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Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification by Shen, Guanxiong; Zhang, Junqing; Marshall, Alan; Valkama, Mikko; Cavallaro, Joseph is a scholarly article available to read on EtoBox.

What is Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification about?

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by the intrinsic hardware impairments. State-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is rarely investigated. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network and transformer. We adopt data augmentation during training which can significantly improve the model's robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We

Author

Shen, Guanxiong; Zhang, Junqing; Marshall, Alan; Valkama, Mikko; Cavallaro, Joseph

Published

2022

Language

EN