Endogenous Security Through AI-Driven Physical-Layer Authentication for Future 6G Networks

Abstract: The 6G mobile networks are expected to offer higher data rates, spectral efficiency, and reliability than 5G, achieving a significant transition from mobile communication to mobile information services. However, the revolutionary network architecture and emerging enabling technologies of 6G will bring increasingly severe security challenges. To ensure the access security of 6G, physical-layer authentication (PLA) leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters. Furthermore, the introduction of artificial intelligence (AI) facilitates the learning of the distribution characteristics of channel fingerprints, effectively addressing the uncertainties and unknown dynamic challenges in the process of wireless link modeling. This paper reviews representative AI-enabled PLA schemes and proposes a graph neural network (GNN)-based PLA approach in response to the challenges faced by existing methods in identifying mobile users. Simulation results demonstrate that the proposed method outperforms six baseline schemes in terms of authentication accuracy. Furthermore, this paper outlines the future development directions of PLA.

Keywords: physical-layer authentication; artificial intelligence; wireless security; intelligent authentication