[论文解读] Radio Map Based 3D Path Planning for Cellular-Connected UAV

Shuowen Zhang, Rui Zhang|arXiv (Cornell University)|Nov 29, 2019

UAV Applications and Optimization参考文献 37被引用 9

一句话总结

该论文提出了一种基于无线电信道图的三维路径规划框架，用于蜂窝连接的无人机（UAV），在确保与地面基站（GBS）的SINR目标的同时最小化飞行距离。通过从大规模路径损耗和干扰模型构建三维SINR图，利用图论中的最短路径问题（SPP）公式求解最优路径，结合网格量化方法在保持性能权衡的同时降低复杂度。

ABSTRACT

In this paper, we study the three-dimensional (3D) path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the expected signal-to-interference-plus-noise ratio (SINR) at the UAV receiver with each of its associated ground base stations (GBSs) during the flight. To exploit the location-dependent and spatially varying channel as well as interference over the 3D space, we propose a new radio map based path planning framework for the UAV. Specifically, we consider the channel gain map of each GBS that provides its large-scale channel gains with uniformly sampled locations on a 3D grid, which are due to static and large-size obstacles (e.g., buildings) and thus assumed to be time-invariant. Based on the channel gain maps of GBSs as well as their loading factors, we then construct an SINR map that depicts the expected SINR levels over the sampled 3D locations. By leveraging the obtained SINR map, we proceed to derive the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. We further propose a grid quantization approach where the grid points in the SINR map are more coarsely sampled by exploiting the spatial channel/interference correlation over neighboring grids. Then, we solve an approximate SPP over the reduced-size SINR map (graph) with reduced complexity. Numerical results show that the proposed solution can effectively minimize the flying distance/time of the UAV subject to its communication quality constraint, and a flexible trade-off between performance and complexity can be achieved by adjusting the grid quantization ratio in the SINR map. Moreover, the proposed solution significantly outperforms various benchmark schemes without fully exploiting the channel/interference spatial distribution in the network.

研究动机与目标

• 解决蜂窝连接无人机的干扰感知三维路径规划挑战，以确保可靠的空-地通信。
• 在目标信号干扰加噪声比（SINR）约束下，最小化无人机从初始位置到目标位置的飞行距离。
• 利用三维城市环境中空间可变的信道和干扰特性，提升路径规划性能。
• 通过SINR图表示中的网格量化，实现灵活的性能-复杂度权衡。
• 优于基准方案，后者未能充分利用空间信道和干扰分布的全貌。

提出的方法

• 构建一个均匀采样的三维无人机位置网格，并计算每个地面基站（GBS）到每个网格点的大规模路径损耗信道增益。
• 引入GBS负载因子以建模上行链路干扰，并在每个三维网格点计算预期SINR，从而形成三维SINR图。
• 将无人机路径规划建模为图论中的最短路径问题（SPP），其中节点表示可行的三维网格点，边表示具有关联距离的转移。
• 应用网格量化通过利用空间相关性来粗化SINR图，减少图的规模和计算复杂度。
• 在简化后的图上使用标准算法（如Dijkstra算法）求解SPP，以获得次优但高效的路径。
• 通过允许有限通信中断（最多$O_T = 50$ m路径长度）将框架扩展为具有抗断续能力的变体，以访问更多可行飞行区域并减少总飞行距离。

实验结果

研究问题

• RQ1 如何优化蜂窝连接无人机的三维路径规划，以在维持目标SINR的同时最小化飞行距离？
• RQ2 空间相关的阴影衰落和来自多个GBS的干扰对无人机路径可行性与性能有何影响？
• RQ3 如何高效地在三维空间中表示大规模信道和干扰特性，以支持路径规划？
• RQ4 在SINR图构建中，网格量化在实时无人机路径规划中的性能-复杂度权衡如何？
• RQ5 允许在低SINR区域出现短时通信中断的抗断续路径规划方法是否能减少飞行距离？

主要发现

• 所提出的最优路径规划方案在SINR目标为$\bar{\gamma}_{\mathrm{T}} = -0.2$ dB时，飞行距离达到945.924 m。
• 具有抗断续能力的路径规划变体在相同SINR目标和$O_T = 50$ m断续容忍度下，将飞行距离减少至933.210 m，显示出更高的效率。
• 网格量化方法通过控制空间分辨率，在显著降低计算复杂度的同时保持了接近最优的性能。
• 数值结果表明，所提方法优于未充分利用干扰和信道增益空间分布全貌的基准方案。
• SINR图有效捕捉了由于波束下倾导致的非单调路径损耗行为和覆盖盲区，即使在复杂城市环境中也能实现准确的路径选择。
• 通过调整SINR图中网格量化比，该框架支持在性能与复杂度之间灵活权衡。