Graph Learning Based Transmission Resources Scheduling in Dense Space Networks

LIU Run-zi; WU Wei-hua; ZHANG Wen-zhu; ZHOU Di; ZHANG Yan

doi:10.12263/DZXB.20201217

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Graph Learning Based Transmission Resources Scheduling in Dense Space Networks

PAPERS | 更新时间：2025-12-08

- Graph Learning Based Transmission Resources Scheduling in Dense Space Networks
- ACTA ELECTRONICA SINICA Vol. 49, Issue 11, Pages: 2133-2137(2021)
- 作者机构：
  
  1.西安建筑科技大学信息与控制工程学院，陕西西安 710055
  2.西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201217
  CLC： TN919;TN927
- Received：30 October 2020，
  
  Revised：2021-07-08，
  
  Published：25 November 2021
- 稿件说明：
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刘润滋,吴伟华,张文柱等.基于图学习的密集空间网络传输资源调度方法[J].电子学报,2021,49(11):2133-2137.

LIU Run-zi,WU Wei-hua,ZHANG Wen-zhu,et al.Graph Learning Based Transmission Resources Scheduling in Dense Space Networks[J].ACTA ELECTRONICA SINICA,2021,49(11):2133-2137.
刘润滋,吴伟华,张文柱等.基于图学习的密集空间网络传输资源调度方法[J].电子学报,2021,49(11):2133-2137. DOI： 10.12263/DZXB.20201217.

LIU Run-zi,WU Wei-hua,ZHANG Wen-zhu,et al.Graph Learning Based Transmission Resources Scheduling in Dense Space Networks[J].ACTA ELECTRONICA SINICA,2021,49(11):2133-2137. DOI： 10.12263/DZXB.20201217.

摘要

面对密集空间网络传输资源调度问题中的复杂度以及有效性挑战，本文以图论模型为纽带，将传统数学模型与机器学习方法相结合，提出一种基于图学习的密集空间网络传输资源调度方法.该方法基于图论模型对问题结构的认知将密集空间网络资源调度问题分解，由数学模型与基于图结构的强化学习交替配合完成整个求解过程.实验结果表明，与传统的基于数学模型的资源调度方法相比，所提方法能将资源调度收益提升25.1%，且其训练结果对网络场景变化具有较好的适应性.

Abstract

Facing the challenges of the transmission resource schedule of dense space networks

we combine the mathematical models and machine learning methods

and propose a graph learning based approach for the scheduling of transmission resources in dense space networks. In the proposed method

transmission resource scheduling problem is decomposed based on the knowledge of the problem structure brought by graph theory. On this basis

mathematical model and reinforcement learning alternately complete the whole solution process. Simulation results show that

compared with the traditional mathematical model-based methods

the proposed method improves the scheduling profits by 25.1%

and its training results have better generality.

关键词

Keywords

references

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