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Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

Morocho-Cayamcela, Manuel Eugenio, Lee, Haeyoung and Lim, Wansu (2019) Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions IEEE Access, 4.

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Abstract

Driven by the demand to accommodate today’s growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the everincreasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning.We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Morocho-Cayamcela, Manuel Eugenio
Lee, Haeyounghaeyoung.lee@surrey.ac.uk
Lim, Wansu
Date : 19 September 2019
DOI : 10.1109/ACCESS.2019.2942390
Copyright Disclaimer : © 2019 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords : Machine learning; 5G mobile communication; B5G; Wireless communication; Mobile communication; Artificial intelligence
Depositing User : Clive Harris
Date Deposited : 27 Sep 2019 07:06
Last Modified : 27 Sep 2019 07:06
URI: http://epubs.surrey.ac.uk/id/eprint/852820

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