Modulation Recognition of Underwater Acoustic Signals Using Deep Hybrid Neural Networks-Reference-Cited by-同舟云学术

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Modulation Recognition of Underwater Acoustic Signals Using Deep Hybrid Neural Networks

Published:2022-08 Issue:8 Volume:21 Page:5977-5988
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Zhang Weilong¹^ORCID,Yang Xinghai¹,Leng Changli²,Wang Jingjing¹^ORCID,Mao Shiwen³^ORCID

Affiliation:

1. College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong, China

2. Qingdao Institute of Intelligent Navigation and Control, Qingdao, Shandong, China

3. Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA

Funder

National Natural Science Foundation of China

Major Basic Research Project of Natural Science Foundation of Shandong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7693/9854929/09694507.pdf?arnumber=9694507

Reference35 articles.

1. Urban flood susceptibility assessment based on convolutional neural networks

2. An Effective Optimization Method for Machine Learning Based on ADAM

3. Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (SGD)

4. Going deeper with convolutions

5. A review on the long short-term memory model

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3. One2ThreeNet: An Automatic Microscale-Based Modulation Recognition Method for Underwater Acoustic Communication Systems;IEEE Transactions on Wireless Communications;2024-08

4. Automatic modulation identification for underwater acoustic signals based on the space–time neural network;Frontiers in Marine Science;2024-07-04

5. Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique;Traitement du Signal;2024-06-26

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