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

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: channel estimation, design and optimization of reflection factor, 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.