LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning
Joint Authors
Pi, Zhao
Zhou, Zou
Wang, Kaixuan
Zheng, Fei
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-12
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Telecommunications Engineering
Abstract EN
Delay, cost, and loss are low in Low Earth Orbit (LEO) satellite networks, which play a pivotal role in channel allocation in global mobile communication system.
Due to nonuniform distribution of users, the existing channel allocation schemes cannot adapt to load differences between beams.
On the basis of the satellite resource pool, this paper proposes a network architecture of LEO satellite that utilizes a centralized resource pool and designs a combination allocation of fixed channel preallocation and dynamic channel scheduling.
The dynamic channel scheduling can allocate or recycle free channels according to service requirements.
The Q-Learning algorithm in reinforcement learning meets channel requirements between beams.
Furthermore, the exponential gradient descent and information intensity updating accelerate the convergence speed of the Q-Learning algorithm.
The simulation results show that the proposed scheme improves the system supply-demand ratio by 14%, compared with the fixed channel allocation (FCA) scheme and by 18%, compared with the Lagrange algorithm channel allocation (LACA) scheme.
The results also demonstrate that our allocation scheme can exploit channel resources effectively.
American Psychological Association (APA)
Zheng, Fei& Pi, Zhao& Zhou, Zou& Wang, Kaixuan. 2020. LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1192559
Modern Language Association (MLA)
Zheng, Fei…[et al.]. LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning. Mobile Information Systems No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1192559
American Medical Association (AMA)
Zheng, Fei& Pi, Zhao& Zhou, Zou& Wang, Kaixuan. LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1192559
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192559