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Review
. 2022 Jul 20;22(14):5405.
doi: 10.3390/s22145405.

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

Mohammad Abrar Shakil Sejan  1   2 , Md Habibur Rahman  1   2 , Beom-Sik Shin  1   2 , Ji-Hye Oh  1   2 , Young-Hwan You  2   3 , Hyoung-Kyu Song  1   2
Affiliations
Review

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

Mohammad Abrar Shakil Sejan et al. Sensors (Basel). .

Abstract

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.

Keywords: intelligent reflecting surfaces (IRSs); machine learning; multiple input multiple output; wireless networks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The architecture of reconfigurable reflecting element based on PIN diode with different layers.
Figure 2
Figure 2
IRS-aided wireless communication system with BS and UE channel link.
Figure 3
Figure 3
The phase shift model and its different parameters.
Figure 4
Figure 4
IRS-based communication model for base station to user link scenario.
Figure 5
Figure 5
The comparison between energy efficiency and data rate for DF relay and IRS.
Figure 6
Figure 6
Taxonomy of ML-based IRS system.
Figure 7
Figure 7
RL-based IRS-enhanced communication system.
Figure 8
Figure 8
SL-based IRS-enhanced communication system.
Figure 9
Figure 9
FL-based IRS-enhanced communication system.
Figure 10
Figure 10
IRS-aided wireless communication applications.
See this image and copyright information in PMC

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