## 3 Machine Learning for IRS-Assisted Communication Systems
### Supervised Learning for IRS-Enhanced Communication Systems
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **ML Model Architecture** & **Major Contributions** & **Remarks** \\ \hline
[84] & DNN with three full layers & Phase reconfiguration & Performance is close to the perfect CSI-based approach. The pilot signal overhead is reduced \\ \hline
[85] & Multi-layer perceptron (MLP) with eight hidden layers, ReLU activation function & CE by normalized mean squared error algorithm & Performance improves with higher signal-to-noise ratio (SNR) \\ \hline
[23] & Complex-valued DnCNN & Compressive sensing-based broadband CE algorithm & Robustness makes it possible for application in different SNRs without repetitive training \\ \hline
[86] & Deep-learning-based phase shift control (D-PSC), fully connected layers & Find out optimal phase shifts maximizing data rate & Data rate more than 25\% over the conventional phase shift control schemes using the same pilot resources \\ \hline
[54] & CNN with three convolution layers & Predict the optimal IRS phase shift & Can converge to near-optimal data rates using less than 2\% of the total number of receiver locations \\ \hline
[87] & Deep-RL & Decaying-DQN-based algorithm & Proposed system significantly reduces energy dissipation by integrating IRSs in UAV-enabled wireless networks \\ \hline
[88] & ML-inspired algorithmic framework & Cross-entropy optimization & Proposed method can simultaneously optimize transmit and reflecting beamforming in an IRS-assisted wireless system \\ \hline
[89] & ML framework & Optimization-driven DDPG algorithm & Proposed model can improve both convergence and reward performance compared to conventional model-free learning scheme \\ \hline
[90] & Fully-connected DNN model & Spectral efficiency problem & Proposed model has less computational complexity and does not require any computational load for data labeling \\ \hline
[91] & Neural network model & IRS-aided localization calculation & Proposed system requires multiple APs and a large number of fingerprint grid samples and then acquires great localization results \\ \hline
[83] & DNN with three hidden layers & Beam management (BM) classification for mmWave networks & Gained highly efficient BM with remarkably attenuate system overhead \\ \hline
[52] & Artificial neural network (ANN) with 10 layers, ReLU activation functions & ANN data-driven approaches for optimization & Proposed model can be trained to learn virtually any input–output map [92] \\ \hline
[93] & CNN with three conventional layers, ReLU activation functions & CE using deep denoising algorithm & Proposed method can use optimal minimum mean square error estimator with channel probability density function \\ \hline
[94] & Recurrent neural network (RNN) model, ReLU activation functions & CE using single and multi-scale RNN algorithm & Model enhanced flexibility of overall network to obtain better generalization and fitting capabilities \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between ML-based technologies for IRS-assisted wireless communication. \begin{table}
\begin{tabular}{c c c} \hline \hline
**IRS Communication Problem** & **ML Approach** & **Developed Model** \\ \hline Channel estimation & DL, SL, RL, FL &
\begin{tabular}{c} deep multi-layer perceptron, ChannelNet, \\ CV-DnCNN, DReL, CDRN, ODE-CNN, KGNet \\ \end{tabular} \\ \hline Signal detection & DL & DeepIRS, CNN, SVM \\ \hline Phase shift configuration and beamforming & DL, RL, SL, FL, UL & DQN, DNN, DL-RNN, DQN, DeepMIMO, LPSNet \\ \hline Security & DL, FL & DRL, CNN \\ \hline Resource allocation & DL, FL & DNN, AirFL \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Machine learning approaches for IRS-based communication. In [81], a hybrid precoding architecture was proposed for THz communication for IRS-based communication. An SL-based approach was adapted to optimize the hybrid precoding problem. It has been shown that the proposed method has a similar performance to the traditional hybrid precoding algorithm.