaces

TL;DR

This paper introduces a simple, computationally efficient algorithm for estimating channel covariance matrices in multiuser massive MIMO systems, leveraging infinite dimensional Hilbert space models to handle realistic signal conditions.

Contribution

The novel algorithm uses only array response and rough angular support knowledge, avoiding idealized assumptions and enabling effective covariance estimation in practical scenarios.

Findings

Estimates are close to standard techniques in interference-free noiseless systems.

The algorithm requires only a single matrix-vector multiplication.

It performs well under realistic signal models.

Abstract

We propose a novel algorithm to estimate the channel covariance matrix of a desired user in multiuser massive MIMO systems. The algorithm uses only knowledge of the array response and rough knowledge of the angular support of the incoming signals, which are assumed to be separated in a well-defined sense. To derive the algorithm, we study interference patterns with realistic models that treat signals as continuous functions in infinite dimensional Hilbert spaces. By doing so, we can avoid common and unnatural simplifications such as the presence of discrete signals, ideal isotropic antennas, and infinitely large antenna arrays. An additional advantage of the proposed algorithm is its computational simplicity: it only requires a single matrix-vector multiplication. In some scenarios, simulations show that the estimates obtained with the proposed algorithm are close to those obtained with standard estimation techniques operating in interference-free and noiseless systems.