# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 1 Introduction
Unlike traditional MIMO systems, which focus on beamforming at the base station (BS) and user ends, IRS-assisted communication systems require a joint design of passive BF for IRSs and active BF for BS to effectively harness passive BF gains. Additionally, by employing large-scale passive reflecting components, IRS-assisted systems can enhance both energy efficiency (EE) and spectrum efficiency (SE) while remaining cost-effective and energy-efficient. Recent research has explored various aspects of IRS-supported wireless networks, including channel estimation, modulation and encoding, channel modeling, outage probability, SE analysis, symbol error rate (SER) probability, bit error rate (BER) probability, energy efficiency, weighted sum rate, and performance assessment. The extensive research on these topics reflects the enormous potential benefits offered by IRS-enabled environments. In this paper, we present an overview of the in-depth results of IRS-assisted communication using RNNs. RNN algorithms can efficiently handle temporal dependencies, which can be used for time-varying systems like IRS-based wireless communication systems [14]. In addition, RNN algorithms can help obtain accurate channel state information for the IRS channel. One of the core tasks in IRS-based communication is beamforming, where signals are reflected in specific directions to maximize signal strength and minimize interference. RNNs can help optimize this process by continuously learning the optimal reflection anglesand phase shifts over time. RNNs can reduce the computational time required to process data for IRS-based channels. Various RNN algorithms were implemented to evaluate performance in terms of BER and SER probability for IRS-enhanced communication. The types of used RNN algorithms are shown in Figure 1. We examine single-layer and double-layer LSTM, BiLSTM, and gated recurrent units (GRU) models for IRS-based communication. This experiment aims to assess the effectiveness of each RNN model and provide a comparative analysis of the results. In the next phase, we create hybrid models by combining two RNN techniques to observe their performance in IRS-based communication. Specifically, LSTM and BiLSTM are combined to form one hybrid model, and vice versa. Similarly, LSTM and GRU are combined to create another hybrid model, and vice versa. Lastly, we consider a combination of GRU and BiLSTM for IRS-based communication, and vice versa. In this paper, the main contributions can be stated as follows:  
* The goal of this experiment is to estimate the channel for intelligent reflecting surface-based wireless communication using RNN algorithms. Different combinations of RNN algorithms are considered for channel estimation. * Rather than using traditional CE techniques like LS and MMSE, the suggested LSTM, BiLSTM, and GRU combined model can jointly estimate and identify the transmitted data from BS to UE. * Comparative simulation results are presented to evaluate the effectiveness of the suggested system with respect to BER and SER measures. The rest of the article is organized as follows: Section 3 presents the wireless communication system model with IRS, including the reflecting elements and the training data generation process. Section 4 describes the RNN models, while Section 5 details the proposed model using RNN techniques. Section 6 presents the simulation results, and Section 7 provides the conclusions.