# GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting

arXiv: https://arxiv.org/abs/2303.14312 · HTML: https://mlsanity.com/paper/2303.14312/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training](https://mlsanity.com/paper/2510.09405/) (2025)
- [Exploiting Radio Frequency Fingerprints for Device Identification: Tackling Cross-receiver Challenges in the Source-data-free Scenario](https://mlsanity.com/paper/2512.16648/) (2025)
- [HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD](https://mlsanity.com/paper/2507.12133/) (2025)
- [Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting](https://mlsanity.com/paper/2605.30364/) (2026)
- [Out-of-Distribution (OOD) Detectors for Open-Set RF Fingerprinting](https://mlsanity.com/paper/2606.12718/) (2026)

## Abstract (arXiv)

Radio frequency fingerprinting has been proposed for device identification. However, experimental studies also demonstrated its sensitivity to deployment changes. Recent works have addressed channel impacts by developing robust algorithms accounting for time and location variability, but the impacts of receiver impairments on transmitter fingerprints are yet to be solved. In this work, we investigate the receiver-agnostic transmitter fingerprinting problem, and propose a novel two-stage supervised learning framework (RXA) to address it. In the first stage, our approach calibrates a receiver-agnostic transmitter feature-extractor. We also propose two deep-learning approaches (SD-RXA and GAN-RXA) in this first stage to improve the receiver-agnostic property of the RXA framework. In the second stage, the calibrated feature-extractor is utilized to train a transmitter classifier with only one receiver. We evaluate the proposed approaches on the transmitter identification problem using a large-scale WiFi dataset. We show that when a trained transmitter-classifier is deployed on new receivers, the RXA framework can improve the classification accuracy by 19.5%, and the outlier detection ROC AUC by 12.0% compared to a naive approach without calibration. Moreover, GAN-RXA can further increase the closed-set classification accuracy by 5.0%, and the outlier detection ROC AUC by 12.5% compared to the RXA approach.