Joint Optimization on Trajectory, Altitude, Velocity, and Link Scheduling for Minimum Mission Time in UAV-Aided Data Collection
Joint Optimization on Trajectory, Altitude, Velocity, and Link Scheduling for Minimum Mission Time in UAV-Aided Data Collection
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DOI：
10.1109/jiot.2019.2955732
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发表时间：
2020-02-01
影响因子：
10.6
通讯作者：
Ren, Baoquan
中科院分区：
文献类型：
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作者：
Li, Jiaxun;Zhao, Haitao;Ren, Baoquan
由于3D空间的灵活性和空地通信中视线（LoS）的高概率，无人机（UAV）已被视为支持节能数据收集的手段。然而，在紧急应用中，使命完成时间应该是主要关注的问题。在这篇文章中，我们提出了一个无人机辅助数据收集设计，收集数据从一些地面用户（GU）。目标是优化无人机的轨迹、高度、速度和与地面站的数据链路，以最小化总使命时间。然而，困难在于，制定的时间最小化问题与轨迹变量的相互影响。为了解决这个问题，我们首先将原问题等价地转化为轨迹长度问题，然后将问题分解为三个子问题：1）高度优化; 2）轨迹优化; 3）速度和链路调度优化。在高度优化方面，以最大化地面站的传输区域为目标，以利于轨道设计;在轨道优化方面，提出了基于分段的轨道优化算法（STOA），避免了重复飞行;在大规模高密度地面站部署方面，提出了基于分组的轨道优化算法（GTOA），减轻了STOA带来的大量计算量。仿真结果表明，STOA算法和GTOA算法均能获得比现有算法更短的轨迹，且GTOA算法的计算复杂度更低;与基准算法相比，本文提出的时间最小化设计是有效的。
Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV's trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.