  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.
