Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication

TL;DR

This paper investigates the fundamental capacity limits of IRS-aided MIMO systems, proposing optimization algorithms for narrowband and broadband scenarios, demonstrating significant capacity improvements over traditional MIMO channels.

Contribution

It introduces novel optimization algorithms for IRS reflection and MIMO transmit covariance to maximize capacity in both narrowband and broadband MIMO systems, including OFDM.

Findings

Algorithms achieve substantially higher capacity than traditional MIMO without IRS.

Proposed methods significantly improve IRS channel parameters like power, rank, and condition number.

Numerical results validate the effectiveness of the algorithms in capacity enhancement.

Abstract

Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices can be optimized for different subcarriers while only one common set of IRS reflection coefficients can be designed to cater to all subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.