Radio frequency (RF) signal recognition plays a critical role in modern wireless communication and security applications. Deep learning-based approaches have achieved strong performance but typically rely heavily on extensive training data and often fail to generalize to unseen signals. In this paper, we propose a method to learn discriminative embeddings without relying on real-world RF signal recordings by training on signals of synthetic wireless protocols. We validate the approach on a dataset of real RF signals and show that the learned embeddings capture features enabling accurate discrimination of previously unseen real-world signals, highlighting its potential for robust RF signal classification and anomaly detection.
@article{arxiv.2510.23186,
title = {Approaching Domain Generalization with Embeddings for Robust Discrimination and Recognition of RF Communication Signals},
author = {Lukas Henneke and Frank Kurth},
journal= {arXiv preprint arXiv:2510.23186},
year = {2025}
}