Radio Frequency Fingerprint Identification Based on Transfer Learning

Abstract

Given the current radio frequency(RF) fingerprint identification methods based on deep learning, there is a problem of poor recognition performance in the scene of RF fingerprint offset. This paper designs an Internet of Things (IoT) devices identification system based on software defined radio(SDR) and transfer learning (TL) technology. The main advantage of this system is to use the universal software radio peripheral (USRP) to collect the RF signals of IoT devices and identify the RF signals preprocessed by SDR based on transfer learning. This system can reduce data redundancy and model complexity, has the characteristics of lightweight and high efficiency and meet different experimental scenarios' needs. The experimental results show that compared to reducing the recognition accuracy based on the deep learning method in RF fingerprint offset, transfer learning dramatically improves the recognition performance and reduces the model training time. Simultaneously, the experimental process is in a natural wireless communication environment, and the actual application has high reliability.