Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification
Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification
复制标题
DOI：
10.1109/tifs.2023.3266626
复制
发表时间：
2022-07
影响因子：
6.8
通讯作者：
Guanxiong Shen;Junqing Zhang;A. Marshall;M. Valkama;J. Cavallaro
中科院分区：
文献类型：
--
作者：
Guanxiong Shen;Junqing Zhang;A. Marshall;M. Valkama;J. Cavallaro
射频指纹识别(RFFI)可以通过分析硬件固有损伤引起的信号失真来对无线设备进行分类。最近，最先进的神经网络已被用于射频识别。然而，许多神经网络，例如多层感知器(MLP)和卷积神经网络(CNN)，需要固定大小的输入数据。此外，许多物联网设备工作在低信噪比(SNR)场景中，但此类场景下的RFFI性能往往不尽如人意。在本文中，我们分析了基于MLP和CNN的RFFI系统受输入大小限制的原因。为了克服这一问题，我们提出了四种可以处理可变长度信号的神经网络，即无平坦化CNN、长短期记忆(LSTM)网络、门控递归单元(GRU)网络和变压器。在训练过程中采用了数据增强的方法，显著提高了模型对噪声的鲁棒性。我们比较了两种增强方案，即离线增强和在线增强。测试结果表明，在线测试的效果更好。在推理过程中，进一步利用多包推理方法来提高低信噪比场景下的分类精度。我们以LORA为例，对10台商用LORA设备在不同的信噪比条件下进行了分类，并对系统进行了评估。在线增强可以将低信噪比分类准确率提高50%，多包推理方法可以进一步提高20%以上的准确率。
Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, 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 often unsatisfactory. 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 take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.