- Title
- Machine Learning Techniques for 5G and beyond
- Creator
- Kaur, Jasneet; Khan, M. Arif; Iftikhar, Mohsin; Imran, Muhammad; Emad Ul Haq, Qazi
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186435
- Identifier
- vital:16892
- Identifier
-
https://doi.org/10.1109/ACCESS.2021.3051557
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 9, no. (2021), p. 23472-23488
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/ V
- Rights
- Copyright @ IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 41 Environmental sciences; 42 Health Sciences; 46 Information and computing sciences; Artificial intelligence (AI); Deep learning (DL); Federated learning (FL); Fifth generation (5G); Machine learning (ML); Reinforcement learning (RL); Sixth generation (6G)
- Full Text
- Reviewed
- Funder
- This work was supported in part by the School of Computing and Mathematics, Charles Sturt University, Australia, and in part by the Deanship of Scientific Research at King Saud University through research group under Project RG-1435-051.
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