Blockage-Aware UAV-Assisted Wireless Data Harvesting With Building Avoidance

Unmanned aerial vehicles (UAVs) offer dynamic trajectory control, enabling them to avoid obstacles and establish line-of-sight (LoS) wireless channels with ground nodes (GNs), unlike traditional ground-fixed base stations. This study addresses the joint optimization of scheduling and three-dimensional (3D) trajectory planning for UAV-assisted wireless data harvesting. The objective is to maximize the minimum uplink throughput among GNs while accounting for signal blockages and building avoidance. To achieve this, we first present mathematical models designed to avoid cuboid-shaped buildings and to determine wireless signal blockage by buildings through rigorous mathematical proof. The optimization problem is formulated as nonconvex mixed-integer nonlinear programming and solved using advanced techniques. Specifically, the problem is decomposed into convex subproblems via quadratic transform and successive convex approximation. Building avoidance and signal blockage constraints are incorporated using the separating hyperplane method and an approximated indicator function. These subproblems are then iteratively solved using the block coordinate descent algorithm. Simulation results validate the effectiveness of the proposed approach. The UAV dynamically adjusts its trajectory and scheduling policy to maintain LoS channels with GNs, significantly enhancing network throughput compared to existing schemes. Moreover, the trajectory of the UAV adheres to building avoidance constraints for its continuous trajectory, ensuring uninterrupted operation and compliance with safety requirements.