Ultralight Signal Classification Model for Automatic Modulation Recognition
This addresses the need for efficient signal classification on edge devices, though it appears incremental as it builds on existing models with optimizations for edge deployment.
The paper tackled the problem of automatic modulation recognition for radar signals on resource-constrained edge devices by proposing an ultralight hybrid neural network, achieving a mean accuracy of 96.3% at 0 dB SNR with less than 100 samples per class and reduced computational overhead.
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.