Machine Learning Meets Communication Networks: Current Trends and Future Challenges

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 (+3 others)
2020 IEEE Access  
Ahmad, I.; Shahabuddin, S.; Malik, H.; Harjula, E.; Leppänen, Teemu; Lovén, L.; Anttonen, Antti; Sodhro, A. H.; Alam, M. Mahtab; Juntti, M.; Ylä-Jääski, A.; Sauter, T.; Gurtov, A.; Ylianttila, Mika; Riekki, Jukka Machine Learning Meets Communication Networks: Current Trends and Future Challenges ABSTRACT 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
more » ... ns. 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. INDEX TERMS Communication networks, machine learning, physical layer, MAC layer, network layer, SDN, NFV, MEC, security, artificial intelligence (AI). I. INTRODUCTION The security, availability and performance demands of new applications, services and devices are increasing at a pace higher than anticipated. Real-time responsiveness in application areas like e-health, traffic, and industry requires communication networks to make real-time decisions autonomously. Such real-time autonomous decision-making requires that The associate editor coordinating the review of this manuscript and approving it for publication was Kathiravan Srinivasan . the network must react and learn from the environment, and control itself without human interventions. However, communication networks have until now taken a different path. Traditional networks rely on human involvement to respond manually to changes such as traffic variation, updates in network functions and services, security breaches, and faults. Human-machine interactions have resulted in network downtime [1], have opened the network to security vulnerabilities [2], and lead to many other challenges in current communication networks [3], [4]. 223418 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020 communication networks with such capabilities [19], [25] . Along with the improvement in communication technologies, several disciplines with distinct algorithms and tools have emerged in ML. These algorithms and tools of ML are actively investigated for numerous use-cases, diverse services, and technologies in communication networks. Therefore, this is the right time to shed light on the convergence of the solutions, algorithms, and tools of ML with the advanced technological concepts in communication networks. In this article, we provide a detailed survey of the solutions, algorithms, and tools of ML in communication networks. Beginning from the physical layer, the use of ML in MAC and network layers, and in technologies such as SDN, NFV, and MEC is described. Future research directions are drawn to help the research community to circumvent the challenges of future services (e.g. for massive IoT) and technologies (e.g. NFV) using ML and grasp attention to bridging the existing gaps. Various surveys on the topic have recently appeared covering some specific applications of communication technologies. However, the major difference between the existing articles and this article is that our work provides an up-to-date overview of the merger of the disciplines of ML, the different communication layers, and emerging technological concepts in communication networks. We also provide a summary of the existing survey articles, and the gaps we identify in this article. This article is organized as follows: Section II describes the related work. The survey of ML applications in the three layers in communications networks, i.e., physical, MAC, and network layers is presented in Sections III, IV, and V respectively. The application of ML in SDN and NFV is discussed in Section VI. ML for edge computing is discussed in Section VII, and an overview of using ML for network security is provided in Section VIII. Interesting insights into the future of using ML for communication networks are provided in Section IX, and the paper is concluded in Section X. For smooth readability, the most used acronyms are presented in full in Table. 1.
doi:10.1109/access.2020.3041765 fatcat:erbcetvcrjabrl4qloow3dqcai