Shen, Guanxiong, Zhang, Junqing 
ORCID: 0000-0002-3502-2926, Marshall, Alan ORCID: 0000-0002-8058-5242, Peng, Linning and Wang, Xianbin
(2021)
Radio Frequency Fingerprint Identification for LoRa Using Deep Learning.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 39 (8).
pp. 2604-2616.
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INFOCOM2021_RFFI_LoRa.pdf - Author Accepted Manuscript Download (1MB) | Preview |
Abstract
Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrinsic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs.
| Item Type: | Article |
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| Additional Information: | Accepted for publication in IEEE INFOCOM 2021 |
| Uncontrolled Keywords: | Deep learning, Spectrogram, Signal representation, Time-frequency analysis, Hardware, Authentication, Wireless communication, Internet of Things, LoRa, device authentication, radio frequency fingerprint, deep learning, carrier frequency offset |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 14 Jan 2021 10:52 |
| Last Modified: | 15 Mar 2024 14:44 |
| DOI: | 10.1109/JSAC.2021.3087250 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3112081 |
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