## 4 ML Applications for RIS-Assisted Communications
### Beamforming Applications
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Contributions** & **Remarks** \\ \hline
[84] & DL-based approach where the RIS learns the optimal interaction with the incident signal; only channels at the active elements are given & The proposal can achieve nearly optimal data rates without any knowledge of the RIS array geometry \\ \hline
[85] & DL-based approach for phase reconfiguration at the RIS that uses the local propagation environment & The model outperforms the classical least-squares estimator with low training overhead \\ \hline
[86] & Unsupervised DL-based approach for phase-shift prediction of RIS reflecting elements & The model is able to perform shift configuration in real time while mantaining a reasonable rate \\ \hline
[87] & Optimization of both beamformers at the BS and reflective coefficients at the RIS based on a DL scheme & With a few number of pilots the proposal can learn to maximize a rate or minimize an objective \\ \hline
[88] & DL-based solution for optimizing the active beamforming from the BS to users and passive beamforming for the RIS & The proposed solution can achieve better BER performance than a conventional RIS system \\ \hline
[89] & ML approach to maximize the weighted sum-rate designed according to the property of product in RIS-aided systems & The proposal outperforms the block coordinate descent algorithm (BCD) solution \\ \hline
[83] & Joint design solution with beamforming at the access point and phase vector at RIS elements based on the BCD algorithm & The proposal achieves significant performance gain compared to benchmarks that use 100 RIS elements \\ \hline
[90] & DRL approach for the joint design of transmit beamforming and phase shifts at the RIS in an MU-MISO environment & The proposal overcomes hardware impairments for RIS-aided wireless systems \\ \hline
[91] & DRL solution for the real-time control of the phase at the RIS, which is independent of CSI & The proposal outperforms the model-free RIS control without sub-channel CSI \\ \hline
[92] & DRL approach for the joint design of the phase shift at the RIS and the control of trajectories of UAVs & The proposal improves the energy-efficiency performance of an RIS-assisted UAV system \\ \hline
[93] & DRL-based solution for the energy harvesting and phase shift control of an RIS-assisted UAV system & The proposal outperforms in terms of trade-off efficiency and practicality \\ \hline
[94] & DRL approach for the joint optimization of a UAV trajectory and the active/passive beamforming of the RIS & The proposal achieves greater performance in terms of energy savings and sum-rate \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Revised ML-based approaches for beamforming in RIS-assisted communication systems. architecture consists of an ANN with five fully connected layers with a variable number of neurons, a ReLU activation, batch normalization, and the Adam optimizer. The DL algorithm's input data were synthetically generated and previously normalized. An ML-based framework capable of directly optimizing both the BS's beamformers and the RIS's reflective coefficients was proposed in [87]. This work addressed the problem of optimally tuning the RIS elements for capacity enhancement in a multiuser cellular network. In [88], the signal processing functions at the RIS were optimized by learning algorithms, where the modulation and beamforming were performed in parallel. The authors developed a DL architecture that consisted of two DNNs: one for the BS and another for the user device. The BS DNN mapped the transmitted bit stream to the transmitted signal modulation and beamforming, while the user DNN mapped the received signal to the estimated soft bit stream. The outputs from the second DNN were combined and demodulated at the UE device. The goal was to achieve optimal BER performance, where the network was trained once. The designed end-to-end learning scheme could simultaneously optimize the BS precoding, the RIS, and the UE device.