## 3 Machine Learning for IRS-Assisted Communication Systems
### Deep Learning for IRS-Enhanced Communication Systems  
For the revolution of communication systems, DL is a potential and important technique. As a result of its potential learning capabilities, DL can be applied in various areas of IRS-enhanced wireless networks [51; 16; 52]. In MIMO communication, the timely acquisition and appropriate CSI play a vital role in the wireless communication system. Due to the large number of antennas in a massive MIMO communication system, the estimation of CSI becomes more difficult and complex [53]. To overcome this drawback, DL-based research has been conducted by many researchers for estimating CSI, especially for DL-based IRS  
Figure 6: Taxonomy of ML-based IRS system.  
communication systems [54]. The authors in [35] proposed a DL method for training the IRS reflection matrices from sampled channel knowledge without any command of the IRS geometrical array for estimating the large number of unspecified parameters that are created by IRS. The key idea is to extract environment descriptors that capture information about the multi-path signature of a signal travelling towards IRS. This information helps to train the DL model to estimate the channel parameters. In [55], authors proposed a DL algorithm for the enhancement of the key generation rate (KGR) in the physical layer key generation by using a single antenna UE time division duplexing mode system. The authors proposed KGNet for frequency band feature mapping to construct a reciprocal channel feature between communication parties. To estimate the compressive CSI for IRS-assisted mmWave systems with a low training overhead, the authors in [23] introduced a deep denoising neural network. The proposed DL method can jointly process real and imaginary parts of the channel matrix. The simulation results provide a robust result for SNR = 10 dB, with different numbers of multipath signal components. In this way, for estimating the CSI of an IRS-assited wireless network, the DL method can be adopted. In addition to the above applications of DL in IRS-enhanced wireless networks, to estimate the mapping between a UE position and the IRS phase configuration to maximize the received SNR, a deep neural network (DNN)-based approach in the indoor communication environment was proposed in [56]. The proposed DL architecture consists of five input layers, three hidden layers, and an output layer. The hidden layers are mainly responsible for the input-output mapping the user position and optimal IRS phase configuration. Moreover, for the optimal IRS phase shift configuration, DL can also be applied. The authors in [57] proposed a DL-based algorithm for optimally designing the phase shift of the IRS by training in an offline stage. As the number of reflecting elements increases, the effective gain of the reflecting path increases. In addition, an increase in the number of antennas in the user can enhance the performance. In addition, the DL-based IRS wireless networks can be deployed for signal detection. The signal estimation and detection in the IRS-assisted wireless networks were carried out in [58]. For estimating channels and phase angles of a reflected signal received by an IRS, a DL-based approach was introduced. The bit-error-rate is also improved by the application of the DL method in an IRS-based wireless communication system.