基于RL的货运无人机轨迹规划与小区关联，以最小化切换、断连和能耗
RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption
AI总结：
针对货运无人机能耗、蜂窝可靠性与频繁切换问题，提出基于强化学习的轨迹规划和小区关联联合优化方法，并通过仿真验证其综合性能优于基准。
摘要：
无人机（UAV）是用于最后一英里货物配送的一项有前景的技术。然而，有限的机载电池容量、蜂窝网络不可靠性以及空域中的频繁切换，是释放其全部潜力的主要障碍。鉴于现有蜂窝网络主要是为服务地面用户而设计的，将同一架构重新用于高移动性空中用户（例如货运无人机）被认为具有挑战性。事实上，为确保使用货运无人机安全配送，关键在于高效利用可用能量，同时保证指挥与控制的可靠连接，并避免频繁切换。为实现这一目标，我们提出了一种联合进行货运无人机轨迹规划和小区关联的新方法。具体而言，我们将货运无人机任务表述为一个多目标问题，旨在：1）最小化能耗，2）减少切换事件，以及3）保证沿轨迹的蜂窝可靠性。我们利用强化学习（RL）联合优化货运无人机的轨迹和小区关联。仿真结果表明，与基准方法相比，我们提出的方法在切换、断连和能耗方面均表现出性能提升。
英文摘要：
Unmanned aerial vehicle (UAV) is a promising technology for last-mile cargo delivery. However, the limited on-board battery capacity, cellular unreliability, and frequent handoffs in the airspace are the main obstacles to unleash its full potential. Given that existing cellular networks were primarily designed to service ground users, re-utilizing the same architecture for highly mobile aerial users, e.g., cargo-UAVs, is deemed challenging. Indeed, to ensure a safe delivery using cargo-UAVs, it is crucial to utilize the available energy efficiently, while guaranteeing reliable connectivity for command-and-control and avoiding frequent handoff. To achieve this goal, we propose a novel approach for joint cargo-UAV trajectory planning and cell association. Specifically, we formulate the cargo-UAV mission as a multi-objective problem aiming to 1) minimize energy consumption, 2) reduce handoff events, and 3) guarantee cellular reliability along the trajectory. We leverage reinforcement learning (RL) to jointly optimize the cargo-UAV's trajectory and cell association. Simulation results demonstrate a performance improvement of our proposed method, in terms of handoffs, disconnectivity, and energy consumption, compared to benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
