Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems✩

Mu Niu; Keshuang Han; Xudong Zhong; Baoquan Ren; Pinchang Zhang; Ji He

doi:10.1016/j.dcan.2024.10.001

›› 2026, Vol. 12 ›› Issue (4) :618 -629. DOI: 10.1016/j.dcan.2024.10.001

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Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems^✩

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^a Portland Institute, Nanjing University of Post and Telecommunications , Nanjing 210023, China

^b School of Computer, Nanjing University of Post and Telecommunications , Nanjing 210023, China

^c Institute of Systems Engineering, Academy of Military Sciences , Beijing 100141, China

^d School of Computer Science and Technology, Xidian University , Xi’an 710071, China

^* E-mail addresses: P22000801@njupt.edu.cn (M. Niu),

1023041139@njupt.edu.cn (K. Han),

zxd148367@163.com (X. Zhong),

renbq88@126.com (B. Ren),

zpc@njupt.edu.cn (P. Zhang),

jihe@xidian.edu.cn (J. He).

Mu Niu is currently working toward the BS degree in electronic science and technology with Portland Institute, Nanjing University of Posts and Telecommunications, Nanjing, China. Her research interests include semiconductor devices, wireless security, and physical-layer authentication.

Keshuang Han received the BS degree in information security from the Nanjing University of Posts and Telecommunications, Nanjing, China, in 2023. He is currently working toward the MS degree in wireless network security with the School of Computer Science, Nanjing University of Posts and Telecommunications. His current main interests include in wireless network security, physical layer authentication, and device identification.

Xudong Zhong received the B.S. degree in communications engineering from the School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China, in 2013, the M.S. degree in communications and information systems from the College of Communications Engineering, PLA University of Science and Technology, Nanjing, in 2016, and the Ph.D. degree in information and communication engineering from the College of Communications Engineering, Army Engineering University of PLA, Nanjing, in 2019. He is currently an Associate Research Fellow with the Institute of Systems Engineering, Academy of Military Sciences, Beijing, China, and a member of Chinese Institute of Electronics (CIS). He has published nearly 40 research papers in international journals and conferences. His current research interests include resource allocation and management in satellite communication networks, space information networks, and complex environment communication networking theory. His research works have received the Second Prize of Science and Technology Award of China Institute of Communications (CIC) in 2020 and 2022, the First Prize of Science and Technology Award of Chinese Institute of Command and Control (CICC) in 2023, and the First Prize of Science and Technology Award of CIC in 2025.

Baoquan Ren received the B.S. degree from Zhangjiakou Communication College, Xuanhua, China, in 1997, the M.S. degree from Commanding Communications Academy, Wuhan, China, in 2002, and the Ph.D. degree from the Institute of China Electronic System Engineering Corporation, Beijing, China, in 2010. He was an Senior Engineer with the Institute of China Electronic System Engineering Corporation. He is currently a Research Fellow with the Institute of Systems Engineering, Academy of Military Sciences, Beijing. His current research interests include Internet of Things, wireless communications, and mobile communication network technology.

Pinchang Zhang received the B.S. degree in electronic information engineering from Wuyi University, Jiangmen, China, in 2009, the M.S. degree in electronic and communication engineering from Kunming University of Science and Technology, Kunming, China, in 2012, and the Ph.D. degree in systems information science from Future University Hakodate, Hakodate, Japan, in 2020. He is currently with the College of Cyber Security, Tarim University, Xinjiang. He has published around 30 technical papers at premium international journals and conferences, like IEEE Transactions on Information Forensics and Security, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Mobile Computing, IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, and IEEE INFOCOM. His research interests include wireless network security, physical layer authentication, device identification, satellite internet security, and covert authentication.

Ji He received the B.S. and M.S. degrees from Xidian University, China, and the Ph.D. degree from Future University Hakodate, Japan. He is currently an Associate Professor and Master’s Supervisor with the School of Computer Science and Technology. His research interests lie in AI4Sec, with a particular focus on applying advanced AI techniques to Cybersecurity and wireless security. He specializes in developing LLM-based security agents that implement a closed-loop detection-decision-response process for autonomous cyber defense. He has authored more than 30 research papers in leading journals such as IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), and IEEE Transactions on Wireless Communications (TWC), and holds over 10 national invention patents. He is the (co-) recipient of the 2021 IEEE Sapporo Section Based Award and 2020 IEEE Sapporo Section Encouragement Award.

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Abstract

This paper exploits multi-modal Physical (PHY)-layer features in terms of artificial fingerprint, In-phase/Quadrature (IQ) imbalance and Angle of Arrival (AoA) to propose a novel PHY-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-enabled communication system. First, we resort to the AoA-based spatial fingerprint to effectively address the challenge of channel fingerprint instability induced by high-speed UAV mobility. To further enhance the low discriminability of hardware fingerprints caused by refined manufacturing techniques, artificial Gaussian noise is injected into the transmission signals to assist the receiver in better distinguishing between legitimate and illegitimate UAVs. Then, we jointly combine with inherent IQ imbalance and AoA features to design a hybrid authentication scheme and thus construct a multi-dimensional fingerprint space for a comprehensive characterization of UAV identities. To theoretically evaluate the effectiveness of the proposed authentication framework, the analytical closed-form expressions of performance metrics like false alarm and detection probabilities are also exactly derived based on the statistical signal processing technology and composite hypothesis testing. Finally, we provide large simulation results to validate the correctness and feasibility of the proposed theoretical models, and also discuss the relation between system security and communication service quality under different artificial fingerprint level.

Keywords

Multi-modal PHY-layer authentication / UAV-enabled communication systems / MmWave MIMO technology / Wireless security / Artificial fingerprint

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Mu Niu, Keshuang Han, Xudong Zhong, Baoquan Ren, Pinchang Zhang, Ji He. Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems^✩. , 2026, 12 (4) : 618-629 DOI:10.1016/j.dcan.2024.10.001

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CRediT authorship contribution statement

Mu Niu: Validation, Methodology, Formal analysis, Data curation, Conceptualization. Keshuang Han: Writing – original draft, Software, Formal analysis, Conceptualization. Xudong Zhong: Investigation, Formal analysis. Baoquan Ren: Resources, Project administration, Investigation. Pinchang Zhang: Visualization, Validation, Supervision, Software, Resources, Project administration. Ji He: Investigation, Funding acquisition.

Declaration of competing interest

The authors declare that there are no conflicts of interest.

Acknowledgements

This work was supported in part by the National Key R&D Program of China under Grant 2023YFB3107500, in part by the National Natural Science Foundation of China under Grant 62272241, in part by the State Key Laboratory of Integrated Services Networks (Xidian University), under Grant ISN24-18, in part by the Nanjing University of Posts and Telecommunications Scientific Research Foundation under Grant NY221122.

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