论文标题
基于LTE系统和可变通道的现实世界射频信号数据集
A Real-World Radio Frequency Signal Dataset Based on LTE System and Variable Channels
论文作者
论文摘要
射频指纹（RFF）识别由于深度学习而有可能提高无线网络的安全性能。最近，提出了一些RFF数据集以满足大规模数据集的要求。但是，这些数据集中的大多数都是从2.4G WiFi设备和类似的频道环境中收集的。同时，他们仅提供特定设备收集的接收数据。本文将软件无线电外围设备作为数据集生成平台。因此，用户可以自定义数据集的参数，例如频段，调制模式，天线增益等。此外，提出的数据集是通过各种复杂的通道环境生成的，该数据集旨在更好地表征现实世界中的射频信号。我们在发射机和接收器上收集数据集，以模拟基于长期演变（LTE）的真实世界RFF数据集。此外，我们验证数据集并确认其可靠性。可以从GitHub链接下载本文的数据集和可再现代码：https：//github.com/njuptzsp/xsrpdataset。
Radio Frequency Fingerprint (RFF) identification on account of deep learning has the potential to enhance the security performance of wireless networks. Recently, several RFF datasets were proposed to satisfy requirements of large-scale datasets. However, most of these datasets are collected from 2.4G WiFi devices and through similar channel environments. Meanwhile, they only provided receiving data collected by the specific equipment. This paper utilizes software radio peripheral as a dataset generating platform. Therefore, the user can customize the parameters of the dataset, such as frequency band, modulation mode, antenna gain, and so on. In addition, the proposed dataset is generated through various and complex channel environments, which aims to better characterize the radio frequency signals in the real world. We collect the dataset at transmitters and receivers to simulate a real-world RFF dataset based on the long-term evolution (LTE). Furthermore, we verify the dataset and confirm its reliability. The dataset and reproducible code of this paper can be downloaded from GitHub link: https://github.com/njuptzsp/XSRPdataset.
