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Intelligent and Converged Networks 2021, 2(3): 213-243 https://doi.org/10.23919/ICN.2021.0015
Open Access | Issue | Published: 01 September 2021

Artificial intelligence for satellite communication: A review

Show Author's Information Fares Fourati1,  Mohamed-Slim Alouini1( )
Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
Keywords:
artificial intelligence, deep learning, machine learning, reinforcement learning, satellite communication, wireless communication
Cite this article:
Fourati F, Alouini M-S. Artificial intelligence for satellite communication: A review. Intelligent and Converged Networks, 2021, 2(3): 213-243. https://doi.org/10.23919/ICN.2021.0015
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Abstract Full text About this article

Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects has demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.


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About this article

Artificial intelligence for satellite communication: A review

Show Author's information Fares Fourati1, Mohamed-Slim Alouini1( )
Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

Abstract

Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects has demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

Keywords: artificial intelligence, deep learning, machine learning, reinforcement learning, satellite communication, wireless communication

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Received: 25 January 2021
Revised: 01 June 2021
Accepted: 12 July 2021
Published: 01 September 2021
Issue date: September 2021

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