Fingerprint recognition

Linguistics

Engineering

Philosophy

Aerospace engineering

Constellation

Biology

Identification (biology)

Materials science

Radio frequency

Telecommunications

Computer science

Botany

TRACE (psycholinguistics)

Physics

Fingerprint (computing)

Pattern recognition (psychology)

Astronomy

Differential (mechanical device)

Electronic engineering

Artificial intelligence

Deep learning

Convolutional neural network

Electrical engineering

IEEE Transactions on Vehicular Technology

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.

Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure