Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design of hand-crafted expert features. With the intui...

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Veröffentlicht in:	arXiv.org 2021-01
Hauptverfasser:	Liao, Kaisheng, Zhao, Yaodong, Gu, Jie, Zhang, Yaping, Zhong, Yi
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Classification Comparative studies Computer architecture Computer Science - Learning Distillation Modulation Neural networks Noise levels Quadratures Recurrent neural networks Signal to noise ratio
Online-Zugang:	Volltext
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