Towards Channel-Robust and Receiver-Independent Radio Frequency Fingerprint Identification

TL;DR

This paper introduces a three-stage deep learning approach for radio frequency fingerprint identification that effectively mitigates channel and receiver effects, enhancing IoT device authentication accuracy even with limited data.

Contribution

It proposes a novel contrastive learning-based pretraining and Siamese network scheme to improve robustness against channel and receiver impairments in RF fingerprinting.

Findings

Achieved over 90% accuracy in dynamic NLOS scenarios with minimal data

Effectively mitigated channel and receiver effects in diverse datasets

Pretraining reduces the need for extensive fine-tuning data

Abstract

Radio frequency fingerprint identification (RFFI) is an emerging method for authenticating Internet of Things (IoT) devices. RFFI exploits the intrinsic and unique hardware imperfections for classifying IoT devices. Deep learning-based RFFI has shown excellent performance. However, there are still remaining research challenges, such as limited public training datasets as well as impacts of channel and receive effects. In this paper, we proposed a three-stage RFFI approach involving contrastive learning-enhanced pretraining, Siamese network-based classification network training, and inference. Specifically, we employed spectrogram as signal representation to decouple the transmitter impairments from channel effects and receiver impairments. We proposed an unsupervised contrastive learning method to pretrain a channel-robust RFF extractor. In addition, the Siamese network-based scheme is enhanced by data augmentation and contrastive loss, which is capable of jointly mitigating the effects of channel and receiver impairments. We carried out a comprehensive experimental evaluation using three public LoRa datasets and one self-collected LoRa dataset. The results demonstrated that our approach can effectively and simultaneously mitigate the effects of channel and receiver impairments. We also showed that pretraining can significantly reduce the required amount of the fine-tuning data. Our proposed approach achieved an accuracy of over 90% in dynamic non-line-of-sight (NLOS) scenarios when there are only 20 packets per device.