Ultralight Signal Classification Model for Automatic Modulation Recognition

Autor:	Oquendo, Alessandro Daniele Genuardi, Cerviño, Agustín Matías Galante, Sinha, Nilotpal, Andrea, Luc, Mugel, Sam, Orús, Román
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.19585 Zobrazit plný text záznamu View this record from Arxiv