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Radio Frequency Fingerprinting (RFF) is one of the promising passiveauthentication approaches for improving the security of the Internet of Things(IoT). However, with the proliferation of low-power IoT devices, it becomesimperative to improve the identification accuracy at low SNR scenarios. Toaddress this problem, this paper proposes a general Denoising AutoEncoder(DAE)-based model for deep learning RFF techniques. Besides, a partiallystacking method is designed to appropriately combine the semi-steady andsteady-state RFFs of ZigBee devices. The proposed Partially Stacking-basedConvolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well asdevice identification. Experimental results demonstrate that compared toConvolutional Neural Network (CNN), PSCDAE can improve the identificationaccuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive WhiteGaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, theidentification accuracy is as high as 97.5%.

Radio Frequency Fingerprint Identification Based on Denoising Autoencoders