Spatio-Temporal Trajectory-Driven Dynamic TDMA Scheduling for UAV-Assisted Wireless-Powered Communication Networks

UAV-assisted data collection often suffers from spatial data holes and communication unfairness, a challenge exacerbated in Wireless Powered Communication Networks (WPCNs) by the inherent doubly near-far problem. To bridge these gaps, this paper proposes a novel Spatio-Temporal Trajectory-Driven Dynamic Time-Division Multiple Access (STD-TDMA) scheduling strategy. Deviating from conventional discrete hovering paradigms, we introduce a continuous-flight framework that exploits the UAV's mobility to provide seamless spatial coverage. By jointly optimizing the UAV's flight speed and dynamic time-slot allocation, the proposed strategy ensures that each sensor node can interact with the UAV at its optimal channel condition along the trajectory, thereby effectively mitigating the doubly near-far effect and ensuring absolute nodal fairness. To solve the formulated non-convex optimization problem, we develop a low-complexity algorithm that integrates Binary Search for speed optimization with the Hungarian algorithm for spatio-temporal mapping. Extensive simulations demonstrate that our STD-TDMA strategy significantly enhances nodal fairness and boosts overall task execution efficiency compared to conventional baseline schemes.