Machine Learning Meets Communication Networks: Current Trends and Future Challenges
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 te...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9274307/ |
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| author | Ijaz Ahmad Shariar Shahabuddin Hassan Malik Erkki Harjula Teemu Leppanen Lauri Loven Antti Anttonen Ali Hassan Sodhro Muhammad Mahtab Alam Markku Juntti Antti Yla-Jaaski Thilo Sauter Andrei Gurtov Mika Ylianttila Jukka Riekki |
| author_facet | Ijaz Ahmad Shariar Shahabuddin Hassan Malik Erkki Harjula Teemu Leppanen Lauri Loven Antti Anttonen Ali Hassan Sodhro Muhammad Mahtab Alam Markku Juntti Antti Yla-Jaaski Thilo Sauter Andrei Gurtov Mika Ylianttila Jukka Riekki |
| author_sort | Ijaz Ahmad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b8ac991212e542e99c213cae584c4baa |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b8ac991212e542e99c213cae584c4baa2025-08-20T02:16:49ZengIEEEIEEE Access2169-35362020-01-01822341822346010.1109/ACCESS.2020.30417659274307Machine Learning Meets Communication Networks: Current Trends and Future ChallengesIjaz Ahmad0https://orcid.org/0000-0003-1101-8698Shariar Shahabuddin1https://orcid.org/0000-0002-7006-0928Hassan Malik2https://orcid.org/0000-0002-8564-3683Erkki Harjula3https://orcid.org/0000-0001-5331-209XTeemu Leppanen4https://orcid.org/0000-0002-3513-6106Lauri Loven5https://orcid.org/0000-0001-9475-4839Antti Anttonen6https://orcid.org/0000-0002-0575-9409Ali Hassan Sodhro7https://orcid.org/0000-0001-5502-530XMuhammad Mahtab Alam8https://orcid.org/0000-0002-1055-7959Markku Juntti9https://orcid.org/0000-0002-5413-1896Antti Yla-Jaaski10https://orcid.org/0000-0002-2069-1721Thilo Sauter11https://orcid.org/0000-0003-1559-8394Andrei Gurtov12https://orcid.org/0000-0002-9829-9287Mika Ylianttila13https://orcid.org/0000-0002-8079-5514Jukka Riekki14https://orcid.org/0000-0002-1694-9152VTT Technical Research Centre of Finland, Espoo, FinlandNokia, Nokia, FinlandComputer Science Department, Edge Hill University, Ormskirk, U.K.Centre for Wireless Communications, University of Oulu, Oulu, FinlandCenter for Ubiquitous Computing, University of Oulu, Oulu, FinlandCenter for Ubiquitous Computing, University of Oulu, Oulu, FinlandVTT Technical Research Centre of Finland, Espoo, FinlandDepartment of Computer and System Science, Mid-Sweden University, Östersund, SwedenThomas Johann Seebeck Department of Electronics, Tallinn University of Technology, Tallinn, EstoniaCentre for Wireless Communications, University of Oulu, Oulu, FinlandDepartment of Computer Science, Aalto University, Espoo, FinlandInstitute of Computer Technology, TU Wien, Wien, AustriaDepartment of Computer and Information Science, Linköping University, Linköping, SwedenCentre for Wireless Communications, University of Oulu, Oulu, FinlandCenter for Ubiquitous Computing, University of Oulu, Oulu, FinlandThe 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.https://ieeexplore.ieee.org/document/9274307/Communication networksmachine learningphysical layerMAC layernetwork layerSDN |
| spellingShingle | Ijaz Ahmad Shariar Shahabuddin Hassan Malik Erkki Harjula Teemu Leppanen Lauri Loven Antti Anttonen Ali Hassan Sodhro Muhammad Mahtab Alam Markku Juntti Antti Yla-Jaaski Thilo Sauter Andrei Gurtov Mika Ylianttila Jukka Riekki Machine Learning Meets Communication Networks: Current Trends and Future Challenges IEEE Access Communication networks machine learning physical layer MAC layer network layer SDN |
| title | Machine Learning Meets Communication Networks: Current Trends and Future Challenges |
| title_full | Machine Learning Meets Communication Networks: Current Trends and Future Challenges |
| title_fullStr | Machine Learning Meets Communication Networks: Current Trends and Future Challenges |
| title_full_unstemmed | Machine Learning Meets Communication Networks: Current Trends and Future Challenges |
| title_short | Machine Learning Meets Communication Networks: Current Trends and Future Challenges |
| title_sort | machine learning meets communication networks current trends and future challenges |
| topic | Communication networks machine learning physical layer MAC layer network layer SDN |
| url | https://ieeexplore.ieee.org/document/9274307/ |
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