Digital Signal Modulation Classification With Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks

Digital Signal Modulation Classification With Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks

Automated modulation classification plays a very important part in cognitive radio networks. Deep learning is also a powerful tool that we could not overlook its potential in addressing signal modulation recognition problem. In our last work, we propose a new data conversion algorithm in order to ga...

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Bibliographic Details
Main Authors: Bin Tang, Ya Tu, Zhaoyue Zhang, Yun Lin
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Cognitive radio
modulation recognition
pattern recognition
classification algorithms
deep learning
convolutional networks
Online Access:https://ieeexplore.ieee.org/document/8319926/

Internet

https://ieeexplore.ieee.org/document/8319926/

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