Joint Beamforming and UAV Deployment Optimization for ISAC-Enhanced UAV-Assisted VEC

In urban temporary congestion or hotspot scenarios, fixed roadside units (RSUs) often fail to provide reliable and efficient communication services due to their static deployment. Unmanned Aerial Vehicles (UAVs) can be rapidly deployed to provide flexible and on-demand communication and sensing support, effectively complementing ground infrastructure. However, UAVs are faced with challenges such as limited coverage, high deployment complexity, and unbalanced communication-sensing performance. These challenges give rise to increased energy consumption and reduced communication efficiency. To address these issues, we propose a UAV energy-efficient deployment method based on Integrated Sensing and Communication (ISAC), which balances performance and energy consumption. Specifically, under UAV energy constraints, we jointly optimize UAV deployment positions and beamforming to maximize communication capacity. We decompose the problem into two subproblems: UAV deployment and beamforming strategy optimization. During the iteration process, the subproblems are respectively solved by using the sparrow search algorithm based on refraction-based learning and successive convex approximation-based iterative algorithm and the first order Taylor expansion method. Simulation results show that the proposed method outperforms the benchmark schemes, achieving approximately a 10.51% improvement in average coverage rate and a 19.83% reduction in UAV energy consumption, while maintaining an effective trade-off between communication coverage and energy efficiency.