轻量级模型在低资源下实现高精度调制识别
Ultralight Signal Classification Model for Automatic Modulation Recognition
- 设计轻量级混合神经网络，适配边缘设备
- 仅需每类100样本，信噪比0dB时准确率达96.3%
- 计算开销极低，适合实际部署
雷达信号日益复杂，亟需高效响应的检测系统，以在资源受限的边缘设备上运行。现有模型虽有效，但通常依赖大量计算资源和数据集，难以部署于边缘端。本文提出一种专为边缘应用优化的超轻量级混合神经网络，在信噪比为0 dB时平均准确率达96.3%，每类仅需少于100个样本即可实现鲁棒性能，并显著降低计算开销。
原文摘要 · Abstract (English)
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.
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