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ML-based Radio Resource Management in 5G and Beyond Networks: A survey

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dc.contributor.author ΜΠΑΡΤΣΙΩΚΑΣ, Ιωάννης
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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Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα Except where otherwise noted, this item's license is described as Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα