Contemporary Survey of Machine Learning-based Approaches to Solving Communication Issues for Intelligent Reflecting Surfaces

SI Pengbo; LI Shuangyuan; LIU Chang; LI Meng

doi:10.11936/bjutxb2023110015

Journal of Beijing University of Technology > 2025 > 51(1): 87-99. > DOI: 10.11936/bjutxb2023110015

SI Pengbo, LI Shuangyuan, LIU Chang, LI Meng. Contemporary Survey of Machine Learning-based Approaches to Solving Communication Issues for Intelligent Reflecting Surfaces[J]. Journal of Beijing University of Technology, 2025, 51(1): 87-99. DOI: 10.11936/bjutxb2023110015

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Contemporary Survey of Machine Learning-based Approaches to Solving Communication Issues for Intelligent Reflecting Surfaces

SI Pengbo^{1, 2,},
LI Shuangyuan^{1, 2},
LIU Chang^{1, 2, ,},
LI Meng^{1, 2}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Beijing Laboratory of Advanced Information Networks, Beijing 100124, China

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Received Date: November 16, 2023
Revised Date: January 15, 2024

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Abstract

Abstract

Intelligent reflecting surfaces (IRS) can dynamically change the wireless channel and improve the communication performance by subtly adjusting the signal reflection through a large number of low-cost passive reflecting elements, which has become the focus of wireless communication research. However, due to the addition of IRS, the whole communication system becomes more complex and the dynamics of the system is higher, making the communication system face many new challenges. With a strong data processing and adaptive ability, machine learning (ML) can constantly adapt to changing environments and needs and well cope with many challenges in communication systems. Therefore, employing ML to tackle issues in IRS-assisted communication systems has emerged as a central theme of current research. Based on this, this paper gives a systematic overview of the application of ML in IRS system. Starting with the problems existing in IRS-assisted communication system, this paper expounds and analyzes the application of ML in IRS system from four aspects: design and optimization of reflection factor, channel processing and modeling, resource allocation and management, and security enhancement, and it also discusses the advantages and future development trends and challenges of applying ML in IRS system.
- intelligent reflecting surfaces (IRS),
- wireless communication,
- reflection factor,
- channel,
- resource allocation,
- communication security,
- machine learning (ML)

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References (56)

References

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Contemporary Survey of Machine Learning-based Approaches to Solving Communication Issues for Intelligent Reflecting Surfaces