Energy efficiency optimization for UAV-mounted IRS assisted ISAC systems under statistical CSI

Integrated Sensing and Communication (ISAC) systems are advantageous for enhancing both communication and sensing capabilities, but their performance is significantly impacted by signal blockages in dynamic vehicular environments. An Unmanned Aerial Vehicle (UAV)-mounted Intelligent Reflective Surface (IRS) for air-to-ground communication and sensing can significantly enhance coverage and deployment flexibility. However, the additional power consumption of the UAV-mounted IRS (UIRS) remains a challenge. To mitigate this, we propose a novel UIRS-assisted ISAC system that aims to maximize communication energy efficiency (EE) while meeting sensing quality-of-service (QoS) requirements by optimizing the UAV trajectory, IRS passive beamforming, and base station (BS) active beamforming. Due to the complex and dynamic nature of wireless channels, acquiring Channel State Information (CSI) is challenging, especially with the UAV's mobility and the passive mode of IRS. Therefore, statistical CSI is adopted in the proposed scheme. The optimization problem is reformulated into a tractable form and solved by decomposing it into three subproblems, which include using the Dinkelbach transformation for fractional programming in EE calculation, Successive Convex Approximation (SCA) for UAV trajectory optimization, and Semi-Definite Relaxation (SDR) for both active and passive beamforming designs. An alternating optimization (AO)-based framework iteratively solves all subproblems, with proven algorithm convergence and computational efficiency. Simulation results demonstrate that the proposed UIRS-assisted ISAC system significantly improves both communication and sensing performance compared to benchmark schemes.