RIS-Aided Integrated Sensing and Communication Systems: STAR-RIS Versus Passive RIS?

Abstract

A shortage of frequencies exists due to the demand of extensive connectivity and limited availability of spectrum. Thus a prominent solution of sharing spectrum between radar and communication systems has been proposed. Integrated sensing and communication (ISAC) aims to achieve complete integration and reciprocal advantages between communication and sensing processes. In this work, we consider Simultaneous Transmission and Reflection (STAR) Reconfigurable Intelligent Surface (S-RIS) and passive RIS (P-RIS) assisted dual-function radar communication system where S-RIS and P-RIS assist communication and sensing functionalities, respectively. We formulate an optimization problem that jointly optimizes the beamforming vector at the multi-antenna ISAC transmitter and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit the ISAC transmitter and ensuring the performance of sensing model to detect targets and limitations of phase and amplitude of S-RIS elements. To address the non-convexity, we propose a low-complexity alternating optimization (AO) algorithm. Furthermore, we provide simulation results to verify the viability of the proposed framework with its a) only P-RIS assisted scheme, and b) proposed model with random phase shift, counterparts. The proposed algorithm is also shown to be effective in delivering nearly optimal design outcomes in scenario where the channel state information is imperfect (ICSI). Simulation results demonstrate the impact of RIS elements, number of antennas at the ISAC transmitter and the transmit power on WSR. Accordingly, we illustrate the impact of S-RIS and reflection users (RU) locations and the effect of sensing threshold and the number of targets which highlights the trade-off between sensing and communication. The S-RIS framework offer approximately performance gain of 12.5% and 38.8% as compared to conventional and random cases, respectively.