Optimization of STAR-RIS-Aided DFRC Systems: Balancing Sensing and Communication

Dual-functional radar and communication (DFRC), aided by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS), offers promising potential for performance enhancement. However, coupled time, frequency, and space resources, echo interference between communication and sensing, and the lack of STAR-RIS customized operation protocols limit the performance improvement of STAR-RIS-aided DFRC systems. In this paper, we investigate a performance optimization problem in STAR-RIS-aided DFRC systems, which involves balancing the defined sensing mutual information and communication performance by optimizing sub-carrier allocation, active beamforming, STAR-RIS reflection and transmission coefficients. An optimization-training heuristic alternating optimization scheme that considers the algorithm's performance and cost is proposed entirely using successive convex approximation, fractional programming, and particle swarm optimization. Simulation results demonstrate the convergence and effectiveness of the proposed scheme, highlighting its advantages in improving sensing performance compared with benchmark schemes and emphasizing its value in practical applications.