Peng, Linning, Zhang, Junqing 
ORCID: 0000-0002-3502-2926, Liu, Ming and Hu, Aiqun
(2020)
Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 69 (1).
pp. 1091-1095.
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TVT_2019_DCTF_CNN.pdf - Author Accepted Manuscript Download (2MB) | Preview |
Abstract
This paper proposes a novel deep learning-based radio frequency fingerprint (RFF) identification method for internet of things (IoT) terminal authentications. Differential constellation trace figure (DCTF), a two-dimensional (2D) representation of differential relationship of signal time series, is utilized to extract RFF features without requiring any synchronization. A convolutional neural network (CNN) is then designed to identify different devices using DCTF features. Compared to the existing CNN-based RFF identification methods, the proposed DCTF-CNN possesses the merits of high identification accuracy, zero prior information and low complexity. Experimental results have demonstrated that the proposed DCTF-CNN can achieve an identification accuracy as high as 99.1% and 93.8% under SNR levels of 30 dB and 15 dB, respectively, when classifying 54 target ZigBee devices, which significantly outperforms the existing RFF identification methods.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Physical layer security, radio frequency fingerprint, differential constellation trace figure, convolutional neural network |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 28 Oct 2019 10:24 |
| Last Modified: | 15 Mar 2024 14:44 |
| DOI: | 10.1109/TVT.2019.2950670 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3059732 |
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