A Real-World Radio Frequency Signal Dataset Based on LTE System and Variable Channels
Shupeng Zhang, Yibin Zhang, Xixi Zhang, Jinlong Sun, Yun Lin, Haris, Gacanin, Fumiyuki Adachi, and Guan Gui

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…
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Taxonomy
TopicsWireless Signal Modulation Classification · Hate Speech and Cyberbullying Detection · Speech Recognition and Synthesis
