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

• URL: http://arxiv.org/abs/2512.12070v1
• Date: Fri, 12 Dec 2025 22:32:08 GMT
• Title: Towards Channel-Robust and Receiver-Independent Radio Frequency Fingerprint Identification
• Authors: Jie Ma, Junqing Zhang, Guanxiong Shen, Linning Peng, Alan Marshall,
• Abstract summary: Radio frequency fingerprint identification (RFFI) is an emerging method for authenticating Internet of Things (IoT) devices.<n>Deep learning-based RFFI has shown excellent performance, but there are still remaining research challenges.<n>We propose a three-stage RFFI approach involving contrastive learning-enhanced pretraining, Siamese network-based classification network training, and inference.
• Score: 23.32212243151147
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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