## 3 Machine Learning for IRS-Assisted Communication Systems
### Supervised Learning for IRS-Enhanced Communication Systems  
In the presence of a supervisor, the type of learning that recognizes the system parameters is known as SL. In this learning, the collection of data is needed for an algorithm that  
Figure 7: RL-based IRS-enhanced communication system. is combined with output and input information. Depending on the input and output data connection, the model can be built up. After that, a fresh data set is input into the model to obtain a prediction result [69; 70; 71]. SL is one of the strongest branches of ML, which includes regression, decision tree and random forest, K-nearest neighbors (KNN), support vector machines (SVM), and Bayes classification. This powerful model can be applied to various challenging fields, such as spectrum sensing [72], prediction of traffic/QoE [73], channel/antenna sorting [74], and association of network [75]. In recent years, owing to the benefits of a low complexity and faster speed, SL algorithms can be applied to solve the associated difficulty with enough training data in the IRS-empowered wireless communication system. Figure 8 depicts the SL-based IRS communication system. The signal generated from the base station is first transmitted to IRS elements. Next, the reflected signal from IRS is captured at the user to create a dataset. The dataset is used for training to create an SL-based model for an optimal IRS interaction, as shown in Figure 8. In [76], authors proposed a SL convolutional neural network (CNN) model in the MISO system for obtaining the sum-rate maximization in the IRS wireless communication system. The CNN model is deployed in the IRS, where the input is an incident RF signal and the output is the set of interfering users. More than a 99% accuracy was achieved by the proposed model. The OFDM-based single receiving antenna system model has been utilized in the IRS network to achieve a maximum performance rate with the SL algorithm [35; 77; 78]. The study in [77] proposed an ordinary differential equation (ODE)-based CNN model for IRS-based communication. ODE-based methods can be used to describe the latent relation between different layers in a neural network. The performance analysis shows that ODE-based CNN is always superior compared to a standard CCN network. The authors in [78] proposed an algorithm that leverages previous channel information to improve the quality of an optimal IRS interaction. The authors of [79] proposed a SL algorithm in the OFDM-based single input single output (SISO) system to obtain a maximum performance in the IRS network. The MIMO system can obtain the maximum output performance with the assistance of the SL-CNN model, where the received pilot signals were used as the input data for the ML model [23; 80]. The study conducted in [80] proposed a twin CNN architecture for both direct and cascade channel estimation using pilot signals. The proposed method can tolerate a four-degree displacement in the user location. In addition, a SL-based IRS wireless network communication system was carried out in the [78; 81; 82; 83], where most of the papers mentioned the increase in the achievable rate maximization. Figure 8: SL-based IRS-enhanced communication system.