Radio-Frequency Fingerprint Identification Dataset

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This page lists publicly available datasets and source code for radio-frequency fingerprint identification research.

LoRa Dataset

University of Liverpool

  1. 2024
    G. Shen, J. Zhang, A. Marshall, R. Woods, J. Cavallaro, and L. Chen, “Towards receiver-agnostic and collaborative radio frequency fingerprint identification,” IEEE Transactions on Mobile Computing, vol. 23, no. 7, pp. 7618–7634, 2024, doi: 10.1109/tmc.2023.3340039.
    DOI arXiv Code Data 
    @article{shen2023towards,
  title = {Towards receiver-agnostic and collaborative radio frequency fingerprint identification},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Woods, Roger and Cavallaro, Joseph and Chen, Liquan},
  journal = {IEEE Transactions on Mobile Computing},
  volume = {23},
  number = {7},
  pages = {7618 - 7634},
  year = {2024},
  doi = {10.1109/tmc.2023.3340039},
  arxiv = {2207.02999},
  keywords = {rffi,lora, data_code}
}
  2. 2024
    G. Shen, J. Zhang, X. Wang, and S. Mao, “Federated Radio Frequency Fingerprint Identification Powered by Unsupervised Contrastive Learning,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 9204–9215, 2024, doi: 10.1109/tifs.2024.3469820.
    DOI Code Data 
    @article{shen2024federated,
  title = {Federated Radio Frequency Fingerprint Identification Powered by Unsupervised Contrastive Learning},
  author = {Shen, Guanxiong and Zhang, Junqing and Wang, Xuyu and Mao, Shiwen},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {19},
  pages = {9204 - 9215},
  year = {2024},
  doi = {10.1109/tifs.2024.3469820},
  keywords = {rffi, lora, data_code}
}
  3. 2023
    G. Shen, J. Zhang, A. Marshall, M. Valkama, and J. R. Cavallaro, “Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification,” IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2355–2367, 2023, doi: 10.1109/tifs.2023.3266626.
    DOI arXiv Code Data 
    @article{shen2023length,
  title = {Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Valkama, Mikko and Cavallaro, Joseph R},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {18},
  pages = {2355 - 2367},
  year = {2023},
  doi = {10.1109/tifs.2023.3266626},
  arxiv = {2207.03001},
  keywords = {rffi,lora, data_code}
}
  4. 2022
    G. Shen, J. Zhang, A. Marshall, and J. R. Cavallaro, “Towards scalable and channel-robust radio frequency fingerprint identification for LoRa,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 774–787, 2022, doi: 10.1109/tifs.2022.3152404.
    DOI arXiv Code Data 
    @article{shen2022scalable,
  title = {Towards scalable and channel-robust radio frequency fingerprint identification for {LoRa}},
  author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Cavallaro, Joseph R},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {17},
  pages = {774--787},
  year = {2022},
  doi = {10.1109/tifs.2022.3152404},
  arxiv = {2107.02867},
  keywords = {rffi,lora,data_code}
}

Northeastern University and InterDigital

Al-Shawabka, A., Pietraski, P., Pattar, S.B., Restuccia, F., & Melodia, T. (2021, July 26-29). DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation. Proc. 22nd International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Shanghai, China.

Oregon State University

The NetSTAR lab Oregon State University at Oregon State University has made a few datasets available, including LoRa and Wi-Fi datasets. Dataset Download Link

Wi-Fi Dataset

Oregon State University

The NetSTAR lab Oregon State University at Oregon State University has made a few datasets available, including LoRa and Wi-Fi datasets. Dataset Download Link

Drone Remote Controller RF Signal Dataset

North Carolina State University

  • M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, “Micro-UAV detection and classification from RF fingerprints using machine learning techniques,” in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
  • M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, “Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference,” IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
  • E. Ozturk, F. Erden, and I. Guvenc, “RF-based low-SNR classification of UAVs using convolutional neural networks.” arXiv preprint arXiv:2009.05519, Sept. 2020.
  • Dataset Download Link

ADS-B Dataset

Embry-Riddle Aeronautical University

Yongxin Liu, Jian Wang, Jianqiang Li, Shuteng Niu, and Houbing Song, “Class-Incremental Learning for Wireless Device Identification in IoT,” IEEE Internet of Things, vol. 8, no. 23, pp. 17227 - 17235, Dec. 2021.

GENESYS Lab at Northeastern University

GENESYS Lab at Northeastern University External resource has made several datasets available.