Structured Channel Covariance Estimation from Limited Samples for Large Antenna Arrays

TL;DR

This paper introduces a novel parametric approach for estimating channel covariance matrices in massive MIMO systems with limited samples, improving accuracy over existing methods.

Contribution

It proposes a new method combining MUSIC and dictionary-based modeling for better covariance estimation with limited data in large antenna arrays.

Findings

Outperforms state-of-the-art covariance estimators.

Effective with small sample sizes.

Improves system performance in massive MIMO.

Abstract

In massive multiple-input multiple-output (MIMO) systems, the knowledge of the users' channel covariance matrix is crucial for minimum mean square error (MMSE) channel estimation in the uplink as well as it plays an important role in several multiuser beamforming schemes in the downlink. Due to the large number of base station antennas in massive MIMO, accurate covariance estimation is challenging especially in the case where the number of samples is limited and thus comparable to the channel vector dimension. As a result, the standard sample covariance estimator may yield a too large estimation error which in turn may yield significant system performance degradation with respect to the ideal channel covariance knowledge case. To address such problem, we propose a method based on a parametric representation of the channel angular scattering function. The proposed parametric representation includes a discrete specular component which is addressed using the well-known MUltiple SIgnal Classification (MUSIC) method, and a diffuse scattering component, which is modeled as the superposition of suitable dictionary functions. To obtain the representation parameters we propose two methods, where the first solves a non-negative least-squares problem and the second maximizes the likelihood function using expectation-maximization. Our simulation results show that the proposed methods outperform the state of the art with respect to various estimation quality metrics and different sample sizes.