Radio Frequency Fingerprint Identification Method Based on Ensemble Learning

Abstract

At the physical layer, the combination of radio frequency (RF) fingerprint and deep learning has been widely used to identify wireless communication devices. Considering that the traditional convolutional neural network (CNN) is applied to RF fingerprint, the classification performance is poor in the low signal to noise ratio (SRN) scenario. Considering the significant effect of ensemble learning on improving the classification accuracy of base classifier and the wide application of ensemble learning in other fields, we propose an RF fingerprint classification method based on ensemble learning, which improves the classification accuracy on the basis of traditional CNN. Firstly, the RF signals of four power amplifiers are collected by acquisition equipment. These signals are composed of in-phase and quadrature signals, the sampling points are 200,000. After slicing the data samples and artificially introducing different SRN noises, it is then input into an improved CNN for training. Bagging and Boosting algorithms in ensemble learning are combined with the improved CNN to integrate multiple base classifiers and output the final classification results. Finally, the simulation results prove the proposed method. Its classification accuracy is better than traditional CNN in low SNR environment.