ZTE Communications ›› 2025, Vol. 23 ›› Issue (1): 18-29.DOI: 10.12142/ZTECOM.202501004

• Special Topic • Previous Articles     Next Articles

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

MENG Rui1, FAN Dayu1, XU Xiaodong1,2(), LYU Suyu3, TAO Xiaofeng4   

  1. 1.State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2.Department of Broadband Communication, Peng Cheng Laboratory, Shenzhen 518066, China
    3.School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
    4.National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2025-01-09 Online:2025-03-25 Published:2025-03-25
  • About author:MENG Rui received his BS degree in information engineering and PhD degree in information and communication engineering both from Beijing University of Posts and Telecommunications (BUPT), China in 2018 and 2024, respectively. He is currently a postdoctoral fellow with BUPT. His research interests cover next-generation networks, physical layer authentication, identity security, semantic security, deep learning, and Internet of Things.
    FAN Dayu received his BS degree in information engineering from Beijing University of Posts and Telecommunications (BUPT), China in 2024, where he is currently pursuing his master’s degree in communication engineering. His research interests cover wireless security, semantic communication, and deep learning.
    XU Xiaodong (xuxiaodong@bupt.edu.cn) received his BS degree in information and communication engineering and master’s degree in communication and information system both from Shandong University, China in 2001 and 2004, respectively. He received his PhD degree in circuit and system from Beijing University of Posts and Telecommunications (BUPT), China in 2007. He is currently a professor of BUPT, a research fellow of the Department of Broadband Communication of Peng Cheng Laboratory and a member of IMT-2030 (6G) Experts Panel. He has coauthored nine books/chapters and more than 120 journal and conference papers. He is also the inventor or co-inventor of 51 granted patents. His research interests cover semantic communications, intellicise communication systems, moving networks, and mobile edge computing and caching.
    LYU Suyu received her bachelor’s degree and PhD degree in information and communication engineering from Beijing University of Posts and Telecommunications, China in 2018 and 2024, respectively. From November 2022 to September 2023, she was a visiting student with the School of Electronic Engineering and Computer Science, Queen Mary University of London, UK. She is currently a post-doctoral researcher at Beijing University of Technology, China. Her main research interests include ultra-reliable low-latency communications, reconfigurable intelligent surface, and non-orthogonal multiple access.
    TAO Xiaofeng received his BS degree in electrical engineering from Xi’an Jiaotong University, China in 1993, and MS and PhD degrees in telecommunication engineering from Beijing University of Posts and Telecommunications (BUPT), China, in 1999 and 2002, respectively. He is a professor at BUPT, a fellow of the IET, and Chair of the IEEE ComSoc Beijing Chapter. He has authored or co-authored over 200 papers and three books in wireless communication areas. He focuses on 5G/B5G research.

Abstract:

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 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 existing methods face 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.

Key words: physical-layer authentication, artificial intelligence, wireless security, intelligent authentication