# A Generic Machine Learning Framework for Radio Frequency Fingerprinting

arXiv: https://arxiv.org/abs/2510.09775 · HTML: https://mlsanity.com/paper/2510.09775/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 75 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 27 of 27)

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- [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)
- [Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques](https://mlsanity.com/paper/1901.07703/) (2019)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Radio Frequency Fingerprint Identification Based on Denoising Autoencoders](https://mlsanity.com/paper/1907.08809/) (2019)
- [Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2411.03636/) (2024)
- [A Survey of Machine Learning-based Physical-Layer Authentication in Wireless Communications](https://mlsanity.com/paper/2411.09906/) (2024)
- [Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning](https://mlsanity.com/paper/2207.06592/) (2022)
- [On Equivalence and Cores for Incomplete Databases in Open and Closed Worlds](https://mlsanity.com/paper/2001.04757/) (2020)

## Abstract (arXiv)

Fingerprinting radio frequency (RF) emitters typically involves finding unique characteristics that are featured in their received signal. These fingerprints are nuanced, but sufficiently detailed, motivating the pursuit of methods that can successfully extract them. The downstream task that requires the most meticulous RF fingerprinting (RFF) is known as specific emitter identification (SEI), which entails recognising each individual transmitter. RFF and SEI have a long history, with numerous defence and civilian applications such as signal intelligence, electronic surveillance, physical-layer authentication of wireless devices, to name a few. In recent years, data-driven RFF approaches have become popular due to their ability to automatically learn intricate fingerprints. They generally deliver superior performance when compared to traditional RFF techniques that are often labour-intensive, inflexible, and only applicable to a particular emitter type or transmission scheme. In this paper, we present a generic and versatile machine learning (ML) framework for data-driven RFF with several popular downstream tasks such as SEI, data association (EDA) and RF emitter clustering (RFEC). It is emitter-type agnostic. We then demonstrate the introduced framework for several tasks using real RF datasets for spaceborne surveillance, signal intelligence and countering drones applications.