Machine learning meets communication networks: Current trends and future challenges

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Ahmad, Ijaz, Shahabuddin, Shariar, Malik, Hassan, Harjula, Erkki, Leppanen, Teemu, Loven, Lauri, Anttonen, Antti, Sodhro, Ali Hassan, Mahtab Alam, Muhammad, Juntti, Markku, Yla-Jaaski, Antti, Sauter, Thilo, Gurtov, Andrei, Ylianttila, Mika and Riekki, Jukka (2020) Machine learning meets communication networks: Current trends and future challenges. IEEE Access, 8. pp. 223418-223460. ISSN 2169-3536

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Abstract

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.

Item Type:	Article
Uncontrolled Keywords:	artificial intelligence (ai),communication networks,mac layer,machine learning,mec,network layer,nfv,physical layer,sdn,security,computer science(all),materials science(all),engineering(all) ,/dk/atira/pure/subjectarea/asjc/1700
Faculty \ School:	Faculty of Science > School of Computing Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	02 Jul 2025 10:30
Last Modified:	14 Jul 2025 12:32
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99804
DOI:	10.1109/ACCESS.2020.3041765

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