## 2 Related Work
### Physical Layer Authentication (PLA)  
PLA is a technique that aims to verify the identity of wireless devices by analysing their physical characteristics or behaviour, including CSI, Radio Frequency (RF) fingerprint, modulation scheme, etc. By exploiting these unique characteristics, PLA helps to strengthen the security of wireless communications by detecting and identifying malicious or unauthorised devices that may pose a threat to the network. [PERSON] et al. [18] presented a unified framework for Physical Layer Security (PLS) in 5G and beyond networks. They argued that PLS provides a complementary solution to traditional cryptography by exploiting the dynamic characteristics of the wireless environment to ensure authenticity, confidentiality, integrity, and availability of legitimate transmissions. They reviewed the existing PLS methods and classified them into four categories: channel-based, hardware-based, signal-based, and hybrid. PLA is an important part of the PLS security framework, among the above methods, channel-based methods and hardware-based methods are the most representative methods applied in PLA. [PERSON] et al. [19] discussed the security concerns and challenges for 5G/6G mobile network technology and quantum communications. They explained the benefits and applications of 5G/6G networks in various domains, such as smart cities and autonomous vehicles. They also highlighted the security risks that these networks face, such as cyber-attacks, eavesdropping, jamming, spoofing, and privacy violations. They then reviewed some of the existing and proposed security solutions for 5G and 6G networks, such as encryption, authentication, access control, network slicing, edge computing, and blockchain. [PERSON] et al. [20] conducted a survey on intelligent authentication mechanisms for IoT 5G devices in healthcare. They explored how AI can be used to integrate the physical layer and 5G networks to improve the performance of smart healthcare devices. They also presented methods for developing intelligent authentication models using AI, and provided future directions and recommendations. This survey provided a comprehensive overview of the state of the art of intelligent authentication techniques for 5G networks, which is the focus of the proposed mechanism in this paper. The hardware-based physical layer authentication schemes utilize the characteristics of hardware components to classify different devices effectively. During a pre-training process, the authenticator collects radio features generated by device hardware variations as authentication basis. To address issues such as unstable Region Of Interest (ROI), high feature design costs, and poor automation in RF fingerprint-based methods, [PERSON] et al. [21] proposed an RF fingerprint-based authentication scheme for Zigbee. [PERSON] et al. [5] suggested an authentication scheme using differential constellation trace figure and frequency offset as features for RF fingerprint. RF fingerprint-based schemes have higher requirements for the receiver so come with higher implementation costs. [PERSON] et al. [22]introduces a node authentication method, for Wireless Sensor Network (WSN). In such networks, multiple nodes communicate with a sink node, with fixed node positions, but communication channels vary due to the movement of scatterers. The sink node is able to perform continuous authentication of nodes based on wireless fingerprinting, that is, using specific attributes of the nodes' wireless channel for authentication. Another approach to PLA is to extract authentication evidence from wireless channel characteristics, [PERSON] et al. [23] proposed an authentication scheme in MIMO systems using the CFO. They quantized the difference between the estimated CFO and uses statistical signal processing to perform physical layer authentication. [PERSON] et al. [24] proposed the use of deep learning to establish the mapping relationship between device location and CSI, enabling device identity authentication in static environments. [PERSON] et al.