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Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure.
- Source :
- IEEE Transactions on Vehicular Technology; Jan2020, Vol. 69 Issue 1, p1091-1095, 5p
- Publication Year :
- 2020
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
ARTIFICIAL neural networks
RADIO frequency
INTERNET of things
Subjects
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 69
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 141381434
- Full Text :
- https://doi.org/10.1109/TVT.2019.2950670