## 1 Introduction  
With the drastic advancement of biomedical applications and digital healthcare, many secure wireless communication problems arise while enhancing the information processing rate. Distributed systems in healthcare services increasingly require increasing demand for high data rates and real-time communication. Moreover, cloud computing can no longer meet heterogeneity, low delay, and other network requirements [1; 2; 3]. With this context, mobile edge computing (MEC) came into being that extends cloud computing to the edge of the network [4; 5; 6]. It is used to enhance the system efficiency and solve the problems of weak geographic information perception ability, mobility, and provide low delay [7]. Nevertheless, MEC is more vulnerable to impersonation attacks by malicious users. In an impersonation attack, an illegal edge node or mobile user pretends to be the legitimate node and sends data to other network nodes for illicit gains. Traditional security solutions, such as access control, key management, digital signature, and identity authentication, require high complexity, maintenance, and management. The security of traditional authentication technology mainly depends on the mathematical computation complexity of an encryption algorithm, but with the development of quantum computers, this technology is facing the risk of being breached. Moreover, the traditional authentication technology has great limitations in the MEC environment. The computing resources of each node are limited and cannot satisfy the complex encryption and decryption algorithms. Several mobile users in the fields of healthcare and biomedical applications communicate with edge nodes through wireless channels in the MEC environment. However, the open characteristic of wireless network makes it highly vulnerable to impersonation attack. Malicious users forge legitimate user identities to obtain administrative frame or control frame messages, and then launch other malicious attacks. Due to wireless media, the communication between edge nodes and mobile users can face numerous security challenges such as a man-in-the-middle attack, impersonation, hijacking, and eavesdropping attack. As the wireless network environment becomes dynamically complex and unknown, it is difficult to obtain the channel model or parameters, and it is difficult for the receiver to select an appropriate detection threshold for sender identity authentication. The impersonation attack detection method based on unsupervised learning in a physical layer can learn the optimal detection threshold in an unknown communication environment and improve detection performance, so it is more suitable for the development of wireless communication in the future. By introducing a reinforcement learning algorithm, the best detection threshold can be obtained by adaptive spoofing attack detection using current perception information in a dynamic unknown environment. The main advantages of Q-learning-based methods over other algorithms are optimality effects and generality. At present, most studies on impersonation attack detection based on deep reinforcement learning are based on discrete detection threshold analysis, while the continuous control of the detection threshold based on deep reinforcement learning lacks relevant literature research, and the related detection performance needs to be analyzed. In an MEC environment, physical layer authentication is usually used to protect the secure communication of mobile users. The authentication scheme mainly includes device-based features and channel-based features. In recent years, researchers have taken advantage of physical layer characteristics and have relied on signal processing and encoding modulation to secure communication. This can help us to establish a secure transmission between mobile users and edge nodes. Remarkably, the physical layer key generation technology can directly realize the key generation between two communicating parties, eliminating key management center and key distribution procedures.