Reduced-Rank Channel Estimation for Large-Scale MIMO Systems

TL;DR

This paper introduces two reduced-rank channel estimators for large-scale MIMO systems that leverage channel sparsity in the transform domain, improving estimation accuracy and providing additional angle-of-arrival information.

Contribution

The paper proposes novel reduced-rank estimators that utilize transform domain sparsity and angle alignment, along with algorithms for optimal rank and basis selection in large-scale MIMO.

Findings

Estimators outperform traditional methods in MSE performance.

Proper basis and rank selection are crucial for estimator effectiveness.

Angle alignment enhances rank reduction and estimator accuracy.

Abstract

We present two reduced-rank channel estimators for large-scale multiple-input, multiple-output (MIMO) systems and analyze their mean square error (MSE) performance. Taking advantage of the channel's transform domain sparseness, the estimators yield outstanding performance and may offer additional mean angle-of-arrival (AoA) information. It is shown that, for the estimators to be effective, one has to select a proper predetermined unitary basis (transform) and be able to determine the dominant channel rank and the associated subspace. Further MSE analysis reveals the relations among the array size, channel rank, signal-to-noise ratio (SNR), and the estimators' performance. It provides rationales for the proposed rank determination and mean AoA estimation algorithms as well. An angle alignment operation included in one of our channel models is proved to be effective in further reducing the required rank, shifting the dominant basis vectors' range (channel spectrum) and improving the estimator's performance when a suitable basis is used. We also draw insightful analogies among MIMO channel modeling, transform coding, parallel spatial search, and receive beamforming. Computer experiment results are provided to examine the numerical effects of various estimator parameters and the advantages of the proposed channel estimators and rank determination method.