Automatic Modulation Classification for Short Burst Underwater Acoustic Communication Signals Based on Hybrid Neural Networks

Automatic Modulation Classification for Short Burst Underwater Acoustic Communication Signals Based on Hybrid Neural Networks

Automatic modulation classification (AMC) is challenging for short burst underwater acoustic (UWA) communication signals. Difficulties include but are not limited to the poor UWA channels, impulsive noise, and data scarcity. To address these problems, a method based on hybrid neural networks (HNNs)...

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Bibliographic Details
Main Authors: Yongbin Li, Bin Wang, Gaoping Shao, Shuai Shao
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Automatic modulation classification
hybrid neural networks
attention aided convolutional neural networks
sparse auto-encoder
data-reusing
transfer learning
Online Access:https://ieeexplore.ieee.org/document/9300129/

Internet

https://ieeexplore.ieee.org/document/9300129/

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