为灾后提供灵活的航空回程链路:使用无人机群和分布式充电站的设计

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammad Taghi Dabiri;Mazen Hasna;Nizar Zorba;Tamer Khattab
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引用次数: 0

摘要

在本文中,我们的目标是利用无人机(UAV)中继器和充电站(CS)设计一条永久性的长回程链路,将数据从最近的核心网络传输到灾区(DA)。为此,我们首先通过考虑备用无人机(移动无人机)和在役无人机(悬停无人机)的能耗模型、在役无人机相对于灾区的位置和数量以及 CS 相对于无人机位置的位置来描述通信信道的特征。然后,我们定义了两种不同情况下的优化问题。首先,我们以最小化实施成本的方式设计长回程链路。具体而言,优化设计包括找到 CS 的最佳位置、服役中的 UAV 以及备用 UAV 的最佳规划,从而降低实施成本并保证基于 UAV 的多中继无线回程链路的服务质量。实施成本与 CS 数量、投入使用的无人机数量以及备用无人机数量有关。在第二种情况下,我们假定实施成本是预先确定的,并找出无人机和 CS 的最佳位置以及备用无人机的规划,以最大限度地降低中断概率。通过分析优化参数的影响,我们进一步提出了这两种情况下的低复杂度次优算法。然后,我们通过仿真表明,次优算法的性能非常接近最优解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enabling Flexible Arial Backhaul Links for Post Disasters: A Design Using UAV Swarms and Distributed Charging Stations
In this article, our target is to design a permanent long backhaul link using unmanned aerial vehicle (UAV) relays and charge stations (CSs) to transfer data from the nearest core network to disaster area (DA). To this end, we first characterize the communication channel by considering the energy consumption models of the backup UAVs (moving UAVs) and the UAVs in service (hovering UAVs), the position and number of UAVs in service relative to the DA, along with the position of CSs relative to the position of UAVs. Then we define the optimization problem for two different scenarios. First, we design the long backhaul link in such a way that minimizes the implementation cost. In particular, the optimal design includes finding the optimal position for CSs, UAVs in service along with the optimal planning for backup UAVs in such a way as to reduce the implementation cost and guarantee the quality of service of the multi-relay UAV-based wireless backhaul links. The implementation cost is related to the number of CSs, the number of UAVs in service along with the number of backup UAVs. For the second scenario, we assume that the implementation cost is predetermined, and we find the optimal positions for UAVs and CSs along with planning for backup UAVs to minimize the outage probability. By analyzing the effects of optimization parameters, we further propose low complexity sub-optimal algorithms for both scenarios. Then, using simulations, we show that the sub-optimal algorithms achieve a performance that is very close to the optimal solutions.
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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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