[论文解读] UAV-Enabled Data Collection for Wireless Sensor Networks with Distributed Beamforming

Tianxin Feng, Lifeng Xie+2 more|arXiv (Cornell University)|Apr 23, 2020

UAV Applications and Optimization参考文献 43被引用 4

一句话总结

本文提出了一种联合无人机轨迹与功率分配设计，用于无人机启用的无线传感器网络，通过分布式波束成形技术，在延迟容忍场景中最大化数据速率吞吐量，并在延迟敏感场景中最小化中断概率。通过利用凸优化与近似技术，所提方案显著优于基准方案，并且随着任务时长增加，性能趋近理论性能上限。

ABSTRACT

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of ground nodes (GNs) via distributed beamforming. We consider two scenarios with delay-tolerant and delay-sensitive applications, in which the GNs send the common/shared messages to the UAV via adaptive- and fixed-rate transmissions, respectively. For the two scenarios, we aim to maximize the average data-rate throughput and minimize the transmission outage probability, respectively, by jointly optimizing the UAV's trajectory design and the GNs' transmit power allocation over time, subject to the UAV's flight speed constraints and the GNs' individual average power constraints. However, the two formulated problems are both non-convex and thus generally difficult to be optimally solved. To tackle this issue, we first consider the relaxed problems in the ideal case with the UAV's flight speed constraints ignored, for which the well-structured optimal solutions are obtained to reveal the fundamental performance upper bounds. It is shown that for the two approximate problems, the optimal trajectory solutions have the same multi-location-hovering structure, but with different optimal power allocation strategies. Next, for the general problems with the UAV's flight speed constraints considered, we propose efficient algorithms to obtain high-quality solutions by using the techniques from convex optimization and approximation. Finally, numerical results show that our proposed designs significantly outperform other benchmark schemes, in terms of the achieved data-rate throughput and outage probability under the two scenarios. It is also observed that when the mission period becomes sufficiently long, our proposed designs approach the performance upper bounds when the UAV's flight speed constraints are ignored.

研究动机与目标

• 通过无人机作为移动融合中心，提升无线传感器网络中的数据采集效率。
• 解决在实际约束条件下优化无人机轨迹与地面节点功率分配的挑战。
• 在支持自适应速率传输的延迟容忍应用中，最大化平均数据速率吞吐量。
• 在采用固定速率传输的延迟敏感应用中，最小化传输中断概率。
• 为由联合轨迹与功率设计引发的非凸优化问题开发高效算法。

提出的方法

• 建立两个非凸优化问题：一个用于吞吐量最大化，另一个用于在无人机速度与节点功率约束下最小化中断概率。
• 通过忽略无人机速度约束来求解松弛问题，以推导出揭示性能上限的最优解。
• 识别出两类问题的最优轨迹均呈现多位置悬停结构，仅在功率分配策略上有所不同。
• 应用凸优化与近似技术，为具有实际无人机速度约束的一般问题设计次优解。
• 采用基于连续凸逼近（SCA）的迭代算法，以处理非凸性并收敛至高质量解。
• 采用集中式设计框架，由无人机通过相位对齐与功率控制协调地面节点的分布式波束成形。

实验结果

研究问题

• RQ1 在支持分布式波束成形的WSN中，能够最大化数据速率吞吐量的最优无人机轨迹结构是什么？
• RQ2 在延迟敏感的无人机数据采集中，联合功率分配与轨迹设计如何提升中断性能？
• RQ3 与单独优化相比，联合优化无人机移动性与地面节点功率可实现多大的性能增益？
• RQ4 当忽略无人机速度约束时，实际设计方案能多接近理论性能上限？
• RQ5 随着任务时长与发射功率的增加，所提方案与上限之间的性能差距有何变化？

主要发现

• 所提的联合轨迹与功率分配设计在吞吐量与中断概率指标上均显著优于基准方案。
• 两类场景下的最优轨迹均呈现多位置悬停结构，表明在关键位置悬停对性能至关重要。
• 当任务时长足够长时，所提设计趋近于忽略速度约束的松弛问题所导出的理论性能上限。
• 由于最优悬停与路径传输之间的权衡，所提方案与上限之间的性能差距随平均发射功率增加而扩大。
• 功率优化起着关键作用：仅优化轨迹的设计方案性能急剧下降，凸显了联合功率与轨迹控制的重要性。
• 在延迟敏感场景中，所提方法的中断概率低于所有基准方案，并且随着飞行时长增加而趋近于上限。