Radio-Frequency Fingerprint Identification Dataset

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This page lists publicly available datasets and source code for RFFI research. We have validated only those provided by our group. Users should independently assess the quality and suitability of resources from other sources.

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

  1. 2021
    A. Al-Shawabka, P. Pietraski, S. B. Pattar, F. Restuccia, and T. Melodia, “DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation,” in Proceedings of the Twenty-Second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, 2021, pp. 251–260. doi: 10.1145/3466772.3467054.
    DOI Data 
    @inproceedings{alshawabka2021deeplora,
  author = {Al-Shawabka, Amani and Pietraski, Philip and Pattar, Sudhir B. and Restuccia, Francesco and Melodia, Tommaso},
  title = {DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation},
  year = {2021},
  doi = {10.1145/3466772.3467054},
  booktitle = {Proceedings of the Twenty-Second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing},
  pages = {251–260},
  numpages = {10},
  location = {Shanghai, China}
}

Wi-Fi Dataset

Southeast University

  1. 2025
    L. Xie, L. Peng, and J. Zhang, “Towards Robust RF Fingerprint Identification Using Spectral Regrowth and Carrier Frequency Offset,” in Proc. IEEE Conference on Computer Communications (INFOCOM), 2025, pp. 1–10. doi: 10.1109/infocom55648.2025.11044651.
    DOI arXiv Data 
    @inproceedings{xie2024towards,
  title = {Towards Robust {RF} Fingerprint Identification Using Spectral Regrowth and Carrier Frequency Offset},
  author = {Xie, Lingnan and Peng, Linning and Zhang, Junqing},
  booktitle = {Proc. IEEE Conference on Computer Communications (INFOCOM)},
  pages = {1--10},
  year = {2025},
  doi = {10.1109/infocom55648.2025.11044651},
  arxiv = {2412.07269},
  keywords = {rffi,wifi}
}

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

ZigBee

Southeast University

  1. 2025
    J. Shi, L. Peng, L. Xie, H. Fu, and A. Hu, “An SNR-Aware Feature Reconstruction Method in Radio Frequency Fingerprint Identification,” IEEE Transactions on Information Forensics and Security, vol. 20, pp. 7895–7910, 2025, doi: 10.1109/TIFS.2025.3592567.
    DOI Data 
    @article{shi2025snr,
  author = {Shi, Junxian and Peng, Linning and Xie, Lingnan and Fu, Hua and Hu, Aiqun},
  journal = {IEEE Transactions on Information Forensics and Security},
  title = {An SNR-Aware Feature Reconstruction Method in Radio Frequency Fingerprint Identification},
  year = {2025},
  volume = {20},
  number = {},
  pages = {7895-7910},
  doi = {10.1109/TIFS.2025.3592567}
}
  2. 2023
    J. Shi, L. Peng, H. Fu, and A. Hu, “Robust RF Fingerprint Extraction Based on Cyclic Shift Characteristic,” IEEE Internet of Things Journal, vol. 10, no. 21, pp. 19218–19233, 2023, doi: 10.1109/JIOT.2023.3281644.
    DOI Data 
    @article{shi2023robust,
  author = {Shi, Junxian and Peng, Linning and Fu, Hua and Hu, Aiqun},
  journal = {IEEE Internet of Things Journal},
  title = {Robust RF Fingerprint Extraction Based on Cyclic Shift Characteristic},
  year = {2023},
  volume = {10},
  number = {21},
  pages = {19218-19233},
  doi = {10.1109/JIOT.2023.3281644}
}

LTE

Southeast University

  1. 2024
    L. Peng, Z. Wu, J. Zhang, M. Liu, H. Fu, and A. Hu, “Hybrid RFF Identification for LTE Using Wavelet Coefficient Graph and Differential Spectrum,” IEEE Transactions on Vehicular Technology, vol. 73, no. 8, pp. 11621–11636, 2024, doi: 10.1109/tvt.2024.3380671.
    DOI Data 
    @article{peng2024hybrid,
  title = {Hybrid {RFF} Identification for {LTE} Using Wavelet Coefficient Graph and Differential Spectrum},
  author = {Peng, Linning and Wu, Zhenni and Zhang, Junqing and Liu, Ming and Fu, Hua and Hu, Aiqun},
  journal = {IEEE Transactions on Vehicular Technology},
  volume = {73},
  number = {8},
  pages = {11621 - 11636},
  year = {2024},
  doi = {10.1109/tvt.2024.3380671},
  keywords = {rffi,lte}
}

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

Oregon State University

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