Spatial Channel Covariance Estimation for the Hybrid MIMO Architecture: A Compressive Sensing Based Approach

TL;DR

This paper introduces two compressive sensing-based methods for estimating spatial channel covariance in hybrid MIMO systems, addressing the challenge of limited RF chains and extending to wideband scenarios.

Contribution

It proposes novel covariance estimation algorithms leveraging time-varying sensing matrices and Hermitian properties, validated through analysis and simulations.

Findings

Proposed methods outperform traditional estimators.

Algorithms effectively extend to wideband, multi-antenna systems.

Theoretical analysis confirms the superiority of the approaches.

Abstract

Spatial channel covariance information can replace full knowledge of the entire channel matrix for designing analog precoders in hybrid multiple-input-multiple-output (MIMO) architecture. Spatial channel covariance estimation, however, is challenging for the hybrid MIMO architecture because the estimator operating at baseband can only obtain a lower dimensional pre-combined signal through fewer radio frequency (RF) chains than antennas. In this paper, we propose two approaches for covariance estimation based on compressive sensing techniques. One is to apply a time-varying sensing matrix, and the other is to exploit the prior knowledge that the covariance matrix is Hermitian. We present the rationale of the two ideas and validate the superiority of the proposed methods by theoretical analysis and numerical simulations. We conclude the paper by extending the proposed algorithms from narrowband massive MIMO systems with a single receive antenna to wideband systems with multiple receive antennas.