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Radio Frequency Fingerprint (RFF) identification on account of deep learninghas the potential to enhance the security performance of wireless networks.Recently, several RFF datasets were proposed to satisfy requirements oflarge-scale datasets. However, most of these datasets are collected from 2.4GWiFi devices and through similar channel environments. Meanwhile, they onlyprovided receiving data collected by the specific equipment. This paperutilizes 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 isgenerated through various and complex channel environments, which aims tobetter characterize the radio frequency signals in the real world. We collectthe dataset at transmitters and receivers to simulate a real-world RFF datasetbased on the long-term evolution (LTE). Furthermore, we verify the dataset andconfirm its reliability. The dataset and reproducible code of this paper can bedownloaded from GitHub link: https://github.com/njuptzsp/XSRPdataset.

A Real-World Radio Frequency Signal Dataset Based on LTE System and Variable Channels