## 1 Introduction
### Related Work  
Compared with identity authentication methods based on cryptographic algorithms, authentication methods created on the basis of physical layer characteristics generally have the advantages of low complexity and high execution efficiency. Common physical layer characteristics include, but are not limited to, receive signal strength (RSS), channel impulse response (CIR), carrier frequency offset (CFO) and channel state information (CSI). These features or measurements are generally difficult for attackers to imitate or predict because they have obvious differences in time and space.  
By analyzing the CSI of signals received from Wi-Fi access points, human activities can be detected in [16], as CSI could characterize how Wi-Fi signals propagate through the surrounding environment. Similarly, by capturing the subtle impact of people on Wi-Fi signal properties as evidenced through transitions in CSI, artificial intelligence can gain acute insight into the presence, movements, and behaviors of inhabitants within an environment [17]. CSI presents a conduit for the contactless sensing of human activity with far-reaching possibilities. To improve the efficiency of CSI-based device authentication and enhance the accuracy of such methods for authenticating mobile devices, ref. [18] proposed using small-scale autoencoders to authenticate individual packet data. In ref. [19], the mapping relationship between CSI information and devices was learned by deep neural networks to ensure that the authenticator had a high accuracy and could be independent of encryption-based authentication methods. By observing the subtle effects of user keyboard tapping on Wi-Fi CSI, a user identifier based on multiple classifiers was proposed in [20], and experiments based on real data showed that its accuracy was satisfactory.  
Based on CSI fingerprint extraction, ref. [21] proved that its proposed intelligent recognition algorithm for the IoT could achieve an authentication cycle of 1.5 ms, which has significantly higher efficiency than cryptographic authentication methods. The authors in [22] divided the CSI-based user identification process into login and interaction stages. The former realizes the learning of CSI features through deep learning, while the latter realizes the recognition of user finger gestures through classifiers. A framework for user identification in both static and dynamic scenarios was established in [23]. The CSI-based method could accurately distinguish different users and could determine the identity of dynamic users through the time-domain correlation of CSI measurements. In ref. [24], the spatio-temporal features of the environment contained in the CSI information were extracted by convolutional neural networks (CNN) and bound to the device for identity authentication. The method still had a high recognition accuracy at low signal-to-noise ratios (such as 0 dB).  
The existing identification methods using physical layer information are gradually improving in performance. However, due to the complexity of authentication methods or the limitations of authentication accuracy, they are still difficult to apply to authentication environments with a large number of wireless terminals. Especially for the identity authentication of numerous terminals in the IoT scenario, lightweight and high accuracy have always been the goals pursued by authentication methods. Existing methods are difficult to balance well between the two. Cryptographic authentication methods have high accuracy but low efficiency, while physical layer feature-based authentication methods have room for improvement in accuracy.