# Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2310.16406 · HTML: https://mlsanity.com/paper/2310.16406/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting](https://mlsanity.com/paper/2112.15363/) (2021)
- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case](https://mlsanity.com/paper/2201.02213/) (2022)
- [Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2207.03001/) (2022)
- [PAST-AI: Physical-layer Authentication of Satellite Transmitters via Deep Learning](https://mlsanity.com/paper/2010.05470/) (2020)
- [The Day-After-Tomorrow: On the Performance of Radio Fingerprinting over Time](https://mlsanity.com/paper/2305.05285/) (2023)

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

- [Edge AI-based Radio Frequency Fingerprinting for IoT Networks](https://mlsanity.com/paper/2412.10553/) (2024)
- [HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD](https://mlsanity.com/paper/2507.12133/) (2025)

## Abstract (arXiv)

Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Such RF transmitter imperfections are reflected into over-the-air signals, allowing receivers to accurately identify the RF transmitting source. Recent advances in Machine Learning, particularly in Deep Learning (DL), have improved the ability of RFF systems to extract and learn complex features that make up the device-specific fingerprint. However, integrating DL techniques with RFF and operating the system in real-world scenarios presents numerous challenges, originating from the embedded systems and the DL research domains. This paper systematically identifies and analyzes the essential considerations and challenges encountered in the creation of DL-based RFF systems across their typical development life-cycle, which include (i) data collection and preprocessing, (ii) training, and finally, (iii) deployment. Our investigation provides a comprehensive overview of the current open problems that prevent real deployment of DL-based RFF systems while also discussing promising research opportunities to enhance the overall accuracy, robustness, and privacy of these systems.