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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2021

19.08.2020 | Original Article

A survey of 5G network systems: challenges and machine learning approaches

verfasst von: Hasna Fourati, Rihab Maaloul, Lamia Chaari

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2021

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Abstract

5G cellular networks are expected to be the key infrastructure to deliver the emerging services. These services bring new requirements and challenges that obstruct the desired goal of forthcoming networks. Mobile operators are rethinking their network design to provide more flexible, dynamic, cost-effective and intelligent solutions. This paper starts with describing the background of the 5G wireless networks then we give a deep insight into a set of 5G challenges and research opportunities for machine learning (ML) techniques to manage these challenges. The first part of the paper is devoted to overview the fifth-generation of cellular networks, explaining its requirements as well as its key technologies, their challenges and its forthcoming architecture. The second part is devoted to present a basic overview of ML techniques that are nowadays applied to cellular networks. The last part discusses the most important related works which propose ML solutions in order to overcome 5G challenges.
Vorheriger Artikel CL-MAX: a clustering-based approximation algorithm for mining maximal frequent itemsets
Nächster Artikel Fuzzy entropies for class-specific and classification-based attribute reducts in three-way probabilistic rough set models

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1
A backhaul network is an intermediary that enables the data transmission and reception between core networks, or macro base station and small base stations. It can be a wired or wireless link.
 
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Metadaten
Titel
A survey of 5G network systems: challenges and machine learning approaches
verfasst von
Hasna Fourati
Rihab Maaloul
Lamia Chaari
Publikationsdatum
19.08.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2021
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01178-4

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