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Article: Artificial Intelligence Aided Next-Generation Networks Relying on UAVs

TitleArtificial Intelligence Aided Next-Generation Networks Relying on UAVs
Authors
Issue Date2021
Citation
IEEE Wireless Communications, 2021, v. 28, n. 1, p. 120-127 How to Cite?
AbstractIn this article, we propose artificial intelligence (AI) enabled unmanned aerial vehicle (UAV) aided wireless networks (UAWN) for overcoming the challenges imposed by the random fluctuation of wireless channels, blocking and user mobility effects. In UAWN, multiple UAVs are employed as aerial base stations, which are capable of promptly adapting to the randomly fluctuating environment by collecting information about the users' position and tele-traffic demands, learning from the environment and acting upon the satisfaction level feedback received from the users. Moreover, AI enables the interaction among a swarm of UAVs for cooperative optimization of the system. As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity. As a further benefit, dynamic trajectory design and resource allocation are demonstrated. Finally, potential research challenges and opportunities are discussed.
Persistent Identifierhttp://hdl.handle.net/10722/349496
ISSN
2023 Impact Factor: 10.9
2023 SCImago Journal Rankings: 5.926

 

DC FieldValueLanguage
dc.contributor.authorLiu, Xiao-
dc.contributor.authorChen, Mingzhe-
dc.contributor.authorLiu, Yuanwei-
dc.contributor.authorChen, Yue-
dc.contributor.authorCui, Shuguang-
dc.contributor.authorHanzo, Lajos-
dc.date.accessioned2024-10-17T06:58:55Z-
dc.date.available2024-10-17T06:58:55Z-
dc.date.issued2021-
dc.identifier.citationIEEE Wireless Communications, 2021, v. 28, n. 1, p. 120-127-
dc.identifier.issn1536-1284-
dc.identifier.urihttp://hdl.handle.net/10722/349496-
dc.description.abstractIn this article, we propose artificial intelligence (AI) enabled unmanned aerial vehicle (UAV) aided wireless networks (UAWN) for overcoming the challenges imposed by the random fluctuation of wireless channels, blocking and user mobility effects. In UAWN, multiple UAVs are employed as aerial base stations, which are capable of promptly adapting to the randomly fluctuating environment by collecting information about the users' position and tele-traffic demands, learning from the environment and acting upon the satisfaction level feedback received from the users. Moreover, AI enables the interaction among a swarm of UAVs for cooperative optimization of the system. As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity. As a further benefit, dynamic trajectory design and resource allocation are demonstrated. Finally, potential research challenges and opportunities are discussed.-
dc.languageeng-
dc.relation.ispartofIEEE Wireless Communications-
dc.titleArtificial Intelligence Aided Next-Generation Networks Relying on UAVs-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/MWC.001.2000174-
dc.identifier.scopuseid_2-s2.0-85097149772-
dc.identifier.volume28-
dc.identifier.issue1-
dc.identifier.spage120-
dc.identifier.epage127-
dc.identifier.eissn1558-0687-

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