Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review

被引：38

作者：

Sejan, Mohammad Abrar Shakil ^{[1
,2
]}

Rahman, Md Habibur ^{[1
,2
]}

Shin, Beom-Sik ^{[1
,2
]}

Oh, Ji-Hye ^{[1
,2
]}

You, Young-Hwan ^{[2
,3
]}

Song, Hyoung-Kyu ^{[1
,2
]}

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

来源：

SENSORS | 2022年 / 22卷 / 14期

基金：

新加坡国家研究基金会;

关键词：

intelligent reflecting surfaces (IRSs); machine learning; multiple input multiple output; wireless networks; PASSIVE BEAMFORMING DESIGN; CHANNEL ESTIMATION; SIGNAL-DETECTION; NETWORKS; 5G; MODEL; EFFICIENCY; SYSTEMS;

D O I：

10.3390/s22145405

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：21

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