Incremental Learning Based Radio Frequency Fingerprint Identification Using Intelligent Representation

Abstract

5G physical security technology plays an important role in the integration of security and communication. In this paper, we propose to use incremental learning consider to use the radio frequency fingerprint identification technology to realize the physical layer security. Our method incremental learning to improve the neural network put forward the idea of incremental learning to improve the neural network. If we receive part of the data, we can train part of it. On the premise of ensuring certain recognition accuracy, the training time and storage space are reduced. In this paper, the received signals are extracted by traditional methods, such as Hilbert Huang transform, I/Q data input, Bi-spectral transform, etc., and then input into the neural network for training classification and incremental learning for training. Simulation results show that the recognition accuracy can reach 95% with 5dB SNR, and the training time can be reduced by nearly 50% with incremental learning.