A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions

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dc.authorscopusidDursun Delen / 55887961100
dc.authorwosidDursun Delen / AGA-9892-2022
dc.contributor.authorTalal, Mohammed
dc.contributor.authorGarfan, Salem
dc.contributor.authorQays, Rami
dc.contributor.authorPamucar, Dragan
dc.contributor.authorDelen, Dursun
dc.contributor.authorPedrycz, Witold
dc.contributor.authorAlamleh, Amneh
dc.contributor.authorAlamoodi, Abdullah
dc.contributor.authorZaidan, B.B.
dc.contributor.authorSimic, Vladimir
dc.date.accessioned2025-04-18T08:17:51Z
dc.date.available2025-04-18T08:17:51Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThe fifth-generation (5G) network is considered a game-changing technology that promises advanced connectivity for businesses and growth opportunities. To gain a comprehensive understanding of this research domain, it is essential to scrutinize past research to investigate 5G-radio access network (RAN) architecture components and their interaction with computing tasks. This systematic literature review focuses on articles related to the past decade, specifically on machine learning models integrated with 5G-RAN architecture. The review disregards service types like the Internet of Medical Things, Internet of Things, and others provided by 5G-RAN. The review utilizes major databases such as IEEE Xplore, ScienceDirect, and Web of Science to locate highly cited peer-reviewed studies among 785 articles. After implementing a two-phase article filtration process, 143 articles are categorized into review articles (15/143) and learning-based development articles (128/143) based on the type of machine learning used in development. Motivational topics are highlighted, and recommendations are provided to facilitate and expedite the development of 5G-RAN. This review offers a learning-based mapping, delineating the current state of 5G-RAN architectures (e.g., O-RAN, C-RAN, HCRAN, and F-RAN, among others) in terms of computing capabilities and resource availability. Additionally, the article identifies the current concepts of ML prediction (categorical vs. value) that are implemented and discusses areas for future enhancements regarding the goal of network intelligence. © 2024 Elsevier Ltd
dc.identifier.citationTalal, M., Gerfan, S., Qays, R., Pamucar, D., Delen, D., Pedrycz, W., ... & Simic, V. (2024). A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions. Journal of Network and Computer Applications, 104041.
dc.identifier.doi10.1016/j.jnca.2024.104041
dc.identifier.issn10848045
dc.identifier.scopus2-s2.0-85206250044
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.jnca.2024.104041
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6541
dc.identifier.volume233
dc.indekslendigikaynakScopus
dc.institutionauthorDelen, Dursun
dc.institutionauthoridDursun Delen / 0000-0001-8857-5148
dc.language.isoen
dc.publisherAcademic Press
dc.relation.ispartofJournal of Network and Computer Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subject5G network
dc.subjectMachine Learning
dc.subjectRadio Access Network
dc.titleA comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions
dc.typeOther

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