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Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model

Guolin Yin, Junqing Zhang, Yuan Ding, Simon Cotton

TL;DR

This paper tackles the vulnerability of radio frequency fingerprint identification (RFFI) to low SNR by introducing a diffusion-model–based denoising framework. It trains a noise predictor based on a modified Hierarchical Diffusion Transformer and couples it with an SNR-mapped denoising strategy to recover underlying RFF features, followed by a Transformer-based classifier. The approach, validated on a Wi‑Fi testbed with six DUTs and USRP N210, yields up to 34.9% improvement in identification accuracy at 0 dB, highlighting substantial robustness gains in noisy wireless environments. The work advances practical RFFI for resource-constrained IoT by enabling reliable authentication under adverse channel conditions and demonstrates a viable path toward real-time, diffusion-driven denoising in wireless security systems.

Abstract

Securing Internet of Things (IoT) devices presents increasing challenges due to their limited computational and energy resources. Radio Frequency Fingerprint Identification (RFFI) emerges as a promising authentication technique to identify wireless devices through hardware impairments. RFFI performance under low signal-to-noise ratio (SNR) scenarios is significantly degraded because the minute hardware features can be easily swamped in noise. In this paper, we leveraged the diffusion model to effectively restore the RFF under low SNR scenarios. Specifically, we trained a powerful noise predictor and tailored a noise removal algorithm to effectively reduce the noise level in the received signal and restore the device fingerprints. We used Wi-Fi as a case study and created a testbed involving 6 commercial off-the-shelf Wi-Fi dongles and a USRP N210 software-defined radio (SDR) platform. We conducted experimental evaluations on various SNR scenarios. The experimental results show that the proposed algorithm can improve the classification accuracy by up to 34.9%.

Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model

TL;DR

This paper tackles the vulnerability of radio frequency fingerprint identification (RFFI) to low SNR by introducing a diffusion-model–based denoising framework. It trains a noise predictor based on a modified Hierarchical Diffusion Transformer and couples it with an SNR-mapped denoising strategy to recover underlying RFF features, followed by a Transformer-based classifier. The approach, validated on a Wi‑Fi testbed with six DUTs and USRP N210, yields up to 34.9% improvement in identification accuracy at 0 dB, highlighting substantial robustness gains in noisy wireless environments. The work advances practical RFFI for resource-constrained IoT by enabling reliable authentication under adverse channel conditions and demonstrates a viable path toward real-time, diffusion-driven denoising in wireless security systems.

Abstract

Securing Internet of Things (IoT) devices presents increasing challenges due to their limited computational and energy resources. Radio Frequency Fingerprint Identification (RFFI) emerges as a promising authentication technique to identify wireless devices through hardware impairments. RFFI performance under low signal-to-noise ratio (SNR) scenarios is significantly degraded because the minute hardware features can be easily swamped in noise. In this paper, we leveraged the diffusion model to effectively restore the RFF under low SNR scenarios. Specifically, we trained a powerful noise predictor and tailored a noise removal algorithm to effectively reduce the noise level in the received signal and restore the device fingerprints. We used Wi-Fi as a case study and created a testbed involving 6 commercial off-the-shelf Wi-Fi dongles and a USRP N210 software-defined radio (SDR) platform. We conducted experimental evaluations on various SNR scenarios. The experimental results show that the proposed algorithm can improve the classification accuracy by up to 34.9%.

Paper Structure

This paper contains 26 sections, 8 equations, 8 figures.

Table of Contents

  1. Introduction
  2. System Overview
  3. Model Training Stage
  4. Diffusion Model Training
  5. Classifier Training
  6. Inference Stage
  7. Restore RFF Feature with Diffusion Model
  8. Diffusion Model Training
  9. Forward Process
  10. Reverse Process
  11. Noise Removal
  12. Classifier Design
  13. Noise Augmentation
  14. SNR Mapping
  15. Classifier Model Structure
  16. ...and 11 more sections

Figures (8)

  • Figure 1: System overview. (a) Model training stage. (b) Inference stage.
  • Figure 2: (a) The forward and reverse process. (b) The structure of noise predictor.
  • Figure 3: (a) The proposed Transformer-based classifier structure. (b) The temporal encoder. (c) The class encoder. (d) Backbone transformer encoder.
  • Figure 4: The experiment devices. (a) USRP N210. (b) TP-Link USB dongle.
  • Figure 5: The noise schedule during diffusion model training.
  • ...and 3 more figures