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

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliReview Articlevertaisarvioitu

4 Sitaatiot (Scopus)
14 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli9274307
Sivut223418-223460
Sivumäärä43
JulkaisuIEEE Access
Vuosikerta8
DOI - pysyväislinkit
TilaJulkaistu - 2020
OKM-julkaisutyyppiA2 Arvio tiedejulkaisuussa (artikkeli)

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