Machine learning meets communication networks : current trends and future challenges
|Author:||Ahmad, Ijaz1; Shahabuddin, Shariar2; Malik, Hassan3;|
1VTT Technical Research Centre of Finland, 02044 Espoo, Finland
2Nokia, 02610 Espoo, Finland
3Computer Science Department, Edge Hill University, Ormskirk L39 4QP, U.K.
4Centre for Wireless Communications, University of Oulu, 90570 Oulu, Finland
5Center for Ubiquitous Computing, University of Oulu, 90570 Oulu, Finland
6Department of Computer and System Science, Mid-Sweden University, Östersund, Sweden
7Thomas Johann Seebeck Department of Electronics, Tallinn University of Technology, 12616 Tallinn, Estonia
8Department of Computer Science, Aalto University, 02150 Espoo, Finland
9Institute of Computer Technology, TU Wien, 1040 Wien, Austria
10Department for Integrated Sensor Systems, Danube University Krems, 2700 Wiener Neustadt, Austria
11Department of Computer and Information Science, Linköping University, 58183 Linköping, Sweden
|Online Access:||PDF Full Text (PDF, 5.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202101252580
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-01-25
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.
|Pages:||223418 - 223460|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© The Authors 2020. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.