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

ΜΠΑΡΤΣΙΩΚΑΣ, Ιωάννης

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