Probing Power and Minimum Rate Maximization for RIS-Aided ISAC System

With the growing demand for wireless resources, integrated sensing and communication (ISAC) technology has recently gained extensive attention from researchers. Current solutions fail to adapt to the dynamic needs of different users, leading to inefficiencies and resource waste. This paper proposes a scalable framework for real-time beamforming optimization in a reconfigurable intelligent surface (RIS) assisted ISAC system. Both active beamforming at the base station (BS) and passive beamforming at the RIS are optimized to enhance the minimum communication rate for users and increase probing power for sensing performance. Considering the working principles of different ISAC systems, we investigate two transmission techniques based on separated and shared antenna deployment at BS. During the optimization process, the weighted minimum mean-square error (WMMSE) method is employed for active beamforming, while fractional programming (FP) is used for passive beamforming to reformulate target functions into more tractable forms. Numerical results demonstrate that the algorithm is reliable and the RIS makes a 10% improvement in communication rate in both separated and shared deployment scenarios.