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（UAV）技术的快速发展，其为地面节点提供了灵活的通信服务。然而，能量效率对无人机的部署至关重要，尤其是旋翼无人机，其推进功率易受风效应影响。本文首先基于随机风场建模与三维受力分析，推导了适用于旋翼无人机的三维广义推进能耗模型（GPECM）。基于该模型，我们研究了一个无人机辅助下行通信系统，其中旋翼无人机在随机风扰动下飞行，并为地面用户（GU）提供通信服务。我们的目标是基于GPECM，通过联合优化无人机三维轨迹和地面用户调度，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，但由于缺乏实时风场信息而难以求解。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，我们利用基于风场统计信息的随机规划（SP）方法平均化风对无人机的影响；随后，在在线阶段，我们进一步优化瞬时速度以适应实时风场。仿真结果表明，无人机在两个阶段优化后的轨迹能更好地适应风速和风向的变化，相比无风方案取得了更高的能量效率。特别地，我们提出的OBOA设计可应用于强风骤变场景，使无人机动态调整速度以提升能量效率性能。