One-Dimensional Deep Attention Convolution Network (ODACN) for Signals Classification

One-Dimensional Deep Attention Convolution Network (ODACN) for Signals Classification

Handcraft features are commonly used for signal classification, which is a time-consuming feature engineering. In order to develop a general and robust feature learning method for radio signals, a novel One-dimensional Deep Attention Convolution Network (ODACN) is proposed to automatically extract d...

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Bibliographic Details
Main Authors: Shuyuan Yang, Chen Yang, Dongzhu Feng, Xiaoyang Hao, Min Wang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Signal classification
feature learning
one-dimensional convolution neural network
attention layer
Online Access:https://ieeexplore.ieee.org/document/8926472/

Internet

https://ieeexplore.ieee.org/document/8926472/

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