Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model

2025-03-07

AI Summary

To address the severe performance degradation of radio frequency fingerprinting identification (RFFI) under low signal-to-noise ratio (SNR) conditions—where hardware-specific RF signatures are easily obscured by noise—this paper proposes a robust RFFI method based on denoising diffusion probabilistic models. It introduces diffusion modeling to RFFI for the first time, designing a dedicated noise-prediction network and a progressive, signal-level denoising algorithm that directly reconstructs device-specific RF fingerprints from raw in-phase/quadrature (IQ) samples, bypassing conventional, noise-sensitive time-frequency feature extraction. Evaluated on Wi-Fi physical-layer signals using a testbed comprising USRP N210 software-defined radios and commercial Wi-Fi adapters, the method achieves up to a 34.9% improvement in device identification accuracy under low-SNR conditions, significantly outperforming traditional feature-engineering-plus-classification pipelines.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Hardware-aware ML
• Computer Vision: Diffusion Models for Vision

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

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%.

Problem

• Enhance Radio Frequency Fingerprint Identification in noisy environments.
• Improve IoT device security using hardware-based authentication techniques.
• Develop a noise reduction algorithm for better device fingerprint accuracy.

Innovation

• Utilized diffusion model for noise reduction
• Developed tailored noise removal algorithm
• Improved classification accuracy by 34.9%