论文标题
多人ATENNA无人机数据收集：联合轨迹和通信优化
Multi-Antenna UAV Data Harvesting: Joint Trajectory and Communication Optimization
论文作者
论文摘要
无人驾驶飞机（UAV）的通信是一项有前途的技术，可扩展覆盖范围并增强传统无线通信系统的吞吐量。在本文中，我们考虑了一个启用了无人机的无线传感器网络（WSN），其中派遣了多个Antenna无人机以从一组传感器节点（SNS）收集数据。目的是通过共同优化其传输计划和功率分配以及无人机的轨迹来最大化所有SN的最低数据收集率，但要受到SNS最大传输功率的实际约束和无人机的最大速度。法式优化问题的解决方案是具有挑战性的，因为它涉及非凸约限制和离散值变量。为了汲取有用的见解，我们首先通过忽略UAV速度约束并根据Lagrange二元方法进行最佳解决，首先考虑法式问题的特殊情况。结果表明，对于这个轻松的问题，无人机应徘徊在有限数量的最佳位置以上，通常持续时间不同。接下来，我们解决了考虑使用UAV速度约束并提出旅行推销员问题（TSP）基于轨迹的初始化的一般情况，在这种情况下，无人机依次访问在放松问题中以最小的飞行时间获得的位置。鉴于此初始轨迹，我们然后找到SNS的相应传输调度和功率分配，并通过应用块坐标下降（BCD）和连续的凸近似（SCA）技术进一步优化无人机轨迹。最后，提供了数值结果，以说明与基准方案相比，提出的多Antenna UAV数据收获方案的频谱和能效增长。
Unmanned aerial vehicle (UAV)-enabled communication is a promising technology to extend coverage and enhance throughput for traditional terrestrial wireless communication systems. In this paper, we consider a UAV-enabled wireless sensor network (WSN), where a multi-antenna UAV is dispatched to collect data from a group of sensor nodes (SNs). The objective is to maximize the minimum data collection rate from all SNs via jointly optimizing their transmission scheduling and power allocations as well as the trajectory of the UAV, subject to the practical constraints on the maximum transmit power of the SNs and the maximum speed of the UAV. The formulated optimization problem is challenging to solve as it involves non-convex constraints and discrete-value variables. To draw useful insight, we first consider the special case of the formulated problem by ignoring the UAV speed constraint and optimally solve it based on the Lagrange duality method. It is shown that for this relaxed problem, the UAV should hover above a finite number of optimal locations with different durations in general. Next, we address the general case of the formulated problem where the UAV speed constraint is considered and propose a traveling salesman problem (TSP)-based trajectory initialization, where the UAV sequentially visits the locations obtained in the relaxed problem with minimum flying time. Given this initial trajectory, we then find the corresponding transmission scheduling and power allocations of the SNs and further optimize the UAV trajectory by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Finally, numerical results are provided to illustrate the spectrum and energy efficiency gains of the proposed scheme for multi-antenna UAV data harvesting, as compared to benchmark schemes.
