Securing Spectrum Connectivity: Over-the-Air Authentication Using Radio Frequency Fingerprinting

Overview

  • Funder: EPSRC
  • Duration: July 2024 to March 2025
  • Amount: £331k (Total). Liverpool share: £128k
  • Partner: Heriot Watt University and Queen’s University Belfast

This project was awarded under EPSRC Federated Telecoms Hub 6G Research Partnership Funds (THRPF), as a collaborative project with HASC: Future Communications Hub in All-Spectrum Connectivity.

Introduction

Wireless communications have brought revolutionary transformation to our everyday lives, industry, etc, achieved by ubiquitous connectivity of massive wireless devices (21.7 billion in 2024) and exciting applications, e.g., smart homes, connected healthcare, autonomous driving, etc. The wireless infrastructure is traditionally protected by cryptography, which, however, faces difficulties to be applied to devices where cost and accessibility are limiting factors.

Radio frequency fingerprinting identification (RFFI) is an emerging non-cryptographic and physical-layer security technique to secure existing and future telecommunication infrastructure. It exploits unique and intrinsic hardware fingerprints of RF components for device identification, which does not rely on cryptography. Its implementation does not require any change to the transmitters and can be readily applied to existing and future radio communication systems.

Demo

Please visit Wi-Fi RFFI @ University of Liverpool for a detailed introduction of our demo.

Click the image below to watch the demo video. WiFI RFFI Demo

Outcome

  1. 2025
    G. Yin, J. Zhang, Y. Ding, and S. Cotton, “Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model,” in Proc. IEEE Wireless Communications and Networking Conference Workshops, 2025, pp. 1–6. doi: 10.1109/wcnc61545.2025.10978824.
    DOI arXiv 
    @inproceedings{yin2025noise,
  title = {Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model},
  author = {Yin, Guolin and Zhang, Junqing and Ding, Yuan and Cotton, Simon},
  booktitle = {Proc. IEEE  Wireless Communications and Networking Conference Workshops},
  pages = {1--6},
  year = {2025},
  doi = {10.1109/wcnc61545.2025.10978824},
  arxiv = {2503.05514},
  keywords = {rffi,wifi}
}
  2. 2025
    J. Ma, J. Zhang, G. Shen, L. Peng, and A. Marshall, “Towards Channel-Robust Radio Frequency Fingerprint Identification Using Contrastive Learning,” in Proc. IEEE Wireless Communications and Networking Conference, 2025, pp. 1–6. doi: 10.1109/wcnc61545.2025.10978330.
    DOI 
    @inproceedings{ma2025towards,
  title = {Towards Channel-Robust Radio Frequency Fingerprint Identification Using Contrastive Learning},
  author = {Ma, Jie and Zhang, Junqing and Shen, Guanxiong and Peng, Linning and Marshall, Alan},
  booktitle = {Proc. IEEE  Wireless Communications and Networking Conference},
  pages = {1--6},
  year = {2025},
  doi = {10.1109/wcnc61545.2025.10978330},
  keywords = {rffi,lora}
}
  3. 2025
    N. Yuan, J. Zhang, Y. Ding, and S. L. Cotton, “Robust Radio Frequency Fingerprint Identification for Bluetooth Low Energy under Low SNR and Channel Variations,” in Proc. IEEE Wireless Communications and Networking Conference, 2025, pp. 01–06. doi: 10.1109/wcnc61545.2025.10978258.
    DOI 
    @inproceedings{yuan2025robust,
  title = {Robust Radio Frequency Fingerprint Identification for {Bluetooth} Low Energy under Low SNR and Channel Variations},
  author = {Yuan, Ningze and Zhang, Junqing and Ding, Yuan and Cotton, Simon L},
  booktitle = {Proc. IEEE Wireless Communications and Networking Conference},
  pages = {01--06},
  year = {2025},
  doi = {10.1109/wcnc61545.2025.10978258},
  keywords = {rffi,ble}
}
  4. 2024
    T. T. An, S. L. Cotton, J. Zhang, Y. Ding, and T. Q. Duong, “LoRa Radio Frequency Fingerprinting Using a Hybrid Quantum-Classical Neural Network,” in Proc. IEEE 100th Vehicular Technology Conference, 2024.
    @inproceedings{an2024lora,
  title = {{LoRa} Radio Frequency Fingerprinting Using a Hybrid Quantum-Classical Neural Network},
  author = {An, To Truong and Cotton, Simon L and Zhang, Junqing and Ding, Yuan and Duong, Trung Q},
  booktitle = {Proc. IEEE 100th Vehicular Technology Conference},
  year = {2024},
  keywords = {rffi,lora}
}