Toward A Low-Altitude Economy: Radio Map-Driven Optimization of Energy Efficiency in Connectivity-Aware Cargo UAVs

In this paper, we investigate a connectivity-aware unmanned aerial vehicle (UAV)-enabled cargo pickup system, where both payload capacity and onboard energy are constrained. Within the emerging low-altitude economy, UAV-based intelligent cargo delivery and pickup hold substantial promise. However, conventional UAVs are predominantly pilot-operated, which restricts their operational range and heightens the risk of disconnection. Benefiting from integration with low-altitude cellular networks, UAV communications can be managed by cellular base stations (BSs), thereby enabling large-scale deployment, beyond-visual-line-of-sight operation, and autonomous intelligent flight. While the incorporation of UAVs into cellular networks offers notable advantages, it also presents critical challenges. The existing terrestrial cellular infrastructure typically delivers poor coverage at low altitudes due to the downward tilt of antennas and the signal obstruction caused by urban buildings. To address these limitations, we propose a novel radio map-based hierarchical optimization (RM-HO) algorithm. Specifically, we first introduce an enhanced A∗ algorithm that leverages the radio map to optimize UAV flight trajectories and guarantee communication quality. Subsequently, we employ a particle swarm optimization (PSO) algorithm to optimize task allocation and flight speed under payload and energy constraints, thereby reducing energy consumption. Numerical evaluations demonstrate that the proposed RM-HO algorithm outperforms the benchmarks in terms of energy efficiency.