Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure

Peng, Linning, Zhang, J ORCID: 0000-0002-3502-2926, Liu, Ming and Hu, Aiqun (2019) Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure. IEEE Transactions on Vehicular Technology.

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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:	30 Oct 2019 01:10
URI:	http://livrepository.liverpool.ac.uk/id/eprint/3059732

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