Radio Frequency Fingerprint Identification for Narrowband Systems, Modelling and Classification

About this Topic:

Device authentication is essential for securing Internet of things. Radio frequency fingerprint identification (RFFI) is an emerging technique that exploits intrinsic and unique hardware impairments as the device identifier. The existing RFFI literature focuses on experimental exploration, but comprehensive modelling is missing. This paper systematically models impairments of transmitter and receiver in narrowband systems and carries out extensive experiments and simulations to evaluate their effects on RFFI. The modelled impairments include oscillator imperfections, imbalance of inphase (I) and quadrature (Q) branches of mixers and power amplifier (PA) nonlinearity. 

The presenters propose a convolutional neural network-based RFFI protocol. They will carry out experimental measurements over three months and demonstrate that oscillator imperfections are not suitable for RFFI due to their unpredictable time variation caused by temperature change. Their simulation results show that their protocol can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy, namely 99% and 89%, respectively. They also show that the RFFI has some tolerance on different receiver imbalances during training and classification. Specifically, the accuracy is shown to degrade less than 20% when the residual receiver’s gain and phase imbalances are small. Based on the experimental and simulation results, they made recommendations for designing a robust RFFI protocol, namely compensate carrier frequency offset and calibrate IQ imbalances of receivers. They will also present our recent results mapping inference algorithms to architectures and FPGA implementations.

About the Presenters:

Junqing Zhang (M’16, SM’25) received the B.Eng and M.Eng degrees in electrical engineering from Tianjin University, China in 2009 and 2012, respectively, and the Ph.D degree in electronics and electrical engineering from Queen's University Belfast, UK in 2016. 

He is currently a Reader (Associate Professor) at the University of Liverpool. From Feb. 2016 to Jan. 2018, he was a Postdoctoral Research Fellow at Queen's University Belfast. From Feb. 2018 to Oct. 2022, he was a Tenure Track Fellow and then a Lecturer (Assistant Professor) at the University of Liverpool, UK. From Oct. 2022 to May 2026, he was a Senior Lecturer (Associate Professor) at the University of Liverpool. His research interests include the Internet of Things, wireless security, physical layer security, key generation, radio frequency fingerprint identification, and wireless sensing. 

Dr. Zhang was a co-recipient of the IEEE WCNC 2025 Best Workshop Paper Award. He is a Senior Area Editor of IEEE Transactions on Information Forensics and Security and an Associate Editor of IEEE Transactions on Mobile Computing.

Joseph R. Cavallaro (S'78, M'82, SM’05, F’15, LF’25) received the B.S. degree from the University of Pennsylvania, Philadelphia, Pa, the M.S. degree from Princeton University, Princeton, NJ, and the Ph.D. degree from Cornell University, Ithaca, NY, in 1981, 1982 & 1988, respectively, all in electrical engineering.

He is currently a Professor of electrical and computer engineering at Rice University, Houston, TX, since 1988. From 1981 to 1983, he was with AT&T Bell Laboratories, Holmdel, NJ. His research interests include computer arithmetic, and DSP, GPU, FPGA, and VLSI architectures for applications in wireless communications. During the 1996–1997 academic year, he served at the US National Science Foundation as Director of the Prototyping Tools and Methodology Program. He was a Nokia Foundation Fellow and a Visiting Professor at the University of Oulu, Finland in 2005

Dr. Cavallaro is a member of the IEEE SPS TC on Applied Signal Processing Systems. At the related IEEE SiPS workshop, he was TPC Co-Chair in 2016 and General Co-Chair in 2020, 2021, and 2024. He is a Past-Chair of the IEEE CASS TC on Circuits and Systems for Communications. He was a Senior Area Editor for the IEEE Transactions on Signal Processing and has served as an Associate Editor of the IEEE Transactions on Signal Processing and the IEEE Signal Processing Letters.