## 5 Paradigms of AI for Physical Layer Optimization and System Design
### Overview of AI and ML Enabling Technologies  
As powerful and intelligent tools, AI and ML can be widely applied in the optimization and system design of 5G network infrastructures in order to solve complex problems [130; 131; 132]. AI and ML approaches have been widely used to solve complex problems within the upper layers of open system interconnection models, such as the deployment of wireless communication and cognitive radio relay networks, which produce significant performance improvements compared to secure wireless communication systems that were designed using conventional methods [133]. However, considering the recent success of current trends and the challenges of future wireless communication systems, such as high-speed connection demands in complex scenarios with unknown channel models, AI and ML approaches have now also been considered in the physical layer of wireless transmission strategies [134; 135; 136; 137]. Recent advances in AI and ML techniques, especially deep learning (DL), reinforcement learning and convolutional neural networks (CNN), have provided novel concepts and potential opportunities to solve these complex problems. Extensive research has been conducted on the application of ML- and AI-based methods in 5G and B5G networks, for example, as presented in [138; 139; 140; 141; 142; 143]. Examples of typical applications in the physical and data link layers include the use of DL or reinforcement learning algorithms to evaluate and predict channel quality, the use of orthogonal frequency division multiplexing (OFDM) for symbol detection at the receiving end, channel encoding and decoding and dynamic spectrum random access, among other functions. In channel quality assessment, deep neural network (DNN) algorithms analyze limited pilot signals to help massive MIMO systems infer complete and accurate knowledge of CSI. Therefore, performance enhancement, quality of service and low computational costs can be achieved by applying CNN, DNN and reinforcement learning algorithms in channel encoding and decoding.  
Furthermore, AI learning-based methods have been used to design universal frameworks for various technical challenges for base stations, characteristics and applications, which have contributed to the enhancement of 5G wireless networks. AI algorithms can be implemented to address various problems in resource utilization, network traffic and user demand. They can make it possible to smartly coordinate 5G base stations (BSs) and other core 5G network elements, such as user plane functions [144; 145; 146]. ML and AI schemes can help to further improve reliability, security performance, energy efficiency and network latency by developing integrated scheduling and efficient allocation schemes. Considering the potential of PLS in the system design and optimization of 5G networks, the challenges and corresponding opportunities for intelligent AI and ML technologies to achieve a high level of security at the physical layer, as well as cross-layer applications, deserve to attract more attention from the research community. AI and ML algorithms are suitable for the analysis of future 5G networks and the adaption of resource management and network protocols for different applications. These algorithms have their own different pros and cons. An overview of some of the notable techniques and focused issues that are associated with AI and ML technologies are presented in Table 4.  
In the next subsections, we discuss some ML- and AI-based applications in the design and optimization of 5G networks, which cover three different problems within the physical layer of wireless networks, including secure multi-antenna techniques, secure resource management and the optimization of signal processing methods.