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

TL;DR

This paper presents a customizable, real-world LTE RF signal dataset generated via software radio, capturing complex channel environments to improve deep learning-based RF fingerprinting security applications.

Contribution

It introduces a versatile LTE RF dataset with variable parameters and complex channels, addressing limitations of previous WiFi-based datasets for RF fingerprinting.

Findings

Dataset verified for reliability

Supports diverse channel conditions

Enables improved deep learning security models

Abstract

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.