Deep learning-based RF fingerprinting has recently been recognized as a potential solution for enabling newly emerging wireless network applications, such as spectrum access policy enforcement, automated network device authentication, and unauthorized network access monitoring and control. Real, comprehensive RF datasets are now needed more than ever to enable the study, assessment, and validation of newly developed RF fingerprinting approaches. In this paper, we present and release a large-scale RF fingerprinting dataset, collected from 25 different LoRa-enabled IoT transmitting devices using USRP B210 receivers. Our dataset consists of a large number of SigMF-compliant binary files representing the I/Q time-domain samples and their corresponding FFT-based files of LoRa transmissions. This dataset provides a comprehensive set of essential experimental scenarios, considering both indoor and outdoor environments and various network deployments and configurations, such as the distance between the transmitters and the receiver, the configuration of the considered LoRa modulation, the physical location of the conducted experiment, and the receiver hardware used for training and testing the neural network models.
翻译:最近人们认识到,深入学习的RF指纹是使新产生的无线网络应用成为可能的解决办法,例如频谱访问政策执法、自动化网络装置认证和未经授权的网络访问监测和控制。现在比以往任何时候都更需要真正的、全面的RF数据集,以便能够研究、评估和验证新开发的RF指纹方法。在本文件中,我们介绍并发布一个大型的RF指纹数据集,该数据集来自使用USRP B210接收器的25个不同的LoRa驱动的IoT传输装置。我们的数据集包括大量符合SigMF的二进制文件,它们代表I/Q时空样本及其相应的FFTLora传输文档。该数据集提供了一整套必要的实验情景,既考虑到室内和室外环境,也考虑到各种网络部署和配置,例如发射机与接收器之间的距离、考虑过的LoRa调制的配置、所进行试验的实际位置以及用于培训和测试神经网络模型的接收器硬件。