This paper investigates the adaptive trajectory and communication scheduling design for an unmanned aerial vehicle (UAV) relaying random data traffic generated by ground nodes to a base station. The goal is to minimize the expected average communication delay to serve requests, subject to an average UAV mobility power constraint. It is shown that the problem can be cast as a semi-Markov decision process with a two-scale structure, which is optimized efficiently: in the outer decision, the UAV radial velocity for waiting phases and end radius for communication phases optimize the average long-term delay-power trade-off; given outer decisions, inner decisions greedily minimize the instantaneous delay-power cost, yielding the optimal angular velocity in waiting states, and the optimal relay strategy and UAV trajectory in communication states. A constrained particle swarm optimization algorithm is designed to optimize these trajectory problems, demonstrating 100x faster computational speeds than successive convex approximation methods. Simulations demonstrate that an intelligent adaptive design exploiting realistic UAV mobility features, such as helicopter translational lift, reduces the average communication delay and UAV mobility power consumption by 44% and 7%, respectively, with respect to an optimal hovering strategy and by 2% and 13%, respectively, with respect to a greedy delay minimization scheme.

中文翻译：

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功率受限无线无人机中继的自适应调度和轨迹设计

本文研究了无人机 (UAV) 将地面节点产生的随机数据流量中继到基站的自适应轨迹和通信调度设计。目标是最小化服务请求的预期平均通信延迟，受平均 UAV 移动功率约束。结果表明，该问题可以转化为一个具有两尺度结构的半马尔可夫决策过程，该过程得到了有效优化：在外决策中，等待阶段的无人机径向速度和通信阶段的结束半径优化了平均长- 期限延迟-功率权衡；给定外部决策，内部决策贪婪地最小化瞬时延迟功率成本，产生等待状态下的最佳角速度，以及通信状态下的最佳中继策略和无人机轨迹。约束粒子群优化算法旨在优化这些轨迹问题，其计算速度比逐次凸逼近方法快 100 倍。仿真表明，利用直升机平移升力等现实无人机移动特性的智能自适应设计，相对于最佳悬停策略，平均通信延迟和无人机移动功耗分别降低了 44% 和 7%，降低了 2% 和对于贪婪延迟最小化方案，分别为 13%。