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dc.contributor.authorFourati, Fares
dc.contributor.authorAlouini, Mohamed-Slim
dc.date.accessioned2021-02-01T06:41:32Z
dc.date.available2021-02-01T06:41:32Z
dc.date.issued2021-01-25
dc.identifier.urihttp://hdl.handle.net/10754/667135
dc.description.abstractSatellite 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 have 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.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2101.10899
dc.rightsArchived with thanks to arXiv
dc.subjectSatellite Communication
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectReinforcement Learning
dc.titleArtificial Intelligence for Satellite Communication: A Review
dc.typePreprint
dc.contributor.departmentCommunication Theory Lab
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentKing Abdullah University of Science and Technology (KAUST), CEMSE Division, Thuwal, 23955- 6900 KSA
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.eprint.versionPre-print
dc.identifier.arxivid2101.10899
kaust.personFourati, Fares
kaust.personAlouini, Mohamed-Slim
refterms.dateFOA2021-02-01T06:42:12Z


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