## 4 ML Applications for RIS-Assisted Communications
### Estimation of CSI  
The process of CSI estimation for RIS-assisted communication systems using ML takes pilot signals from the receiver or channel realizations as input data. Then, an ML algorithm is trained offline or online for learning to estimate the output CSI channel \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Reference**} & **Type of** & \multirow{2}{*}{**Description**} & **Publicly** \\  & **Datasource** & & **Available?** \\ \hline \multirow{3}{*}{[55]} & \multirow{3}{*}{Synthetic} & Vehicle traffic and & \\  & & ray-tracing simulator & \\  & & to generate 5G-based & \\  & & datasets & \\ \hline \multirow{3}{*}{[56]} & \multirow{3}{*}{Synthetic} & Dataset generator tailored to & \\  & & ML algorithms based on the & \\  & & Remcom ray-tracing & \\  & & application & \\ \hline \multirow{3}{*}{[58]} & \multirow{3}{*}{Physical} & Dataset of channel measurements & \\  & & for different geometric antenna & \\ \cline{1-1}  & & arrangements and RISs & \\ \hline \multirow{3}{*}{[61]} & \multirow{3}{*}{Physical} & Dataset of measurements on RIS & \\  & & in the 6 GHz band & \\ \cline{1-1}  & & and OFDM transceivers & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets for training ML algorithms applied to RIS-assisted communication systems’ enhancement. matrices. Most of the reported research frameworks are DL-based, since in this case, matrices give channel representations of MU systems. CSI estimation is one of the most challenging tasks for RIS-aided communications since the channel estimation and the transmission failure are associated [69]. Hence, the phase-shift selection at the RIS can be improved when performing an adequate channel estimation [70]. Figure 13 shows the channel estimation process aided by ML. We notice that a loss function aids the DL model in computing the distance between the actual and estimated output. The reported frameworks for CSI adapt the loss function according to the mean square error (MSE), having some variations such as the minimum mean square error (MMSE) and the least mean square error (LMSE), among others. Another important parameter to consider is the optimizer, which adjusts the parameters and builds the model and the activation functions used by the neurons. Table 2 enlists DL-based solutions for CSI estimation on RIS-aided wireless systems. Here, we summarize the contributions of each framework and the corresponding remarks. One of the pioneering works introducing a DL framework for CSI in RISs was proposed by [PERSON] et al. [71]. The DL model used the pilot signals that arrived at the UE device as input. The training data were synthetically generated when RIS elements were turned ON/OFF by making input-output pairs for several channel realizations. Input data came from direct and cascaded channels, and the DNN output was vectorized in channel matrices. The proposed approach did not need to be re-trained when the user location changed up to 4 degrees. For the DL architecture, there were two CNNs composed of nine layers, using an SGD optimizer, dropout, and a mini-batch of 128 samples. [PERSON] et al. [72] propose a system