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Cited by 60Satellite Communication SystemsOpportunistic and Delay-Tolerant NetworksSpace Satellite Systems and Control

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Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

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Na, Zhenyu, Pan, Zheng, Liu, Xin, Deng, Zhian, Gao, Zihe, Guo, Qing·2018·Wireless Communications and Mobile Computing
DOI 10.1155/2018/3026405
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Abstract

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space‐terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time‐varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

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Research works printed but not linked: 34

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Metadata

Authors
Na, Z., Pan, Z., Liu, X., Deng, Z., Gao, Z., & Guo, Q.
Affiliations
  • Dalian Maritime University (CN)
  • Dalian University of Technology (CN)
  • Harbin Engineering University (CN)
  • Harbin Institute of Technology (CN)
Corresponding author
Liu, Xin
ORCID
Venue
Wireless Communications and Mobile Computing
Publisher
Wiley
Published
2018-01
Volume
2018
Issue
1
ISSN
  • 1530-8669 (ISSN-L)
  • 1530-8677
DOI
10.1155/2018/3026405
OpenAlex
W2807954440
Cited by
60
References
0
Citations by year
2026: 2 · 2025: 9 · 2024: 13 · 2023: 9 · 2022: 11 · 2021: 12 · 2020: 3 · 2019: 1
FWCI
3.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Wireless Communications and Mobile Computing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Natural Science Foundation of China (61301131, 61601221)
  • Natural Science Foundation of Jiangsu Province (BK20140828)
  • China Postdoctoral Science Foundation (2015M580425)
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Satellite Communication Systems
  • Physical Sciences › Computer Science › Computer Networks and Communications › Opportunistic and Delay-Tolerant Networks
  • Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
Keywords
LEO satellite networks, distributed routing, extreme learning machine, traffic prediction, mobile agents, time-varying topology, space-terrestrial integrated networks, ant colony optimization comparison, link utilization, delay reduction

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CoChat. (2026). Citation check: Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network. https://app.cochat.ai/papers/10.1155/2018/3026405
The paper
Na, Z., Pan, Z., Liu, X., Deng, Z., Gao, Z., & Guo, Q. (2018). Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2018/3026405