Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review
被引:38
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作者:
Sejan, Mohammad Abrar Shakil
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Sejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
Sejong Univ, Dept Convergence Engn Intelligent Drone, Seoul 05006, South KoreaSejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
Sejan, Mohammad Abrar Shakil
[1
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Rahman, Md Habibur
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Sejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
Sejong Univ, Dept Convergence Engn Intelligent Drone, Seoul 05006, South KoreaSejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
Rahman, Md Habibur
[1
,2
]
Shin, Beom-Sik
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Sejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
Sejong Univ, Dept Convergence Engn Intelligent Drone, Seoul 05006, South KoreaSejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
Shin, Beom-Sik
[1
,2
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Oh, Ji-Hye
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Sejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
Sejong Univ, Dept Convergence Engn Intelligent Drone, Seoul 05006, South KoreaSejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
Oh, Ji-Hye
[1
,2
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You, Young-Hwan
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Sejong Univ, Dept Convergence Engn Intelligent Drone, Seoul 05006, South Korea
Sejong Univ, Dept Comp Engn, Seoul 05006, South KoreaSejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
You, Young-Hwan
[2
,3
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机构:
Song, Hyoung-Kyu
[1
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机构:
[1] Sejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea
[2] Sejong Univ, Dept Convergence Engn Intelligent Drone, Seoul 05006, South Korea
[3] Sejong Univ, Dept Comp Engn, Seoul 05006, South Korea
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.
机构:
State Key Laboratory of ISN, Xidian University, Xi’an,710071, China
University of Pisa, Dipartimento di Ingegneria dell’Informazione, Pisa,56126, ItalyState Key Laboratory of ISN, Xidian University, Xi’an,710071, China
Long, Wenxuan
Chen, Rui
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机构:
State Key Laboratory of ISN, Xidian University, Xi’an,710071, China
National Mobile Communications Research Laboratory, Southeast University, Nanjing,210018, ChinaState Key Laboratory of ISN, Xidian University, Xi’an,710071, China
Chen, Rui
Moretti, Marco
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机构:
University of Pisa, Dipartimento di Ingegneria dell’Informazione, Pisa,56126, ItalyState Key Laboratory of ISN, Xidian University, Xi’an,710071, China
Moretti, Marco
Zhang, Wei
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机构:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney,NSW,2052, AustraliaState Key Laboratory of ISN, Xidian University, Xi’an,710071, China
Zhang, Wei
Li, Jiandong
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机构:
State Key Laboratory of ISN, Xidian University, Xi’an,710071, ChinaState Key Laboratory of ISN, Xidian University, Xi’an,710071, China