Beamforming Optimization for STAR-RIS-Assisted Integrated Sensing and Communication

In this paper, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted integrated sensing and communication (ISAC) is considered. Based on constraints of communication signal-to-interference-plus-noise ratio (SINR), maximum transmit power limitation, and the law of conservation of energy, the objective is to maximize the SINR of the radar perception signal. To address this non-convex maximization problem, a novel method rooted in fractional programming (FP) and block coordinate descent (BCD) is introduced. To handle non-convex constraints, an alternative optimization algorithm grounded in BCD is introduced. To tackle the non-convex problem of fractional form, the original formulation is optimized utilizing fractional programming techniques, thereby transforming it into a convex problem for more efficient solution. Additionally, select conditions are relaxed through the application of semi-definite relaxation (SDR) techniques. Finally, the numerical results show that: 1) As the number of iterations increases, the proposed algorithm shows good convergence. 2) The performance of the proposed algorithm is significantly better than the state-of-the-art algorithms.