@inproceedings{d1dcba551c9d4515b990ddd299f60f39,
title = "A Multi-feature Fusion Model for RF Fingerprint Recognition in Low SNR Scenarios",
abstract = "A multi-feature fusion model based on the mixture of experts (MoE) model is proposed, which further improves the recognition accuracy of RF fingerprint identification algorithms in low SNR scenarios. Different signal processing and data representation methods were used for training. Experiments are conducted to demonstrate the performance advantages of our method, and ablation studies on the data representations are carried out. Experimental results show that the identification accuracy for 150 transmitting devices still exceeds 90\% at a SNR of 4 dB.",
keywords = "Low SNR, Mixture of Experts, RF Fingerprint",
author = "Yiyang Li and Ying Ma and Luyan Xu and Xuhui Ding",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 8th China Intelligent Networked Things Conference, CINT 2025 ; Conference date: 13-06-2025 Through 15-06-2025",
year = "2026",
doi = "10.1007/978-981-95-1103-7\_20",
language = "English",
isbn = "9789819511020",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "204--211",
editor = "Lin Zhang and Yuanjun Laili and Wensheng Yu and Ting Qu",
booktitle = "Intelligent Networked Things - 8th China Intelligent Networked Things Conference, CINT 2025, Proceedings",
address = "Germany",
}