基于多智能体深度强化学习的低轨星座跳波束资源调度研究

张晨; 徐阳威; 李宛静; 王威; 张更新

doi:10.11959/j.issn.1000-436x.2025009

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基于多智能体深度强化学习的低轨星座跳波束资源调度研究

学术论文 | 更新时间：2025-02-13

- 基于多智能体深度强化学习的低轨星座跳波束资源调度研究
- Research on low earth orbit constellation beam hopping resource scheduling based on multi-agent deep reinforcement learning
- 通信学报 2025年46卷第1期页码：35-51
- 作者机构：
  
  1.南京邮电大学通信与信息工程学院，江苏南京 210003
  2.南京邮电大学通信与网络技术国家工程研究中心，江苏南京 210003
- 作者简介：
  
  [ "张晨（1985- ），男，安徽淮南人，博士，南京邮电大学副研究员、高级工程师、硕士生导师，主要研究方向为天地一体化信息网络、卫星通信新体制。" ]
  [ "徐阳威（1998- ），男，河南周口人，南京邮电大学硕士生，主要研究方向为卫星通信。" ]
  [ "李宛静（2001- ），女，浙江台州人，南京邮电大学博士生，主要研究方向为卫星通信。" ]
  [ "王威（2000- ），男，江苏宿迁人，南京邮电大学硕士生，主要研究方向为卫星通信。" ]
  [ "张更新（1967- ），男，浙江平湖人，博士，南京邮电大学教授、博士生导师，主要研究方向为空间信息网络、卫星通信、深空通信、物联网、频谱监测。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2022YFB2902600)
- DOI：10.11959/j.issn.1000-436x.2025009
  中图分类号： TN927
- 收稿：2024-08-01，
  
  修回：2024-12-12，
  
  纸质出版：2025-01-25
- 稿件说明：
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张晨,徐阳威,李宛静等.基于多智能体深度强化学习的低轨星座跳波束资源调度研究[J].通信学报,2025,46(01):35-51.

ZHANG Chen,XU Yangwei,LI Wanjing,et al.Research on low earth orbit constellation beam hopping resource scheduling based on multi-agent deep reinforcement learning[J].Journal on Communications,2025,46(01):35-51.
张晨,徐阳威,李宛静等.基于多智能体深度强化学习的低轨星座跳波束资源调度研究[J].通信学报,2025,46(01):35-51. DOI： 10.11959/j.issn.1000-436x.2025009.

ZHANG Chen,XU Yangwei,LI Wanjing,et al.Research on low earth orbit constellation beam hopping resource scheduling based on multi-agent deep reinforcement learning[J].Journal on Communications,2025,46(01):35-51. DOI： 10.11959/j.issn.1000-436x.2025009.

摘要

针对低轨星座跳波束资源调度的需求，提出一种基于多智能体深度强化学习的低轨星座跳波束资源调度方法。通过多目标优化的选星接入方式，建立卫星与服务区域之间的映射关系。在此基础上，根据业务类型和QoS需求的多样性，采用混合专家模型的方法，构建一个资源调度多智能体，用于星上资源与跳波束图案的实时决策调度。仿真结果表明，与传统方法相比，所提资源调度方法不仅能满足不同业务对时延和吞吐量的性能需求，还能有效平衡算法的复杂度，适应多样化业务的融合传输需求，应对业务流量的时空分布不均和动态变化，具有较强的泛化能力。

Abstract

A low earth orbit constellation beam hopping resource scheduling method based on multi-agent deep reinforcement learning was proposed to meet the requirements of low earth orbit constellation beam hopping resource scheduling. The mapping relationship between the satellite and the service area was established by optimizing the access of multi-target satellite selection. On this basis

according to the diversity of service types and QoS requirements

based on the concept of mixture of experts

a resource scheduling multi-agent was constructed to carry out real-time decision scheduling of on-board resources and beam hopping patterns. The simulation results show that compared with the traditional methods

the proposed resource scheduling method can not only meet the performance requirements of different services on delay and throughput

but also effectively balance the algorithm complexity. At the same time

the algorithm can adapt to the converged transmission requirements of diversified services

cope with the uneven spatiotemporal distribution and dynamic changes of traffic and have strong generalization ability.

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references

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