Machine Learning Meets Communication Networks: Current Trends and Future Challenges

被引：75

作者：

Ahmad, Ijaz ^{[1
]}

Shahabuddin, Shariar ^{[2
]}

Malik, Hassan ^{[3
]}

Harjula, Erkki ^{[4
]}

Leppanen, Teemu ^{[5
]}

Loven, Lauri ^{[5
]}

Anttonen, Antti ^{[1
]}

Sodhro, Ali Hassan ^{[6
]}

Mahtab Alam, Muhammad ^{[7
]}

Juntti, Markku ^{[4
]}

Yla-Jaaski, Antti ^{[8
]}

Sauter, Thilo ^{[9
,10
]}

Gurtov, Andrei ^{[11
]}

Ylianttila, Mika ^{[4
]}

Riekki, Jukka ^{[5
]}

机构：

[1] VTT Tech Res Ctr Finland, Espoo 02044, Finland

[2] Nokia, Espoo 02610, Finland

[3] Edge Hill Univ, Comp Sci Dept, Ormskirk L39 4QP, England

[4] Univ Oulu, Ctr Wireless Commun, Oulu 90570, Finland

[5] Univ Oulu, Ctr Ubiquitous Comp, Oulu 90570, Finland

[6] Mid Sweden Univ, Dept Comp & Syst Sci, Ostersund, Sweden

[7] Tallinn Univ Technol, Thomas Johann Seebeck Dept Elect, Tallinn, Estonia

[8] Aalto Univ, Dept Comp Sci, Espoo 02150, Finland

[9] TU Wien, Inst Comp Technol, A-1040 Vienna, Austria

[10] Danube Univ Krems, Dept Integrated Sensor Syst, A-2700 Wiener Neustadt, Austria

[11] Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

芬兰科学院;

关键词：

Communication networks; machine learning; physical layer; MAC layer; network layer; SDN; NFV; MEC; security; artificial intelligence (AI); SOFTWARE-DEFINED NETWORKING; RADIO RESOURCE-MANAGEMENT; EDGE COMPUTING ARCHITECTURE; NEURAL-NETWORK; CHANNEL ESTIMATION; WIRELESS NETWORKS; MASSIVE MIMO; INTRUSION-DETECTION; MOBILITY PREDICTION; SWARM INTELLIGENCE;

D O I：

10.1109/ACCESS.2020.3041765

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：223418 / 223460

页数：43

共 351 条

[81] Two Use Cases of Machine Learning for SDN-Enabled IP/Optical Networks: Traffic Matrix Prediction and Optical Path Performance Prediction [Invited] [J].