Optimization for LEO Satellite-Ground Integrated Networks via Deep Reinforcement Learning

Ju Hyung Lee; Young Chai Ko

doi:10.1109/ICTC52510.2021.9621058

Optimization for LEO Satellite-Ground Integrated Networks via Deep Reinforcement Learning

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Witnessing an emerging communication platform of low-altitude earth orbit (LEO) satellites (SATs), integrating LEO SAT networks and existing terrestrial networks is getting attention. In this paper, we study the optimization of space-ground-integrated networks. Towards maximizing the communication efficiency, using a ground terminal as a relay terminal for the orbiting LEO SAT networks should be considered and optimized; however, that is tricky due to the time-varying network topology and a huge number of possible control actions. To tackle the challenge, a deep reinforcement learning method is used. Simulation results validate that SAT-ground relay (GR)-integrated scheme achieves 2.9x higher end-to-end sum throughput compared to a benchmark scheme with only-SAT scheme.

Original language	English
Title of host publication	ICTC 2021 - 12th International Conference on ICT Convergence
Subtitle of host publication	Beyond the Pandemic Era with ICT Convergence Innovation
Publisher	IEEE Computer Society
Pages	1758-1762
Number of pages	5
ISBN (Electronic)	9781665423830
DOIs	https://doi.org/10.1109/ICTC52510.2021.9621058
Publication status	Published - 2021
Event	12th International Conference on Information and Communication Technology Convergence, ICTC 2021 - Jeju Island, Korea, Republic of Duration: 2021 Oct 20 → 2021 Oct 22

Publication series

Name	International Conference on ICT Convergence
Volume	2021-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	12th International Conference on Information and Communication Technology Convergence, ICTC 2021
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/10/20 → 21/10/22

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICTC52510.2021.9621058

Cite this

Lee, J. H., & Ko, Y. C. (2021). Optimization for LEO Satellite-Ground Integrated Networks via Deep Reinforcement Learning. In ICTC 2021 - 12th International Conference on ICT Convergence: Beyond the Pandemic Era with ICT Convergence Innovation (pp. 1758-1762). (International Conference on ICT Convergence; Vol. 2021-October). IEEE Computer Society. https://doi.org/10.1109/ICTC52510.2021.9621058

Optimization for LEO Satellite-Ground Integrated Networks via Deep Reinforcement Learning. / Lee, Ju Hyung; Ko, Young Chai.
ICTC 2021 - 12th International Conference on ICT Convergence: Beyond the Pandemic Era with ICT Convergence Innovation. IEEE Computer Society, 2021. p. 1758-1762 (International Conference on ICT Convergence; Vol. 2021-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, JH & Ko, YC 2021, Optimization for LEO Satellite-Ground Integrated Networks via Deep Reinforcement Learning. in ICTC 2021 - 12th International Conference on ICT Convergence: Beyond the Pandemic Era with ICT Convergence Innovation. International Conference on ICT Convergence, vol. 2021-October, IEEE Computer Society, pp. 1758-1762, 12th International Conference on Information and Communication Technology Convergence, ICTC 2021, Jeju Island, Korea, Republic of, 21/10/20. https://doi.org/10.1109/ICTC52510.2021.9621058

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title = "Optimization for LEO Satellite-Ground Integrated Networks via Deep Reinforcement Learning",

abstract = "Witnessing an emerging communication platform of low-altitude earth orbit (LEO) satellites (SATs), integrating LEO SAT networks and existing terrestrial networks is getting attention. In this paper, we study the optimization of space-ground-integrated networks. Towards maximizing the communication efficiency, using a ground terminal as a relay terminal for the orbiting LEO SAT networks should be considered and optimized; however, that is tricky due to the time-varying network topology and a huge number of possible control actions. To tackle the challenge, a deep reinforcement learning method is used. Simulation results validate that SAT-ground relay (GR)-integrated scheme achieves 2.9x higher end-to-end sum throughput compared to a benchmark scheme with only-SAT scheme. ",

author = "Lee, {Ju Hyung} and Ko, {Young Chai}",

note = "Funding Information: ACKNOWLEDGEMENT This work was supported by the ICT R&D program of MSIT/IITP. [2021-0-01810, Development of elemental technologies for Ultra-secure Quantum Internet] Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th International Conference on Information and Communication Technology Convergence, ICTC 2021 ; Conference date: 20-10-2021 Through 22-10-2021",

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N2 - Witnessing an emerging communication platform of low-altitude earth orbit (LEO) satellites (SATs), integrating LEO SAT networks and existing terrestrial networks is getting attention. In this paper, we study the optimization of space-ground-integrated networks. Towards maximizing the communication efficiency, using a ground terminal as a relay terminal for the orbiting LEO SAT networks should be considered and optimized; however, that is tricky due to the time-varying network topology and a huge number of possible control actions. To tackle the challenge, a deep reinforcement learning method is used. Simulation results validate that SAT-ground relay (GR)-integrated scheme achieves 2.9x higher end-to-end sum throughput compared to a benchmark scheme with only-SAT scheme.

AB - Witnessing an emerging communication platform of low-altitude earth orbit (LEO) satellites (SATs), integrating LEO SAT networks and existing terrestrial networks is getting attention. In this paper, we study the optimization of space-ground-integrated networks. Towards maximizing the communication efficiency, using a ground terminal as a relay terminal for the orbiting LEO SAT networks should be considered and optimized; however, that is tricky due to the time-varying network topology and a huge number of possible control actions. To tackle the challenge, a deep reinforcement learning method is used. Simulation results validate that SAT-ground relay (GR)-integrated scheme achieves 2.9x higher end-to-end sum throughput compared to a benchmark scheme with only-SAT scheme.

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Optimization for LEO Satellite-Ground Integrated Networks via Deep Reinforcement Learning

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