## 4 RISs Controlled by AI
### Unsupervised Learning  
Unsupervised, unlike supervised learning, does not require costly labeled datasets, as it uses a self-classification approach. Thus, its use is most appropriate for diverse datasets and/or unstructured data, or, generally, ineffectively labeled data. It may also be used to identify correlation between data and offers valuable insights. Unsupervised learning, when applied to RIS-enabled systems, may work well with big datasets, since they do not need to be previously labeled or structured. Table 3 offers a view to three key studies on unsupervised deep learning for RISs.  
All three works of Table 3 demonstrate the potential of unsupervised learning to address the inherent non-convexity and computational challenges of RIS beamforming and highlight the feasibility of deploying RISs in real-time wireless communication systems, particularly for latency-sensitive applications like 6G networks. Ref. [35] addresses the complexity of passive beamforming optimization in RIS-assisted systems. A customized deep neural network training using unsupervised learning is proposed in order to predict phase shifts in real-time. The achieved performance is comparable to semi-definite relax  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Problem Description** & **Algorithm Used** & **Achievement** \\ \hline [PERSON] et al., 2020 [35] & \begin{tabular}{c} Passive beamforming optimization in \\ RIS-assisted systems \\ \end{tabular} & \begin{tabular}{c} Customized deep neural \\ network trained using \\ unsupervised learning \\ \end{tabular} & \begin{tabular}{c} Prediction of phase-shifts in \\ real-time by using unlabeled \\ collected data \\ \end{tabular} \\ \hline [PERSON] et al., 2021 [36] & \begin{tabular}{c} Beamforming in active and passive \\ MISO systems \\ \end{tabular} & \begin{tabular}{c} Two-stage Unsupervised \\ Deep Learning \\ \end{tabular} & \begin{tabular}{c} Optimization of both transmit \\ beamforming and RIS phase \\ shifts with reduced \\ computational time \\ compared to traditional \\ iterative approaches \\ \end{tabular} \\ \hline [PERSON] et al., 2023 [37] & \begin{tabular}{c} Passive beamforming for RIS-aided \\ MIMO systems \\ \end{tabular} & \
\begin{tabular}{c} Neural network— \\ Unsupervised Deep Learning \\ \end{tabular} &
\begin{tabular}{c} Low implementation \\ complexity and greater time \\ efficiency than conventional \\ programming strategies \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selection of research works demonstrating Unsupervised Deep Learning.  
ation (SDR) methods albeit with significantly reduced computational complexity. The latter is a common asset for all approaches [35; 36; 37], since they manage to reduce significantly computational cost compared to traditional iterative methods; thus, they are suitable for real-time applications. The two-stage unsupervised learning algorithm presented in [36] applies to active and passive beamforming in multi-user multiple-input single-output (MISO) systems, while a deep neural network architecture tailored for passive beamforming has also been proposed for RIS-aided multi-user MIMO systems [36].  
Unsupervised deep learning has demonstrated significant promise in optimizing RIS-enabled wireless systems by reducing complexity and eliminating the need for labeled datasets. However, unsupervised methods may be limited by centralized training constraints and transitioning towards FL could unlock new opportunities for scalable, efficient, and privacy-preserving solutions in the era of 6G communications.