A multipath routing algorithm for satellite networksbased on service demand and traffic awareness

Ziyang XING; Hui QI; Xiaoqiang DI; Jinyao LIU; Rui XU; Jing CHEN; Ligang CONG

doi:10.1631/FITEE.2200507

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A multipath routing algorithm for satellite networksbased on service demand and traffic awareness

Regular Papers | Updated：2023-07-03

- A multipath routing algorithm for satellite networksbased on service demand and traffic awareness
- 一种基于业务需求与流量感知的卫星网络多路径路由算法
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 6, Pages: 844-858(2023)
- Affiliations：
  
  1.Jilin Key Laboratory of Network and Information Security, Changchun 130022, China
  2.School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
  3.Information Center, Changchun University of Science and Technology, Changchun 130022, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2200507
  CLC： TP393
- Received：25 October 2022，
  
  Accepted：27 March 2023，
  
  Published：0 June 2023
- Accepted：
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Ziyang XING, Hui QI, Xiaoqiang DI, et al. A multipath routing algorithm for satellite networksbased on service demand and traffic awareness[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 844-858.
DOI：

Ziyang XING, Hui QI, Xiaoqiang DI, et al. A multipath routing algorithm for satellite networksbased on service demand and traffic awareness[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 844-858. DOI： 10.1631/FITEE.2200507.

摘要

随着低轨卫星制造和发射成本的降低，以及其覆盖范围大、数据传输速率高等优点，低轨卫星已成为空地网络数据传输的重要组成部分。但受地理位置及人们生活习惯等因素影响，用户对数据需求差异会造成网络流量不均衡，可能导致网络拥塞进而影响数据传输。传统卫星网络获取网络信息收敛慢，无法细粒度收集全局网络信息，不利于计算最优路由。多业务请求无法满足服务质量要求。本文将人工智能技术应用于低轨卫星网络，利用软件定义网络获取全局网络信息，感知网络流量，通过强化学习在线制定综合决策，实时更新最优路由策略。仿真结果表明，所提强化学习算法有良好收敛性和较强泛化能力。与传统路由相比，本文算法吞吐量提高了8%，且具有负载均衡性。

Abstract

With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates

satellites have become an important part of data transmission in air-ground networks. However

due to the factors such as geographical location and people’s living habits

the differences in user’ demand for multimedia data will result in unbalanced network traffic

which may lead to network congestion and affect data transmission. In addition

in traditional satellite network transmission

the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner

which is not conducive to calculating optimal routes. The service quality requirements cannot be satisfied when multiple service requests are made. Based on the above

in this paper artificial intelligence technology is applied to the satellite network

and a software-defined network is used to obtain the global network information

perceive network traffic

develop comprehensive decisions online through reinforcement learning

and update the optimal routing strategy in real time. Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability. Compared with traditional routing

the throughput is 8% higher

and the proposed method has load balancing characteristics.

关键词

Keywords

references

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