论文标题
全面的RF数据集收集和发布：基于深度学习的设备指纹用例
Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case
论文作者
论文摘要
基于深度学习的RF指纹最近被认为是实现新兴的无线网络应用程序的潜在解决方案，例如Spectrum Access Policy执行，自动化网络设备身份验证以及未经授权的网络访问监视和控制。现在比以往任何时候都需要真正的，全面的RF数据集来实现新开发的RF指纹方法的研究，评估和验证。在本文中，我们介绍并发布了使用USRP B210接收器从25种不同的LORA IOT传输设备收集的大规模RF指纹数据集。我们的数据集由代表I/Q时间域样本及其相应的LORA Transmissions基于FFT的文件组成的大量SIGMF兼容二进制文件。该数据集提供了一组基本的实验场景集，考虑了室内和室外环境以及各种网络部署和配置，例如发射机和接收器之间的距离，所考虑的LORA调制的配置，导电实验的物理位置以及用于培训和测试神经网络模型的接收器硬件。
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.
