面向结构化空域的基站协同传输策略研究

熊凯; 刘瀚阳; 张瀚文; 冷甦鹏

doi:10.11959/j.issn.1000-0801.DXKX260060

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面向结构化空域的基站协同传输策略研究

更新时间：2026-05-19

- 面向结构化空域的基站协同传输策略研究
- Cooperative Transmission Strategy of Base Stations for Structured Airspace Corridors
- 电信科学 2026年
- 作者机构：
  
  电子科技大学信息与通信工程学院，四川省成都市邮编61173
- 作者简介：
  
  [ "刘瀚阳（2003-），男，电子科技大学硕士研究生在读。主要研究方向为无人编队多模态感知识别及组网协议设计。" ]
- 基金信息：
  
  国家自然科学基金青年项目(批准号 62201122);四川省自然科学基金项目(批准号 2026NSFSC1431);国家自然科学基金青年项目(批准号 62501122);航空工业集团飞机智能决策与协同控制机理模型联合技术中心资助，国家自然科学基金重点项目(批准号 62541103);航空工业集团飞机智能决策与协同控制机理模型联合技术中心资助
- DOI：10.11959/j.issn.1000-0801.DXKX260060
  中图分类号：
- 收稿：2026-01-21，
  
  修回：2026-05-18，
  
  录用：2026-05-18，
- 稿件说明：
移动端阅览
熊凯, 刘瀚阳, 张瀚文, 等. 面向结构化空域的基站协同传输策略研究[J/OL]. 电信科学, 2026.

Xiong Kai, Liu Hanyang, Zhang Hanwen, et al. Cooperative Transmission Strategy of Base Stations for Structured Airspace Corridors[J/OL]. Telecommunications Science, 2026.
熊凯, 刘瀚阳, 张瀚文, 等. 面向结构化空域的基站协同传输策略研究[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260060.

Xiong Kai, Liu Hanyang, Zhang Hanwen, et al. Cooperative Transmission Strategy of Base Stations for Structured Airspace Corridors[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260060.

摘要

随着低空经济快速发展，构建面向飞行器协同出行的低空智联网已成为支撑低空活动安全运行的关键所在。然而现有地面基站的部署方案主要针对地面用户，并未考虑到对结构化空域中的飞行器进行有效信号覆盖。此外，低空飞行器的移动性远高于地面用户，对基站的波束赋形以及发射功率调配提出了高实时性要求。本文聚焦于结构化空中航道场景，提出了一种面向分层航道飞行器接入的地面基站部署与协同功率分配优化策略，以最大化网络整体空-地通信效率。本文将优化设计为两个阶段：首先采用启发式搜索算法对基站部署位置进行全局优化，提升基站对结构化空域中分层定向航道的有效覆盖；然后构建基于DeepSets架构的深度强化学习框架，解决高动态场景下的基站功率分配问题，实现具有预测能力的动态波束追踪。仿真结果表明，采用该基站部署方案，信道质量与基准方案相比带来了约9.78%的空地传输速率提升，所采用的强化框架能够稳定快速完成波束追踪收敛，为构建高效空-地通信与低空智联网提供了理论支撑。

Abstract

With the rapid development of the low-altitude economy

constructing a low-altitude intelligent network for aircraft has become a critical foundation for supporting the safe operation of low-altitude activities. However

existing deployment strategies for ground base stations are primarily designed for ground users

resulting in limited airspace coverage. Furthermore

the mobility of low-altitude aircraft is significantly higher than that of ground users

imposing stringent real-time requirements on beamforming and transmission power allocation of base stations. This paper focuses on structured air route scenarios and proposes a collaborative optimization strategy for base station deployment and power allocation to maximize the overall air-ground transmission efficiency of the network. The optimization is divided into two stages: first

a heuristic search algorithm is employed to globally optimize base station deployment positions

enhancing the geometric coverage performance of the network. Subsequently

a deep reinforcement learning framework based on the DeepSets architecture is constructed to address the issue of power allocation in highly dynamic scenarios

enabling three-dimensional dynamic tracking. Simulation results demonstrate that the proposed base station deployment scheme improves channel quality

achieving approximately 9.78% higher air-ground transmission rates compared to benchmark schemes. The reinforcement learning framework converges stably and quickly

providing effective three-dimensional tracking of aircraft users. This study offers theoretical support and practical guidance for building an efficient air-ground integrated low-altitude communication network.

关键词

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references

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