Machine Learning Meets Communication Networks: Current Trends and Future Challenges

Ijaz Ahmad (Corresponding Author), Shahriar Shahabuddin, Hassan Malik, Erkki Harjula, Teemu Leppanen, Lauri Lovén, Antti Anttonen, Ali Hassan Sodhro, Muhammad Mahtab Alam, Markku Juntti, Antti Ylä-Jääski, Thilo Sauter, Andrei Gurtov, Mika Ylianttila, Jukka Riekki

Research output: Contribution to journalArticleScientificpeer-review

57 Citations (Scopus)


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
JournalIEEE Access
Early online date1 Dec 2020
Publication statusPublished - 2020
MoE publication typeA1 Journal article-refereed


This work was supported in part by the Business Finland (formerly Tekes) and Academy of Finland through the projects: 6Genesis Flagship project (grant number 318927). The work of Andrei Gurtov was supported by the Center for Industrial Information Technology (CENIIT). The work of Ijaz Ahmad was supported by the Jorma Ollila Grant.


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