TY - JOUR
T1 - Machine Learning Meets Communication Networks
T2 - Current Trends and Future Challenges
AU - Ahmad, Ijaz
AU - Shahabuddin, Shahriar
AU - Malik, Hassan
AU - Harjula, Erkki
AU - Leppanen, Teemu
AU - Lovén, Lauri
AU - Anttonen, Antti
AU - Sodhro, Ali Hassan
AU - Alam, Muhammad Mahtab
AU - Juntti, Markku
AU - Ylä-Jääski, Antti
AU - Sauter, Thilo
AU - Gurtov, Andrei
AU - Ylianttila, Mika
AU - Riekki, Jukka
N1 - Project Project 125516
Funding Information: 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.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097366961&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3041765
DO - 10.1109/ACCESS.2020.3041765
M3 - Review Article
VL - 8
SP - 223418
EP - 223460
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9274307
ER -