Multi-UAV Enabled Data Collection with Efficient Joint Adaptive Interference Management and Trajectory Design
Multi-UAV Enabled Data Collection with Efficient Joint Adaptive Interference Management and Trajectory Design
复制标题
通过高效联合自适应干扰管理和轨迹设计实现多无人机数据收集
DOI：
10.3390/electronics10050547
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发表时间：
2021-02
期刊：
影响因子：
2.9
通讯作者：
Zhou Jianming
中科院分区：
文献类型：
--
作者：
Pi Weichao;Zhou Jianming
本文研究了多个无人机（UAV）从一组分布式传感器无线采集数据的数据采集场景中的干扰。为了提高通信吞吐量和最小化完成时间，本文设计了一种联合资源分配和轨迹优化框架，该框架既兼容传统的时分方案和干扰协调方案，又结合了它们的优点。首先，分析了一个基本的准静态场景，其中两个无人机和四个设备以最优的位移悬停来执行数据采集使命，并证明了所提出的最优资源分配和轨迹解是根据干扰的严重程度自适应调整的，并且网络的公共吞吐量是非递减的。其次，针对一般的移动的情况，设计了一种有效的联合求解资源分配和轨迹优化的算法，该算法首先采用块坐标下降法将原非凸问题分解为三个非凸子问题，然后采用专用的遗传算法，罚函数和顺序凸近似（SCA）技术，以有效地解决个别子，问题，并获得满意的局部最优解的自适应初始化计划。随后，数值实验表明，数据收集任务的完成时间与我们所提出的方法是至少25%，比那些与几个基线动态正交计划时，部署4无人机缩短。最后，我们提供了一个实际应用的原则，有关最大的无人机适合的数量，以避免所提出的算法的固有缺陷。
This paper studies interference in a data collection scenario in which multiple unmanned aerial vehicles (UAVs) are dispatched to wirelessly collect data from a set of distributed sensors. To improve the communication throughput and minimize the completion time, we design a joint resource allocation and trajectory optimization framework that not only is compatible with the traditional time-division scheme and interference coordination scheme but also combines their advantages. First, we analyse a basic quasi-stationary scenario with two UAVs and four devices, in which the two UAVs hover at optimal displacements to execute the data collection mission, and it is proven that the proposed optimal resource allocation and trajectory solution is adaptively adjustable according to the severity of the interference and that the common throughput of the network is non-decreasing. Second, for the general mobile case, we design an efficient algorithm to jointly address resource allocation and trajectory optimization, in which we first apply the block coordinate descent method to decompose the original non-convex problem into three non-convex sub-problems and then employ a dedicated genetic algorithm, a penalty function and the sequential convex approximation (SCA) technique to efficiently solve the individual sub-problems and obtain a satisfactory locally optimal solution with an adaptive initialization scheme. Subsequently, numerical experiments are presented to demonstrate that the completion time of the data collection task with our proposed method is at least 25% shorter than those with several baseline dynamic orthogonal schemes when 4 UAVs are deployed. Finally, we provide a practical application principle concerning the maximum suitable number of UAVs to avoid the inherent deficiencies of the proposed algorithm.
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作者：
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