The development of wireless power transfer (WPT) and Internet of Things (IoT) offers significant potential but faces challenges such as limited energy supply, dynamic environmental changes, and unstable transmission links. This paper presents an unmanned aerial vehicle (UAV)-assisted data collection and WPT scheme to support batteryless sensor (BLS) networks in remote areas. In this system, BLSs harvest energy from the UAV and utilize the harvested energy to transmit the collected data back to the UAV. The goal is to maximize the collected data volume and fairness index while minimizing the UAV energy consumption. To achieve these objectives, an optimization problem is formulated to jointly optimize the transmit power and UAV trajectory. Due to the non-convexity and dynamic nature of the problem, a deep reinforcement learning (DRL)-based algorithm is proposed to solve the problem. Specifically, this algorithm integrates prioritized experience replay and the performer module to enhance system stability and accelerate convergence. Simulation results demonstrate that the proposed approach consistently outperforms benchmark schemes in terms of collected data volume, fairness, and UAV energy consumption.

翻译：无线能量传输与物联网技术的发展展现出巨大潜力，但面临着能源供应受限、环境动态变化及传输链路不稳定等挑战。本文提出一种无人机辅助的数据收集与无线能量传输方案，以支持偏远地区的无电池传感器网络。在该系统中，无电池传感器从无人机获取能量，并利用收集的能量将采集的数据回传至无人机。目标是在最小化无人机能耗的同时，最大化数据收集量与公平性指标。为实现这些目标，本文构建了一个联合优化发射功率与无人机轨迹的优化问题。鉴于问题的非凸性与动态特性，提出一种基于深度强化学习的算法进行求解。具体而言，该算法融合了优先经验回放与执行器模块，以提升系统稳定性并加速收敛。仿真结果表明，所提方法在数据收集量、公平性和无人机能耗方面均持续优于基准方案。