Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

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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Bibliographic Details
Main Authors: Kaisheng Liao, Yaodong Zhao, Jie Gu, Yaping Zhang, Yi Zhong
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Automatic modulation classification
convolutional neural networks
cognitive radio
deep learning
recurrent neural networks
spectrum monitoring
Online Access:https://ieeexplore.ieee.org/document/9330604/

Internet

https://ieeexplore.ieee.org/document/9330604/

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