SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems

TL;DR

This paper proposes joint beamforming and reflection design strategies for RIS-assisted ISAC systems, optimizing for communication and sensing performance under SNR and CRB constraints, with algorithms validated through extensive simulations.

Contribution

It introduces novel optimization algorithms for joint beamforming and RIS reflection design in RIS-ISAC systems considering SNR and CRB constraints, enhancing both communication and sensing capabilities.

Findings

More RIS elements improve DoA estimation more than target detection.

Proposed algorithms outperform existing schemes in simulations.

Joint design balances communication and sensing performance effectively.

Abstract

In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultra-reliable communication and high-accuracy sensing functions. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communications and radar sensing with the assistance of an RIS. We focus on both target detection and parameter estimation performance in terms of the signal-to-noise ratio (SNR) and Cramer-Rao bound (CRB), respectively. Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients. Efficient algorithms are developed to solve these two complicated non-convex problems. Extensive simulation results demonstrate the advantages of the proposed joint beamforming and reflection designs compared with other schemes. In addition, it is shown that more RIS reflection elements bring larger performance gains for direct-of-arrival (DoA) estimation than for target detection.