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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.
  • Sejan MAS; Department of Information and Communication Engineering, Sejong University, Seoul 05006, Korea.
  • Rahman MH; Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea.
  • Shin BS; Department of Information and Communication Engineering, Sejong University, Seoul 05006, Korea.
  • Oh JH; Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea.
  • You YH; Department of Information and Communication Engineering, Sejong University, Seoul 05006, Korea.
  • Song HK; Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea.
Sensors (Basel) ; 22(14)2022 Jul 20.
Article en En | MEDLINE | ID: mdl-35891085
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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Redes de Comunicación de Computadores / Aprendizaje Automático Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Redes de Comunicación de Computadores / Aprendizaje Automático Idioma: En Año: 2022 Tipo del documento: Article