无人机网络部署性能研究
首发时间:2021-03-12
摘要:近年来,随着无人机技术的不断进步,全球无人机网络部署性能研究无人机行业快速发展,使无人机搭载基站升空为目标地区提供网络覆盖成为一种可能。由于影响无人机无线网络性能的因素较多、成因复杂,很难通过传统方法进行分析研究。本文通过搭建系统级仿真平台,从系统级角度评估相关参数如天线倾角对目标区域网络性能的影响。通过多基站部署算法给出一定场景下最优空中基站部署位置和无人机数量,对无人机空中基站的网络部署具有参考意义。仿真结果表明在单基站下,随着天线倾角的增加,网络容量先增大后减小,对于小区吞吐量这一指标来说相差可达大约78%;多基站最优部署下,随之无人机数量的增加,网络容量先增大后趋于稳定,且本文结合天线指向的K-means++算法相较于传统K-means算法能够提高25%。
关键词: 无人机基站 系统级仿真 无人机部署 K-means
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Research on UAV Network Deployment Performance
Abstract:In recent years, with the continuous advancement of UAV technology, the global UAV network deployment performance research UAV industry has developed rapidly, making it possible for UAVs to carry base stations to provide network coverage for target areas. Due to the many factors that affect the performance of the UAV wireless network and the complicated causes, it is difficult to conduct analysis and research through traditional methods. In this paper, by building a system-level simulation platform, from a system-level perspective, the impact of related parameters such as antenna tilt on the network performance in the target area is evaluated. The multi-base station deployment algorithm gives the optimal aerial base station deployment position and the number of drones in a certain scenario, which is of reference significance for the network deployment of drone aerial base stations. The simulation results show that under a single base station, with the increase of the antenna inclination, the network capacity first increases and then decreases. For the cell throughput index, the difference can reach about 78%; under the optimal deployment of multiple base stations, there will be no With the increase in the number of humans and machines, the network capacity first increases and then tends to be stable, and the K-means++ algorithm combined with antenna pointing in this paper can increase by 25% compared to the traditional K-means algorithm.
Keywords: UAV base station System level simulation Drone deployment K-means
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