Machine Learning Meets Communication Networks: Current Trends and Future Challenges

I. Ahmad*, S. Shahabuddin, H. Malik, E. Harjula, Teemu Leppänen, L. Lovén, Antti Anttonen, A. H. Sodhro, M. Mahtab Alam, M. Juntti, A. Ylä-Jääski, T. Sauter, A. Gurtov, Mika Ylianttila, Jukka Riekki

*Corresponding author for this work

Research output: Contribution to journalReview Articlepeer-review

4 Citations (Scopus)
14 Downloads (Pure)

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.

Original languageEnglish
Article number9274307
Pages (from-to)223418-223460
Number of pages43
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
MoE publication typeA2 Review article in a scientific journal

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