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Citation:
　Zhengliang Zhu,Feng Tong,Yuehai Zhou,et al.Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism[J].Journal of Marine Science and Application,2023,(3):650-658.[doi:10.1007/s11804-023-00347-5]
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Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism

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Title:
Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism
Author(s):
Zhengliang Zhu1; 2;  Feng Tong1; 2;  Yuehai Zhou1; 2;  Ziqiao Zhang3;  Fumin Zhang4
Affilations:
Author(s):
Zhengliang Zhu1; 2;  Feng Tong1; 2;  Yuehai Zhou1; 2;  Ziqiao Zhang3;  Fumin Zhang4
1. National and Local Joint Engineering Research Center for Navigation and Location Service Technology, Xiamen University, Xiamen 361005, China;
2. College of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, China;
3. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta 30314, USA;
4. Cheng Kar-Shun Robotics Institute, Hong Kong University of Science and Technology, Hong Kong 00852, China
Keywords:
Long short-term memory (LSTM); Attention mechanism; Underwater acoustic communication; Underwater acoustic channel; Channel prediction
分类号:
-
DOI:
10.1007/s11804-023-00347-5
Abstract:
This paper investigates the channel prediction algorithm of the time-varying channels in underwater acoustic (UWA) communication systems using the long short-term memory (LSTM) model with the attention mechanism. AttLstmPreNet is a deep learning model that combines an attention mechanism with LSTM-type models to capture temporal information with different scales from historical UWA channels. The attention mechanism is used to capture sparsity in the time-delay scales and coherence in the gep-time scale under the LSTM framework. The soft attention mechanism is introduced before the LSTM to support the model to focus on the features of input sequences and help improve the learning capacity of the proposed model. The performance of the proposed model is validated using different simulation time-varying UWA channels. Compared with the adaptive channel predictors and the plain LSTM model, the proposed model is better in terms of channel prediction accuracy.

References:

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Memo

Memo:
Received date:2022-9-14;Accepted date:2022-11-22。
Foundation item:The authors would like to thank Dr. Xingbin Tu of the Ocean College, Zhejiang University, for his assistance in computing the temporal coherence of the UWA channel.
Corresponding author:Feng Tong,E-mail:ftong@xmu.edu.cn
Last Update: 2023-10-10