Qiu, Yanjin, Peng, Linning, Zhang, Junqing 
ORCID: 0000-0002-3502-2926, Liu, Ming, Fu, Hua and Hu, Aiqun
(2023)
Signal-independent RFF Identification for LTE Mobile Devices via Ensemble Deep Learning.
In: GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 2022-12-4 - 2022-12-8.
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Signal-independent RFF Identification for LTE Mobile Devices via Ensemble Deep Learning.pdf - Author Accepted Manuscript Download (8MB) | Preview |
Abstract
Radio frequency fingerprint (RFF)-based wireless device authentication is an emerging technique to prevent potential spoofing attacks in wireless communications. The random access preamble of the physical random access channel (PRACH) in Long Term Evolution (LTE) systems is the first message sent from a user equipment (UE). However, PRACH preambles change under different evolved Node B (eNB), which will affect the RFF extraction. In this paper, a signal-independent RFF extraction method is first proposed to extract varying LTE PRACH preambles under different LTE eNBs. Residual transient segment (RTS) features from the varying PRACH preambles are extracted for RFF identification. A convolutional neural network (CNN) based ensemble deep learning scheme is proposed to integrate benefits from different RFF features. An experimental system under real operator LTE eNB is designed to capture and identify real UE signals. Experimental results show that the classification accuracy of five UEs can reach more than 95% under the same eNB and 85% under different eNBs. Furthermore, longtime evaluations show that the UE RTS feature is robust over time.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Uncontrolled Keywords: | CNN, ensemble learning, LTE, PRACH, residual transient segment, RFF |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 21 Feb 2023 15:26 |
| Last Modified: | 15 Mar 2024 14:44 |
| DOI: | 10.1109/GLOBECOM48099.2022.10000722 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3168524 |
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