# Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case

arXiv: https://arxiv.org/abs/2201.02213 · HTML: https://mlsanity.com/paper/2201.02213/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks](https://mlsanity.com/paper/1812.01124/) (2018)
- [A Survey on LoRa Networking: Research Problems, Current Solutions and Open Issues](https://mlsanity.com/paper/1908.10195/) (2019)
- [Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification](https://mlsanity.com/paper/2105.02755/) (2021)
- [Leveraging Hardware-Impaired Out-of-Band Information Through Deep Neural Networks for Robust Wireless Device Classification](https://mlsanity.com/paper/2004.11126/) (2020)
- [Security Issues of Low Power Wide Area Networks in the Context of LoRa Networks](https://mlsanity.com/paper/2006.16554/) (2020)

## Built on by (most cited first, 6 of 6)

- [Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes](https://mlsanity.com/paper/2208.14964/) (2022)
- [Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/2310.16406/) (2023)
- [Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints](https://mlsanity.com/paper/2211.07687/) (2022)
- [ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation](https://mlsanity.com/paper/2301.12360/) (2023)
- [HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication](https://mlsanity.com/paper/2305.09594/) (2023)
- [On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments](https://mlsanity.com/paper/2507.19653/) (2025)

## Abstract (arXiv)

Deep learning-based RF fingerprinting has recently been recognized as a potential solution for enabling newly emerging wireless network applications, such as spectrum access policy enforcement, automated network device authentication, and unauthorized network access monitoring and control. Real, comprehensive RF datasets are now needed more than ever to enable the study, assessment, and validation of newly developed RF fingerprinting approaches. In this paper, we present and release a large-scale RF fingerprinting dataset, collected from 25 different LoRa-enabled IoT transmitting devices using USRP B210 receivers. Our dataset consists of a large number of SigMF-compliant binary files representing the I/Q time-domain samples and their corresponding FFT-based files of LoRa transmissions. This dataset provides a comprehensive set of essential experimental scenarios, considering both indoor and outdoor environments and various network deployments and configurations, such as the distance between the transmitters and the receiver, the configuration of the considered LoRa modulation, the physical location of the conducted experiment, and the receiver hardware used for training and testing the neural network models.