Yuan, Ningze, Zhang, Junqing 
ORCID: 0000-0002-3502-2926, Ding, Yuan and Cotton, Simon
(2025)
Robust Radio Frequency Fingerprint Identification for Bluetooth Low Energy Under Low SNR and Channel Variations.
In: 2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025-3-24 - 2025-3-27.
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WCNC2025_RFFI_BLE.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (478kB) | Preview |
Abstract
Radio frequency fingerprint identification (RFFI) is a promising technique for authenticating Internet of Things (IoT) devices by leveraging unique RF hardware impairments. However, RFFI is vulnerable to channel variations and low signal- to- noise ratio (SNR) conditions. In this paper, we proposed a robust RFFI system specifically designed to tackle these issues for Bluetooth Low Energy (BLE), which is a popular IoT technology. Our system integrated a denoising autoencoder (DAE) to enhance feature robustness under low SNR conditions and employed data augmentation to mitigate the impact of channel and noise effects. We created a testbed consisting of 18 commercial off-the-shelf (COTS) BLE devices and a USRP N210 software-defined radio (SDR) platform and then carried out extensive experimental evaluation under various channel conditions. The experiments involved line-of-sight (LOS) and non-line-of-sight (NLOS) propagation as well as dynamic and static channels. The results demonstrated that our approach consistently achieved over 95 % accuracy in high SNR environments and maintained strong performance with over 75% accuracy at low SNR levels (10 dB).
| Item Type: | Conference Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | 4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, 40 Engineering, 7 Affordable and Clean Energy |
| Divisions: | Faculty of Science and Engineering Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 04 Mar 2025 08:31 |
| Last Modified: | 15 Jun 2025 16:51 |
| DOI: | 10.1109/wcnc61545.2025.10978258 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3190638 |
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