activity
20242026
collaborators

6 papers

cs.LG2026

Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices

Yijia Guo, Junqing Zhang, Yao-Win Peter Hong +1

The rise of wireless technologies has made the Internet of Things (IoT) ubiquitous, but the broadcast nature of wireless communications exposes IoT to authentication risks. Physica…

cs.CR2025

Towards Channel-Robust and Receiver-Independent Radio Frequency Fingerprint Identification

Jie Ma, Junqing Zhang, Guanxiong Shen +2

Radio frequency fingerprint identification (RFFI) is an emerging method for authenticating Internet of Things (IoT) devices. RFFI exploits the intrinsic and unique hardware imperfe…

cs.LG2025

Practical Physical Layer Authentication for Mobile Scenarios Using a Synthetic Dataset Enhanced Deep Learning Approach

Yijia Guo, Junqing Zhang, Y. -W. Peter Hong

The Internet of Things (IoT) is ubiquitous thanks to the rapid development of wireless technologies. However, the broadcast nature of wireless transmissions results in great vulner…

cs.CR2025

Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice

Junqing Zhang, Francesco Ardizzon, Mattia Piana +2

The identification of the devices from which a message is received is part of security mechanisms to ensure authentication in wireless communications. Conventional authentication a…

eess.SP2025

Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model

Guolin Yin, Junqing Zhang, Yuan Ding +1

Securing Internet of Things (IoT) devices presents increasing challenges due to their limited computational and energy resources. Radio Frequency Fingerprint Identification (RFFI)…

eess.SP2024

Towards Robust RF Fingerprint Identification Using Spectral Regrowth and Carrier Frequency Offset

Lingnan Xie, Linning Peng, Junqing Zhang

Radio frequency fingerprint identification (RFFI) is a promising device authentication approach by exploiting the unique hardware impairments as device identifiers. Because the har…