# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 1 Introduction  
The intelligent reflecting surface (IRS) is evaluated as a novel radio technology for enhancing wireless communication systems. To improve the spectral efficiency of upcoming wireless networks, significant attention has been given to IRS, a planar metasurface composed of multiple reflecting elements [1]. Communication systems can be greatly enhanced because the IRS proactively alters the wireless channel by enabling each reflecting element to adjust the incident signal with a desired phase shift [2]. This allows the IRS to create a virtual line-of-sight (LoS) link between user equipment (UE) and the base station (BS), particularly when obstacles block the direct path between them. This capability increases the degree of freedom (DoF) in terms of flexibility and reduces end-to-end latency for ultra-reliable low-latency communication (URLLC) applications [3]. Smartphones and otherweb-enabled devices have become essential tools for global communication, information sharing, and entertainment. The wireless communications sector is currently at an exciting juncture: while the fifth-generation (5G) technology has largely been standardized and commercialized, there is a growing focus on the sixth-generation (6G) wireless technology within both academia and industry. The Cisco annual internet report (2018-2023) has stated mobile connectivity is expected to reach over 70% of the world's population by 2023 [4]. Additionally, the mobile subscriber number is projected to increase from 5.1 billion in 2018 to 5.7 billion by 2023. This signifies a pivotal moment in the wireless sector, with strong momentum toward advancing to the next level of wireless technology to meet the increasing global demand for connectivity [5; 6]. Due to increasingly aggressive frequency reuse, cellular networks have grown denser, leading to the development of intercellular synchronization technologies to manage this complexity [7]. However, the unpredictable nature of wireless transmission and the limited available spectrum continue to constrain network capacity. As a result, higher-frequency bands with abundant spectra are gradually gaining popularity. However, in urban areas, electromagnetic (EM) waves at these higher frequencies are more susceptible to blockage by structures such as buildings. To mitigate communication gaps and enhance network coverage, it requires more energy to deploy additional relays and BSs. Consequently, providing wireless service coverage using traditional cellular technologies becomes increasingly challenging. In response to the spectrum shortage faced by communication systems, IRSs have emerged as a key solution, offering the potential to achieve high spectrum and energy efficiency [8]. The utilization of machine learning (ML) techniques in wireless communications, particularly in the context of IRSs [9; 10; 11; 12], has garnered significant attention due to their adaptability and the need to operate across broader search areas. In recent years, numerous researchers have explored diverse ML algorithms tailored to the communication sector in an effort to address challenges within this domain. These efforts aim to enable infrastructure to autonomously overcome various obstacles. Typically, ML methods involve learning parameters and optimization models based on input data to achieve specific objectives. Given the substantial volume of data involved, the efficiency and effectiveness of mathematical optimization procedures are crucial in determining the popularity and application of ML models in the current landscape [13]. By minimizing inter-user interference (IUI), improving non-line-of-sight connections, managing channel blockages, and expanding coverage, Intelligent reflecting surfaces (IRSs) have shown significant potential for enhancing wireless communications. Each IRS component can adjust the phase shifts and amplitude reflection coefficients, modifying the phase and amplitude of incoming signals to enable passive beamforming (BF).