## 4 ML Applications for RIS-Assisted Communications
### Estimation of CSI
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Reference** & **Contributions** & **Remarks** \\ \hline
[71] & DL twin CNN-based architecture for the estimation of CSI & The model does not need to be re-trained when the user location is changed up to 4 degrees \\ \hline
[72] & Combined DL and compressive sensing to estimate the CSI using only the active reflective elements of the RIS & The DL scheme does not need any knowledge of the RIS array geometry, but a large dataset is needed \\ \hline
[73] & DL approach for the estimation of CSI and symbol detection in RIS wireless systems & The proposed model estimates the CSI and phase angles from the received signals \\ \hline
[74] & DL-based estimation of channels from UE to RIS which works at different SNR levels and number of multipaths & The model can reach a high NMSE performance with a few elements activated during the training stage \\ \hline
[75] & Proposal of two DL architectures for CSI estimation exploiting the low-rank sparsity of channels & The proposal can increase its performance when increasing the density of sensing devices \\ \hline
[76] & Proposed the channel estimation as an image denoising problem using a CNN-based architecture & Numerical results show that the proposed model performance is close when the system has perfect channel knowledge \\ \hline
[77] & DML-based scheme for CSI estimation where the BS and the users perform training collaboratively & The proposal can achieve higher estimation accuracy when the pilot overhead is reduced to \(1/8\) \\ \hline
[78] & Proposed a deep unfolding network for the estimation of the downlink channel of an RIS wireless system & The proposal outperforms the least-squares estimator and has lower complexity using a smaller training overhead \\ \hline
[79] & DL-based estimation of CSI for RIS-aided and massive-MIMO systems which extracts the correlation features of subcarriers & The proposal outperforms in terms of spectral efficiency with a lower signaling overhead \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ML applications for CSI estimation on RIS-aided communications. divided into two phases. Firstly, in the learning phase, the RIS employs an exhaustive search to collect the dataset for the DL model. The optimal beamforming vector with the highest achievable rate reflects the transmitted data. Hence, the model learns how to map the input channel vector to an output vector. Finally, in the prediction stage, the DL model predicts the beamforming vector from the estimated sampled channel. The DL architecture consists of an adapted NN whose number of layers varies, and the neurons are activated by a ReLU function. Also, the model is trained using the DeepMIMO dataset. In [73], the authors developed a DL-based detector called _DeepRIS_ for wireless receivers. The _DeepRIS_ framework estimated the channel and phase angles from the received signal. The model was trained offline with synthetic channel realizations, phase variances, and random sequences of bits. At the output, the model estimated the transmitted symbol and the CSI. This system model consisted of an ANN with a variable number of fully connected layers using a _tanh_ activation function to keep negative weights and the Adam optimizer. A deep-denoising neural network (DDNN) for CSI estimation on mmWave RIS systems was proposed in [74]. Only a few elements were activated using partial channels in the training stage. The complete channel matrix could be reconstructed from the limited measurements by using compressive sensing (CS) with orthogonal matching pursuit (OMP) due to the sparsity of cascaded channels [80]. The complete OMP-DL solution obtained multi-carrier pilots and then built a redundant dictionary to improve system accuracy. The model architecture consisted of a CNN with 15 convolutional layers, with 64 filters each of size \(3\times 3\times 64\), using a ReLU activation function and Adam as the optimizer. A CNN can be used to reduce the complexity of the RIS in CSI estimations.