Artificial intelligence for satellite communication: A review

被引：60
作者：
Fourati F. [1 ]
Alouini M.-S. [1 ]
机构：
[1] The Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal
来源：
Intelligent and Converged Networks | 2021年 / 2卷 / 03期
关键词：
artificial intelligence; deep learning; machine learning; reinforcement learning; satellite communication; wireless communication;
D O I：
10.23919/ICN.2021.0015
中图分类号：
学科分类号：
摘要：
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. © All articles included in the journal are copyrighted to the ITU and TUP.
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页码：213 / 244
页数：31
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