{"ID":2849565,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23186","arxiv_id":"2510.23186","title":"Approaching Domain Generalization with Embeddings for Robust Discrimination and Recognition of RF Communication Signals","abstract":"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.","short_abstract":"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 meth...","url_abs":"https://arxiv.org/abs/2510.23186","url_pdf":"https://arxiv.org/pdf/2510.23186v1","authors":"[\"Lukas Henneke\",\"Frank Kurth\"]","published":"2025-10-27T10:24:45Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}