# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 2 Related Works  
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 ability 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 the challenges within this domain. These efforts aim to enable communication infrastructure to autonomously overcome various obstacles. Typically, ML methods involve learning parameters and optimization models from input data to achieve specific objectives. Given the substantial volume of data involved, the efficiency and effectiveness of mathematical optimization procedures play a crucial role in determining the popularity and application of ML models in the current landscape [13]. Today, ML is applied to solve many aspects of IRS-based communication, with key areas of application including channel estimation, beamforming, energy efficiency, resource management, signal detection, and security [15]. Various approaches have been adopted to address different problems, such as supervised learning, unsupervised learning, reinforcement  
Figure 1: Types of RNN algorithms.  
learning, federated learning, graph learning, transfer learning, hierarchical learning, and meta-learning [16]. In supervised learning, models are trained with labeled data using algorithms such as deep neural networks, convolutional neural networks, decision trees, and support vector machines for IRS-based communication [17]. Unsupervised learning, on the other hand, is applied when input data lacks labels, and results are organized into clusters using methods like k-means or unsupervised neural networks. Reinforcement learning has gained particular attention due to its strong problem-solving capabilities. Common reinforcement learning algorithms include Q-learning, actor-critic learning, multi-agent learning, and double-deep Q-learning. Federated learning allows local models to share parameters in order to create a robust global model, which is particularly desirable for user privacy. Graph-based learning, using graph neural networks, has also been studied extensively. Transfer learning, which reuses existing knowledge to speed up the current learning process, is valued for achieving faster training results. Hierarchical learning divides complex tasks into sub-tasks and solves them from the root level. Finally, meta-learning enhances performance by utilizing the experience gained from prior learning tasks. In the literature, various studies have explored recurrent neural network (RNN) techniques for IRS-based communication. A stacked bidirectional LSTM (BiLSTM) architecture for IRS-assisted UAV communication systems was proposed in [12]. A long short-term memory (LSTM)-based neural network for IRS channel tracking and prediction was introduced in [18]. Additionally, [PERSON]. [19] proposed a prediction method that considers the movement of wireless terminals in IRS-based communication as time series data, using LSTM for this purpose. The study in [20] proposed IRS-assisted NOMA network channel estimation using a convolutional neural network (CNN)-LSTM approach. The authors in [21] applied ML techniques to solve complex optimization problems in wireless communication systems. By combining a hybrid approach of ML-driven method and model-free optimization approach, the results show this approach can increase learning efficiency.