Unmanned Aerial Vehicle Base Stations (UAV-BSs) are effective to support mobile wireless systems in situations where an unusually high density of users require enhanced coverage and capacity such as in large sport events or music festivals. However, the joint deployment problem of UAV-BSs is NP-hard, and the optimization methods used to solve such a class of problems are often too slow for (quasi-)real-time applications when the density of users and the number of UAV-BS are high. This inefficiency creates a need for more adaptable methods to solve the UAV-BS positioning problem. This paper proposes a solution by transforming the UAV-BSs’ placement problem into a decision-making process using the Analytic Hierarchy Process (AHP) method. Our solution considers that all UAV-BSs move along scanning points with a predetermined path, each with a minimal distance from one another. The proposed algorithm, named UAV-AHP, efficiently determines the UAV-BSs’ positions, thereby improving the network performance based on (quasi-) real-time acquisition of user signals at scanning points. For the high-density scenarios under investigation, our numerical results demonstrate that UAV-AHP outperforms commonly used heuristics that sub-optimally solve NP-hard problems, namely, cuckoo search (CS), particle swarm optimization (PSO), and a genetic algorithm (NSGA-II). The proposed method for UAV-BS deployment requires considerably lower running times to find satisfactory solutions than CS, PSO, and NSGA-II.

中文翻译：

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利用多准则决策的临时网络无人机动态部署

无人机基站（UAV-BS）在用户密度异常高、需要更大覆盖和容量的场景（如大型体育赛事或音乐节）中，能够有效支持移动无线系统。然而，UAV-BS 的联合部署问题属于 NP 难题，解决此类问题的优化方法往往对（准）实时应用来说过慢，尤其是在用户密度和 UAV-BS 数量较多的情况下。这种低效促使人们需要更灵活的方法来解决 UAV-BS 定位问题。本文提出了一种解决方案，通过将 UAV-BS 的布置问题转化为利用分析层级过程（AHP）方法的决策过程。我们的解决方案认为所有 UAV-BS 都沿着预定路径的扫描点移动，每个点之间的距离最小。该算法名为 UAV-AHP，能够高效确定 UAV-BS 的位置，从而基于扫描点用户信号的（准）实时获取，从而提升网络性能。对于正在研究的高密度场景，我们的数值结果表明，UAV-AHP 的表现优于常用的次优解 NP 难问题的启发式方法，即布谷鸟搜索（CS）、粒子群优化（PSO）和遗传算法（NSGA-II）。拟议的 UAV-BS 部署方法相比 CS、PSO 和 NSGA-II 需要更短的运行时间才能找到令人满意的解。