Radio Frequency Transmitter Identification Based on Fingerprinting and Convolutional Neural Network

Abstract

The radio frequency (RF) transmitter identification has a wide application prospect in both military and public communications. The traditional RF transmitter identification of technique is mainly based on expert experience, which shows the shortcomings of low recognition accuracy and weak generalization ability. With the fast development in computer vision, deep learning attracts a lot of attention in recent years and is believed to be a promising scheme in RF transmitter identification. In this paper, the RF transmitter identification is studied based on the RF impairment features extracted from the original data. As a typical deep learning scheme, Convolutional Neural Network (CNN) is adopted to train a classifier to distinguish the RF transmitters. The experiment results show that with the proposed classifier, the same-waveform LoRA signals from different transmitters can be identified with very high accuracy.