[论文解读] Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective

Shuowen Zhang, Yong Zeng+1 more|arXiv (Cornell University)|May 18, 2018

UAV Applications and Optimization参考文献 63被引用 14

一句话总结

该论文提出了一种用于蜂窝连接无人机的连通性约束轨迹优化框架，旨在最小化任务完成时间的同时通过地面基站（GBS）保持可靠的通信。通过利用图论和凸优化，作者设计了一种多项式时间算法，实现了接近最优的性能，并具备灵活的复杂度-性能权衡，确保在整个飞行过程中满足最低信噪比（SNR）要求。

ABSTRACT

Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main paradigms in cellular UAV communications, i.e., cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location, during which it needs to maintain reliable communication with the cellular network by associating with one GBS at each time instant. We aim to minimize the UAV's mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target. To tackle this challenging non-convex problem, we first propose a graph connectivity based method to verify its feasibility. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging graph theory and convex optimization techniques. The proposed methods are analytically shown to be capable of achieving a flexible trade-off between complexity and performance, and yielding a solution that is arbitrarily close to the optimal solution in polynomial time. Finally, we make concluding remarks and point out some promising directions for future work.

研究动机与目标

• 解决长距离任务中无人机维持可靠蜂窝连通性的挑战。
• 优化无人机轨迹以最小化任务完成时间，同时确保持续连接到最近且视 Line-of-Sight（LoS）信道最佳的GBS。
• 在整个飞行过程中保证GBS-无人机链路的最低所需SNR。
• 开发高效算法，在计算复杂度与性能之间实现平衡，以支持实际部署。

提出的方法

• 将轨迹设计建模为带有连通性与SNR约束的非凸优化问题。
• 引入一种基于等价图的可行性验证方法，用于检查任意两点间的连通性。
• 基于GBS关联序列随时间的变化，推导最优轨迹的结构性质。
• 提出两种高效算法：一种基于图论，另一种基于凸优化，两者均可实现任意接近最优的解。
• 利用GBS覆盖区域之间的量化边界点，近似最优的基站切换点。
• 通过涉及角分辨率与最大距离偏差的几何论证，界定次优性差距。

实验结果

研究问题

• RQ1 如何优化无人机轨迹以最小化任务时间，同时保持与蜂窝网络的可靠连接？
• RQ2 在连通性与SNR约束下，最优轨迹的结构性质是什么？
• RQ3 低复杂度算法是否能在多项式时间内实现近似最优性能？
• RQ4 GBS覆盖边界点的量化如何影响与最优解的性能差距？

主要发现

• 所提出的算法在多项式时间内实现了与最优轨迹任意接近的解。
• 次优性差距被限定为 $ 4(M-1)\bar{d}\sin\left(\frac{\pi}{4(Q-1)}\right) $，其中 $ M $ 为使用的最大GBS数量，$ Q $ 为每条边界上的量化点数。
• 该方法通过在每个时间点动态选择最佳GBS，确保在整个任务过程中维持最低所需SNR。
• 数值结果表明，在任务完成时间和连通性可靠性方面，性能优于基准方案。
• 基于图的连通性验证可行性检查方法，可在优化过程中高效剔除不可行轨迹。
• 对GBS关联序列的结构性洞察显著减少了搜索空间，支持可扩展部署。