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CLC number: TP393

On-line Access: 2023-07-03

Received: 2022-10-25

Revision Accepted: 2023-07-03

Crosschecked: 2023-03-27

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Xiaoqiang DI


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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.6 P.844-858


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

Author(s):  Ziyang XING, Hui QI, Xiaoqiang DI, Jinyao LIU, Rui XU, Jing CHEN, Ligang CONG

Affiliation(s):  Jilin Key Laboratory of Network and Information Security, Changchun 130022, China; more

Corresponding email(s):   dixiaoqiang@cust.edu.cn

Key Words:  Software-defined network (SDN), Quick user datagram protocol Internet connection (QUIC), Reinforcement learning, Sketch, Multi-service demand, Satellite network

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

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author="Ziyang XING, Hui QI, Xiaoqiang DI, Jinyao LIU, Rui XU, Jing CHEN, Ligang CONG",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T A multipath routing algorithm for satellite networksbased on service demand and traffic awareness
%A Ziyang XING
%A Hui QI
%A Xiaoqiang DI
%A Jinyao LIU
%A Rui XU
%A Jing CHEN
%A Ligang CONG
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 6
%P 844-858
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200507

T1 - A multipath routing algorithm for satellite networksbased on service demand and traffic awareness
A1 - Ziyang XING
A1 - Hui QI
A1 - Xiaoqiang DI
A1 - Jinyao LIU
A1 - Rui XU
A1 - Jing CHEN
A1 - Ligang CONG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 6
SP - 844
EP - 858
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200507

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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