Deep Radio Fingerprint ResNet for Reliable Lightweight Device Identification

Nowadays, a large number of intelligent devices and smart sensors are being connected by various device identification and/or authentication protocols to satisfy various requirements of 5G services. However, how to identify devices by hardware-level radio frequency (RF) fingerprints of real mobile phones has been rarely researched. In this paper, we propose a novel deep learning (DL) based RF fingerprinting ResNet (RFFResNet) to identify different real mobile phones precisely by employing RF fingerprints hidden in wireless signals. Specifically, we quantitatively show how identification accuracy is influenced by channel conditions, noises, the scale of training data and network parameters. We also evaluate the proposed RFFResNet by using a dataset of 220GB long term evolution (LTE) simulation raw time data and a dataset of 25GB real mobile phone's raw time signals. Experiment results show that our RFFResNet can achieve about 95 %-99 % identification accuracy in real LTE application scenario and show great superiority compared with other existing DL model, such as ResNet18-1D, ResNet34-1D and VGG16-1D.