基于三维传播建模与自适应遗传算法的低空通信基站配置优化

周航; 秦亚龙; 龙泽宇; 张盈斐

doi:10.11959/j.issn.1000-0801.2026109

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基于三维传播建模与自适应遗传算法的低空通信基站配置优化

专栏：低空通信 | 更新时间：2026-03-31

- 基于三维传播建模与自适应遗传算法的低空通信基站配置优化
- Optimization of low-altitude communication base station configuration based on three-dimensional propagation modeling and adaptive genetic algorithm
- 电信科学 2026年42卷页码：1-18
- 作者机构：
  
  1.中国民航大学中欧航空工程师学院，天津 300300
  2.郑州航空工业管理学院民航学院，河南郑州 450015
- 作者简介：
  
  [ "周航（1990- ），男，博士，中国民航大学中欧航空工程师学院副教授、硕士生导师，主要研究方向为计算电磁学、低空通信系统。" ]
  [ "秦亚龙（2000- ），男，中国民航大学中欧航空工程师学院硕士生，主要研究方向为通信基站布局优化、计算电磁学。" ]
  [ "龙泽宇（2001- ），男，中国民航大学中欧航空工程师学院硕士生，主要研究方向为无人机组网优化，安全性评估。" ]
  [ "张盈斐（1995- ），女，博士，郑州航空工业管理学院民航学院讲师，主要研究方向为航空安全、智能计算。" ]
- 基金信息：
  
  国家自然科学基金青年科学基金项目(62201577);国家重点研发计划项目课题(2023YFB4302901);中央高校基本科研业务费中国民航大学专项项目(3122020075)
- DOI：10.11959/j.issn.1000-0801.2026109
  中图分类号： TN929.5;V243.1
- 收稿：2025-11-06，
  
  修回：2025-12-10，
  
  录用：2026-01-07，
  
  网络首发：2026-03-30，
  
  纸质出版：2026-04-20
- 稿件说明：
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周航,秦亚龙,龙泽宇等.基于三维传播建模与自适应遗传算法的低空通信基站配置优化[J].电信科学,

Zhou Hang,Qin Yalong,Long Zeyu,et al.Optimization of low-altitude communication base station configuration based on three-dimensional propagation modeling and adaptive genetic algorithm[J].Telecommunications Science,
周航,秦亚龙,龙泽宇等.基于三维传播建模与自适应遗传算法的低空通信基站配置优化[J].电信科学, DOI：10.11959/j.issn.1000−0801.2026109.

Zhou Hang,Qin Yalong,Long Zeyu,et al.Optimization of low-altitude communication base station configuration based on three-dimensional propagation modeling and adaptive genetic algorithm[J].Telecommunications Science, DOI：10.11959/j.issn.1000−0801.2026109.

摘要

为满足低空通信场景中广域覆盖与信号质量均衡的需求，针对城市低空复杂场景，提出一种融合三维传播建模与自适应遗传算法的基站配置优化方法。该方法构建了综合考虑自由空间损耗、多径传播与环境衰减的路径损耗模型，并引入信干噪比开展干扰分析。结合三维地形和离散化需求点建立了覆盖率、信号质量与建设成本的多目标优化框架。为克服传统遗传算法易陷入局部最优的缺陷，采用多段式染色体结构与自适应进化机制改进遗传算法，实现了基站选址与天线参数的联合优化。实验结果表明，该方法在保证成本可控的同时，提升了信号覆盖均衡性和综合性能。在离地200 m的城市低空场景中，改进的遗传算法仅使用8个基站即可100%覆盖目标区域。

Abstract

To satisfy the requirement of wide-area coverage and balanced signal quality in low-altitude communication scenarios

a base station configuration optimization method was proposed for complex urban low-altitude environments

which was combined with three-dimensional propagation modeling and an adaptive genetic algorithm. A path loss model was constructed by comprehensively considering free-space loss

multipath propagation

and environmental attenuation

and the signal to interference plus noise ratio was introduced for interference analysis. A multi-objective optimization framework was established based on three-dimensional terrain and discretized demand points

including coverage rate

signal quality

and construction cost. To overcome the tendency of the traditional genetic algorithm (GA) to fall into local optima

an improved GA with a multi-segment chromosome structure and adaptive evolutionary mechanism was adopted to achieve the joint optimization of base station location and antenna parameters. Experimental results showed that this method improved signal coverage uniformity and overall performance while keeping the cost controllable. In an urban low-altitude scenario at 200 m above the ground

the improved GA could achieve 100% coverage of the target area with only eight base stations.

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references

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