The growing complexity of radar signals demands responsive and accurate detection systems that can operate efficiently on resource-constrained edge devices. Existing models, while effective, often rely on substantial computational resources and large datasets, making them impractical for edge deployment. In this work, we propose an ultralight hybrid neural network optimized for edge applications, delivering robust performance across unfavorable signal-to-noise ratios (mean accuracy of 96.3% at 0 dB) using less than 100 samples per class, and significantly reducing computational overhead.
@article{arxiv.2412.19585,
title = {Ultralight Signal Classification Model for Automatic Modulation Recognition},
author = {Alessandro Daniele Genuardi Oquendo and Agustín Matías Galante Cerviño and Nilotpal Kanti Sinha and Luc Andrea and Sam Mugel and Román Orús},
journal= {arXiv preprint arXiv:2412.19585},
year = {2024}
}