Beyond Diagonal Intelligent Reflecting Surface Aided Integrated Sensing and Communication

TL;DR

This paper introduces a novel BD-IRS architecture for integrated sensing and communication, optimizing reflection matrices to enhance multi-user rates while estimating target locations with minimal error, despite mutual interference.

Contribution

It proposes a new BD-IRS design, formulates a joint optimization for sensing and communication, and develops a PDD-based algorithm for efficient solution finding.

Findings

The proposed BD-IRS system improves sensing accuracy and user rates.

The PDD algorithm effectively solves the non-convex optimization problem.

TDMA scheme reduces sensing-communication interference, enhancing system performance.

Abstract

Beyond diagonal intelligent reflecting surface (BD-IRS) is a new promising IRS architecture for which the reflection matrix is not limited to the diagonal structure as for conventional IRS. In this paper, we study a BD-IRS aided uplink integrated sensing and communication (ISAC) system where sensing is performed in a device-based manner. Specifically, we aim to estimate the unknown and random location of an active target based on its uplink probing signals sent to a multi-antenna base station (BS) as well as the known prior distribution information of the target's location. Multiple communication users also simultaneously send uplink signals, resulting in a challenging mutual interference issue between sensing and communication. We first characterize the sensing performance metric by deriving the posterior Cram\'{e}r-Rao bound (PCRB) of the mean-squared error (MSE) when prior information is available. Then, we formulate a BD-IRS reflection matrix optimization problem to maximize the minimum expected achievable rate among the multiple users subject to a constraint on the PCRB as well as the lossless and reciprocal constraints on the BD-IRS reflection matrix. The formulated problem is non-convex and challenging to solve. To tackle this problem, we propose a penalty dual decomposition (PDD) based algorithm which can find a high-quality suboptimal solution with polynomial-time complexity. In addition, we propose and optimize a time-division multiple access (TDMA) based scheme which removes the sensing-communication mutual interference. Numerical results verify the effectiveness of the proposed designs and provide useful design insights such as the optimal choice of multiple access scheme.