IRS-aided MIMO Systems over Double-scattering Channels: Impact of Channel Rank Deficiency

TL;DR

This paper investigates the impact of channel rank deficiency in IRS-aided MIMO systems over double-scattering channels, proposing a deterministic approximation and an optimization algorithm to mitigate performance loss.

Contribution

It introduces a deterministic approximation for ergodic rate in rank-deficient channels and an optimization method to enhance system performance.

Findings

DA is tight and accurate for ergodic rate estimation.

Proposed algorithm effectively mitigates performance loss due to rank deficiency.

Double-scattering model captures realistic channel rank limitations.

Abstract

Intelligent reflecting surfaces (IRSs) are promising enablers for next-generation wireless communications due to their reconfigurability and high energy efficiency in improving poor propagation condition of channels, e.g., limited scattering environment. However, most existing works assumed full-rank channels requiring rich scatters, which may not be available in practice. To analyze the impact of rank-deficient channels and mitigate the ensued performance loss, we consider a large-scale IRS-aided MIMO system with statistical channel state information (CSI), where the double-scattering channel is adopted to model rank deficiency. By leveraging random matrix theory (RMT), we first derive a deterministic approximation (DA) of the ergodic rate with low computational complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can effectively mitigate the performance loss induced by channel rank deficiency.