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集群和光束宽度优化用于无人机辅助无线通信.

Weidong Zhao1,2, Jun Zhang1, Dongxing Li1

  • 1College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

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|December 9, 2023
PubMed
概括
此摘要是机器生成的。

这项研究提出了改进的k-means集群和固定点代 (FPI) 算法,以优化无人机 (UAV) 束宽度,以提高无线通信网络的能源效率. 该方法有效地集群随机分布的用户,提高系统性能.

关键词:
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科学领域:

  • 无线通信无线通信
  • 空中网络 空中网络
  • 优化算法 优化算法

背景情况:

  • 无人驾驶飞行器 (UAV) 越来越多地被用于无线通信,作为各种地面和海洋用户的空中基站.
  • 随机用户分布和城市/农村地区的密度差异对基于无人机的通信效率构成挑战.
  • 在复杂的通信场景中,优化无人机能源效率对于可持续和有效的部署至关重要.

研究的目的:

  • 开发一种用于集群地面用户和优化无人机光束宽度的新方法.
  • 提高无人机辅助无线通信系统的能源效率.
  • 分析关键参数对系统性能的影响.

主要方法:

  • 导出下链系统中的无人机的能效表达式.
  • 应用改进的k-means算法,以基于地理数据对分布不均的用户进行集群.
  • 使用固定点代 (FPI) 算法来确定最佳的UAV光束宽度后集群.

主要成果:

  • 拟议的算法在提高无人机辅助无线通信的能源效率方面表现出卓越的性能.
  • 模拟分析验证了聚类和光束宽度优化方法的有效性.
  • 该研究分析了诸如集群数和传输功率等参数对整体系统能效的影响.

结论:

  • 开发的集群和光束宽度优化策略显著提高了无人机的能源效率.
  • 改进的k-means和FPI算法为管理随机分布的用户提供了强大的解决方案.
  • 这项研究为设计高效的无人机无线通信网络提供了宝贵的见解.