# Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model

arXiv: https://arxiv.org/abs/2503.05514 · HTML: https://mlsanity.com/paper/2503.05514/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion](https://mlsanity.com/paper/2404.09140/) (2024)
- [Wireless Physical-Layer Identification: Modeling and Validation](https://mlsanity.com/paper/1510.08485/) (2015)
- [Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform](https://mlsanity.com/paper/2010.10233/) (2020)
- [Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2207.02999/) (2022)
- [Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2207.03001/) (2022)
- [Towards Channel-Resilient CSI-Based RF Fingerprinting using Deep Learning](https://mlsanity.com/paper/2403.15739/) (2024)

## Built on by (most cited first, 2 of 2)

- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning](https://mlsanity.com/paper/2507.01976/) (2025)

## Abstract (arXiv)

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%.