## 5 Paradigms of AI for Physical Layer Optimization and System Design
### AI for End-to-End Multi-Antenna Techniques
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML- and AI-Based Techniques** & **Focused Issues** & **Advantages** & **Limitations** \\ \hline AI and edge & Investigation of the gap between & Robust PHY layer key generation & Complexity of training models for \\ computing (EC) [34] & PHY security and AI-EC. & schemes and secure resource & various PLS issues. \\ \hline Deep reinforcement learning [35] & Enabling of secured visible light & Achievement of the optimal & \\  & communication (VLC). & solution between secrecy rate & Avoidance of the \\  & & and utility. & quantization error. \\ \hline Integrated AI [36] & Integration of wireless power & Achievement of the trade-off & \\  & transfers and cooperative & between security performance & Limitations of optimal scheme for \\  & jamming for secure transmission. & and energy consumption. & solving more complex problems. \\ \hline Iterative water-filling algorithm & A comprehensive investigation of & Bridging of the gap between AI & Challenging integration of AI and \\  & MIMO eigenmode transmission. & and 5G technologies. & 5G networks. \\ \hline Distributed AI federated learning [148] & A brief survey on multi-antenna & Contribution of robust and & \\  & techniques. & fine-grained security metrics. & Security issues at the device level. \\ \hline Feed-forward DL model [149] & RF beamforming & Promising results for & Lack of investigation into \\  & codeword prediction. & beamforming problems. & security issues. \\ \hline Adversarial DL model [150] & Adversarial attacks for & & Complexity of adversarial \\  & beamforming prediction. & Consideration of security issues. & training approach. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of ML and AI techniques for physical layer design and optimization. Furthermore, an ANN-based autoencoder was proposed in [161] to generate end-to-end system optimization, which comprises a model-based solution and adaptive learning modules that can obtain important information in real-time scenarios. This framework has established applications in multi-antenna MIMO channel systems, which consist of three functional blocks: the transmitter, the MIMO channel generator and the receiver, as illustrated in Figure 10. The results showed that the learning module aims to encode the data inputs in an unsupervised fashion, which has the ability to redesign sets of data inputs at the output module. The functionality of the autoencoder is obtained by iteratively training the transmitter and receiver modules with block error rate (BLER), which is represented as fully connected DNNs. The resulting system has advantages for guaranteeing energy constraints. This scheme was later extended by other researchers to include multiple user MIMOs [162] in order to achieve better enhanced BER performances in the MIMO channels. This functionality is performed by learning from the input data and training for different scenarios with multiple antennae and various levels of CSI knowledge. Therefore, existing optimization problems have proved the advantages of using AI, specifically DNN-based schemes for end-to-end communication without the assumption of several models in the existing conventional design concepts. Thanks to the potential of autoencoders, the complicated issues that cannot be modeled by conventional methods can be solved by training the scenarios and then optimizing the performance.