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Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review
An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316605/ https://www.ncbi.nlm.nih.gov/pubmed/35891085 http://dx.doi.org/10.3390/s22145405 |
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author | Sejan, Mohammad Abrar Shakil Rahman, Md Habibur Shin, Beom-Sik Oh, Ji-Hye You, Young-Hwan Song, Hyoung-Kyu |
author_facet | Sejan, Mohammad Abrar Shakil Rahman, Md Habibur Shin, Beom-Sik Oh, Ji-Hye You, Young-Hwan Song, Hyoung-Kyu |
author_sort | Sejan, Mohammad Abrar Shakil |
collection | PubMed |
description | An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication. |
format | Online Article Text |
id | pubmed-9316605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93166052022-07-27 Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review Sejan, Mohammad Abrar Shakil Rahman, Md Habibur Shin, Beom-Sik Oh, Ji-Hye You, Young-Hwan Song, Hyoung-Kyu Sensors (Basel) Review An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication. MDPI 2022-07-20 /pmc/articles/PMC9316605/ /pubmed/35891085 http://dx.doi.org/10.3390/s22145405 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Sejan, Mohammad Abrar Shakil Rahman, Md Habibur Shin, Beom-Sik Oh, Ji-Hye You, Young-Hwan Song, Hyoung-Kyu Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review |
title | Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review |
title_full | Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review |
title_fullStr | Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review |
title_full_unstemmed | Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review |
title_short | Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review |
title_sort | machine learning for intelligent-reflecting-surface-based wireless communication towards 6g: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316605/ https://www.ncbi.nlm.nih.gov/pubmed/35891085 http://dx.doi.org/10.3390/s22145405 |
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