We are pleased to announce that our paper, “SMoRFFI: A Large-Scale Same-Model 2.4 GHz Wi-Fi Dataset and Reproducible Framework for RF Fingerprinting”, has been officially published in Computer Networks (2026).
Overview
Radio frequency (RF) fingerprinting has emerged as a promising approach for device identification by exploiting hardware imperfections at the physical layer. However, identifying same-model devices remains an open and challenging problem, as such devices exhibit highly similar RF characteristics.
To address this challenge, we present SMoRFFI, a large-scale dataset and a fully reproducible experimental framework specifically designed for same-model RF fingerprinting.
What We Provide
- A large-scale same-model dataset:
- 123 commercial IEEE 802.11g devices
- 35.42 million raw I/Q samples from Wi-Fi preambles
- 1.85 million extracted RF features
- A rich feature representation:
- Frequency-related features (CFO, coarse/fine CFO)
- Constellation-based features (phase error, magnitude error)
- Hardware-related features (I/Q imbalance, fractal dimension
- A fully reproducible pipeline:
- Data collection (USRP + GNU Radio)
- Feature extraction
- Benchmark evaluation
Key Insights
- Same-model RF fingerprinting is significantly more difficult than heterogeneous-device scenarios due to minimal hardware variation
- Frequency-related features (e.g., CFO) are the most discriminative for device identification
- Even with a lightweight model, our baseline achieves 88.6% accuracy, highlighting both:
- the difficulty of the dataset, and
- the potential for more advanced models
Why This Work Matters
Unlike existing datasets, SMoRFFI:
- Focuses on the worst-case scenario (same-model devices)
- Provides both raw I/Q data and pre-extracted RF features
- Ensures full reproducibility with open-source tools
This makes it a strong benchmark for:
- RF-based authentication
- Open-set / unknown device detection
- Machine learning and deep learning on RF signals
Resources
Citation
Guo, Z., Jia, Z., Zhu, J., Huang, W., & Chen, Y. (2026).
SMoRFFI: A large-scale same-model 2.4 GHz Wi-Fi dataset and reproducible framework for RF fingerprinting.
Computer Networks, 112309.
We hope this work provides a solid foundation for future research in RF fingerprinting and secure wireless device identification.