Machine learning techniques for 5G and beyond

Jasneet Kaur, Arif Khan, Mohsin Iftikhar, Muhammad Imran, Ul Haq Qazi Emad

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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 tonext generation and currently we are seeing deployment of fifth generation (5G) wireless systems aroundthe world. Academics and industries are already discussing beyond 5G wireless systems which will be sixthgeneration (6G) of the evolution. One of the main and key components of 6G systems will be the use ofArtificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component andbuilding block of a wireless system that we currently are familiar with from our knowledge of wirelesstechnologies up to 5G, such as physical, network and application layers, will involve one or another AI/MLtechniques. This overview paper, presents an up-to-date review of future wireless system concepts such as6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual modelfor 6G and show the use and role of ML techniques in each layer of the model. We review some classicaland 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 for6G networks.
Original languageEnglish
Pages (from-to)23472-23488
Number of pages17
JournalIEEE Access
Volume9
Early online date13 Jan 2021
DOIs
Publication statusPublished - 10 Feb 2021

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