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Environment-Aware Deployment of Wireless Drones Base Stations with Google Earth Simulator

机译:使用Google Earth Simulator进行环境感知的无线无人机基站部署

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In this paper, a software-based simulator for the deployment of base station-equipped unmanned aerial vehicles (UAVs) in a cellular network is proposed. To this end, the Google Earth Engine platform and its included image processing functions are used to collect geospatial data and to identify obstacles that can disrupt the line-of-sight (LoS) communications between UAVs and ground users. Given such geographical information, three environment-aware optimal UAV deployment scenarios are investigated using the developed simulator. In the first scenario, the positions of UAVs are optimized such that the number of ground users covered by UAVs is maximized. In the second scenario, the minimum number of UAVs needed to provide full coverage for all ground users is determined. Finally, given the load requirements of the ground users, the total flight time (i.e., energy) that the UAVs need to completely serve the ground users is minimized. Simulation results using a real area of the Virginia Tech campus show that the proposed environment-aware drone deployment framework with Google Earth input significantly enhances the network performance in terms of coverage and energy consumption, compared to classical deployment approaches that do not exploit geographical information. In particular, the results show that the proposed approach yields a coverage enhancement by a factor of 2, and a 65% improvement in energy-efficiency. The results have also shown the existence of an optimal number of drones that leads to a maximum wireless coverage performance.
机译:在本文中,提出了一种基于软件的模拟器,用于在蜂窝网络中部署配备基站的无人飞行器(UAV)。为此,Google Earth Engine平台及其包含的图像处理功能用于收集地理空间数据并识别可能破坏无人机与地面用户之间的视线(LoS)通信的障碍。给定这样的地理信息,使用开发的模拟器研究了三种环境感知的最佳UAV部署方案。在第一种情况下,无人机的位置被优化,以使无人机所覆盖的地面用户数量最大化。在第二种情况下,确定为所有地面用户提供全面覆盖所需的最小数量的无人机。最后,考虑到地面用户的负载需求,将无人机完全为地面用户服务所需的总飞行时间(即能量)降至最低。使用弗吉尼亚理工大学校园的真实区域进行的仿真结果表明,与不利用地理信息的传统部署方法相比,拟议的具有Google Earth输入的环境感知无人机部署框架在覆盖范围和能耗方面显着提高了网络性能。尤其是,结果表明,所提出的方法可将覆盖范围提高2倍,并且能效提高65%。结果还表明,存在最佳数量的无人机,可以带来最大的无线覆盖性能。

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