Energy-Efficient Trajectory Planning and Spectrum Management for AAVs-Based ISAC in Urban Aerospace Applications

Autonomous Aerial Vehicles (AAVs) have been deployed as aerial base stations in smart cities, offering significant advantages such as flexible deployment, extended coverage, and improved responsiveness in dynamic urban environments. In this paper, the optimization targets of maximizing communication throughput, enhancing sensing accuracy (via the Cramér-Rao Bound), and minimizing energy consumption in a multi-AAVs assisted vehicular network where AAVs provide integrated sensing and communication (ISAC) services to terrestrial vehicles. The optimization problem is formulated as a comprehensive joint framework that simultaneously manages multiple interconnected variables: AAV trajectories, subchannel allocation, user association, and anti-collision constraints, addressing challenges in mixed-integer non-linear programming. To tackle this multi-objective problem, an intelligent optimization algorithm is applied to efficiently search the solution space and identify near-optimal settings across all decision variables. The method continuously adjusts AAV positions and resource allocations as traffic conditions change. The framework is tested through extensive simulations in realistic urban crossroad environments, showing clear gains over alternative methods: 122% higher throughput, 4% lower energy consumption, and a 42% reduction in the Cramér–Rao Bound, indicating improved sensing accuracy compared to the Static model.These results confirm the effectiveness of the proposed framework in enabling agile and energy-efficient AAV-based ISAC systems for next-generation urban aerospace applications.