学术报告: Deep Learning-Powered Radio Frequency Fingerprint Identification for Internet of Things

发布人:张艺凡发布时间:2022-06-07动态浏览次数:10

学术报告: Deep Learning-Powered Radio Frequency Fingerprint Identification for Internet of Things

 

时间:2022610星期五晚上5:00pm-6:00pm(北京时间)

地点:线上腾讯会议(416-991-702

 

报告人:Junqing Zhang,英国利物浦大学,助理教授

题目:Deep Learning-Powered Radio Frequency Fingerprint Identification for Internet of Things

 

内容简介: Radio frequency fingerprint identification (RFFI) is an emerging technique that exploits hardware impairments for device authentication. The talk first introduces the impairments of both transmitter and receiver modelling and simulation evaluation. The talk will then elaborate on a novel channel-independent spectrogram approach that is designed to tackle the varying wireless channels. Deep metric-based learning is adopted to achieve a scalable RFFI system. Extensive LoRa-based experimental evaluation has been carried out. The talk will conclude with the remaining challenges of RFFI.

 

个人简介: Dr. Junqing Zhang is a Lecturer (Assistant Professor) with the University of Liverpool, UK. He received the Ph.D degree in Electronics and Electrical Engineering from Queen's University Belfast, UK in 2016. From Feb. 2016 to Jan. 2018, he was a Postdoctoral Research Fellow with Queen's University Belfast, UK. His research interests include the Internet of Things, wireless security, key generation, radio frequency fingerprint identification and wireless sensing. He is the recipient of the EPSRC New Investigator Award.