Energy efficiency optimization for UAV-mounted RIS-aided integrated sensing and communication systems

Energy Efficiency (EE) optimization has emerged as a crucial and challenging task in Integrated Sensing and Communication (ISAC) systems. Although Reconfigurable Intelligent Surface (RIS) has been proven as an effective technology for enhancing ISAC system performance, the traditional fixed-deployed RIS suffers from location constraints, making it difficult to apply in dynamic wireless environments.Motivated by the above, we propose a novel ISAC system architecture based on Unmanned Aerial Vehicle (UAV) mounted passive RIS, which leverages the mobility of UAV to optimize the three-dimensional deployment position of RIS. In addition, limited by the system power budget, we formulate a joint optimization problem aimed at maximizing the EE while ensuring user communication quality of service and target sensing signal-to-noise ratio requirements. Specifically, by integrating generalized Rayleigh quotient optimization, semidefinite relaxation, and majorization-minimization frameworks with Particle Swarm Optimization (PSO), a novel alternating optimization algorithm is proposed for UAV trajectory planning that exploits PSO’s swarm intelligence search and adaptive evolution characteristics. Numerical results demonstrate that the proposed UAV-mounted RIS scheme significantly improves EE and exhibits superior adaptability and performance in mobile wireless environments.