## 4 RISs Controlled by AI
### Supervised Learning  
Supervised learning is mainly used to solve problems for which we have significant amounts of data, and their future conditions are not expected to change considerably. This method can be very accurate when the necessary (big) dataset is available, but the caveat is that it must be labeled. Table 2 summarizes four different applications of Supervised Deep Learning in RIS, for wireless communications.  
Deep Learning has been applied in order to reduce the number of activated elements in a large RIS system. A multi-layer perceptron (MLP) has been employed to optimize element activation and deactivation, achieving energy-efficient operation without significant performance degradation [31]. A Supervised Deep Learning approach has been used in [32] to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Problem Description** & **Algorithm Used** & **Achievement** \\ \hline [PERSON] et al., 2019 [31] & Reduction in the number of activated elements in a very large RIS & Multi-layer perceptron & Optimization of element activation/deactivation \\ \hline [PERSON] et al., 2021 [32] & Reduction in interference & Intelligent Spectrum Learning based on Supervised & Distinguish desired from interfering signals \\  & & Deep Learning & \\ \hline [PERSON] et al., 2022 [33] & Maximization of sum-rate for RIS hybrid precoding architecture & Multiple Discrete Classification based on Supervised Deep Learning & Reduction in runtime without any significant performance degradation \\ \hline [PERSON] et al., 2021 [34] & Addition of signal processing units for RIS to overcome the difficulty of acquiring CSI & Backpropagation algorithm based on Supervised Deep Learning & Real-time channel state information acquisition \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selection of research works using Supervised Deep Learning.  
reduce interference in RIS-assisted networks. The proposed method, termed as Intelligent Spectrum Learning (ISL), enables RIS to distinguish desired signals from interfering ones, improving SINR and system reliability. Moreover, a hybrid precoding architecture for THz communications has been presented, utilizing Deep Learning-based Multiple Discrete Classification (DL-MDC). The goal of this approach, compared with existing solutions, is the maximization of the sum-rate while significantly reducing runtime with negligible performance loss [33]. The last line of Table 2 refers to a study that focuses on overcoming the difficulty of acquiring CSI in RIS systems. A supervised learning-based backpropagation algorithm is employed, enhancing system efficiency and enabling real-time CSI acquisition [34].  
The brief presentation of the previous paragraph demonstrates the contribution of each study to RIS-enabled wireless communications and showcases the potential of supervised deep learning for addressing computationally complex problems in real-time adaptive solutions. Energy efficiency is emphasized by reducing hardware demands and computational overhead [31; 33], while the improvement of the system's performance is achieved either by managing interference [32], or by optimizing sum-rate [33]. However, the reliance on supervised learning requires extensive labeled datasets, which may not always be feasible in dynamic wireless environments, a limitation that may be overcome by employing FL techniques, as will be shown further down.