The rapid development of unmanned aerial vehicle (UAV) technology provides flexible communication services to terrestrial nodes. Energy efficiency is crucial to the deployment of UAVs, especially rotary-wing UAVs whose propulsion power is sensitive to the wind effect. In this paper, we first derive a three-dimensional (3D) generalised propulsion energy consumption model (GPECM) for rotary-wing UAVs under the consideration of stochastic wind modeling and 3D force analysis. Based on the GPECM, we study a UAV-enabled downlink communication system, where a rotary-wing UAV flies subject to stochastic wind disturbance and provides communication services for ground users (GUs). We aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing the 3D trajectory and user scheduling among the GUs based on the GPECM. We formulate the problem as stochastic optimization, which is difficult to solve due to the lack of real-time wind information. To address this issue, we propose an offline-based online adaptive (OBOA) design with two phases, namely, an offline phase and an online phase. In the offline phase, we average the wind effect on the UAV by leveraging stochastic programming (SP) based on wind statistics; then, in the online phase, we further optimize the instantaneous velocity to adapt the real-time wind. Simulation results show that the optimized trajectories of the UAV in both two phases can better adapt to the wind in changing speed and direction, and achieves a higher EE compared with the windless scheme. In particular, our proposed OBOA design can be applied in the scenario with dramatic wind changes, and makes the UAV adjust its velocity dynamically to achieve a better performance in terms of EE.

翻译：随着无人机技术的快速发展，其为地面节点提供了灵活的通信服务。能量效率对于无人机的部署至关重要，尤其是旋翼无人机，其推进功率对风效应非常敏感。本文首先在考虑随机风建模和三维力学分析的基础上，推导了旋翼无人机的三维广义推进能量消耗模型（GPECM）。基于该模型，我们研究了一个无人机辅助的下行通信系统，其中旋翼无人机在随机风扰动下飞行，并为地面用户提供通信服务。旨在基于GPECM通过联合优化三维轨迹和用户调度，最大化无人机的能量效率（EE）。我们将该问题建模为随机优化问题，由于缺乏实时风信息，该问题难以求解。为解决这一问题，我们提出了一种基于离线-在线自适应（OBOA）的两阶段设计方法，即离线阶段和在线阶段。在离线阶段，我们利用基于风统计的随机规划（SP）对无人机的风效应进行平均化处理；然后在在线阶段，进一步优化瞬时速度以适应实时风况。仿真结果表明，两阶段优化后的无人机轨迹能更好地适应风速和风向的变化，相比无风方案实现了更高的能量效率。特别是，我们提出的OBOA设计可应用于风况剧烈变化的场景，使无人机动态调整速度以获得更好的能量效率性能。