Deep Radio Frequency Fingerprinting Based on Wavelet Scattering Network

With the deployment of 5G and large-scale Internet of Things (IoT), the equipment identification and authentication scheme based on RF fingerprint shows unique advantages in terms of lightweight and uniqueness. However, traditional RF fingerprint identification scheme based on machine learning has the disadvantages of high computational complexity and low accuracy. Meanwhile, this scheme requires large-scale labeled datasets to realize network learning, and due to the nonlinearity of the cascade, we can not well understand the properties and optimal configurations of these networks. To solve above problems, in this paper, we propose an RF fingerprint identification method based on wavelet scattering network in the small-scale dataset. Specifically, in this method, we first design a hybrid network model of wavelet scattering network combined with deep residual network (Resnet18). Then, since one of the main problems of RF fingerprinting is the diversity of signal information at different time scales, we choose to use the construction of scattering network based on wavelet basis to complete the accurate feature decomposition of the nonlinear features of RF fingerprint. These features are stable against deformations and retain high frequency information for identification. Finally, we can use the obtained detailed features to realize the accurate identification of RF radiation source equipments. The experimental results show that our scheme can better suppress the interference of noise in the signal, improve the feature representation ability, and it can obtain higher identification accuracy than other comparison schemes.