dc.contributor.author | ΜΠΑΡΤΣΙΩΚΑΣ, Ιωάννης![]() |
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dc.date.accessioned | 2023-10-17T19:15:36Z | |
dc.date.available | 2023-10-17T19:15:36Z | |
dc.identifier.uri | https://dspace.lib.ntua.gr/xmlui/handle/123456789/58197 | |
dc.identifier.uri | http://dx.doi.org/10.26240/heal.ntua.25893 | |
dc.rights | Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nd/3.0/gr/ | * |
dc.subject | 5G | en |
dc.subject | B5G | el |
dc.subject | deep learning | el |
dc.subject | machine learning | el |
dc.subject | mobile edge computing | el |
dc.subject | radio resource management | el |
dc.title | ML-based Radio Resource Management in 5G and Beyond Networks: A survey | el |
heal.type | journalArticle | |
heal.classification | Telecommunications | en |
heal.classification | Machine Learning | el |
heal.classification | Deep Learning | el |
heal.contributorName | Bartsiokas, Ioannis | |
heal.contributorName | Gkonis, Panagiotis | |
heal.contributorName | Kaklamani, Dimitra | |
heal.contributorName | Venieris, Iakovos | |
heal.language | en | |
heal.access | free | |
heal.recordProvider | ntua | el |
heal.publicationDate | 2022-08-05 | |
heal.bibliographicCitation | I. A. Bartsiokas, P. K. Gkonis, D. I. Kaklamani and I. S. Venieris, "ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey," in IEEE Access, vol. 10, pp. 83507-83528, 2022, doi: 10.1109/ACCESS.2022.3196657. | en |
heal.abstract | In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multiple-inputmultiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail. | en |
heal.publisher | IEEE | en |
heal.journalName | IEEE Access | el |
heal.journalType | peer-reviewed | |
heal.fullTextAvailability | false | |
dc.identifier.doi | 10.1109/ACCESS.2022.3196657 | el |
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