Joint Optimization for Sensing-Communication Trade-off in RIS-Aided ISAC Systems

Integrated sensing and communication (ISAC) is a promising technology to alleviate spectrum congestion by enabling simultaneous sensing and data transmission. Leveraging the capability of reconfigurable intelligent surfaces (RIS) to shape wireless channels, this paper investigates a RIS-aided ISAC system where a multi-antenna base station (BS) serves multiple users while sensing multiple targets. We aim to maximize the sensing signal-to-noise ratio (SNR) subject to multi-user signal-to-interference-plus-noise ratio (SINR) constraints, the BS transmit power budget, and the unit-modulus constraints of RIS phase shifts. To address the resulting nonconvex optimization problem, we propose an efficient alternating algorithm. Specifically, the BS beamformer is updated via sequential rank-one constraint relaxation (SROCR), while the RIS reflection coefficients are optimized using a majorization-minimization (MM) scheme integrated with SROCR. Simulation results demonstrate that the proposed method converges rapidly and achieves a superior sensing-communication trade-off compared to conventional ISAC schemes.