Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism

被引:0
作者
Zhengliang Zhu
Feng Tong
Yuehai Zhou
Ziqiao Zhang
Fumin Zhang
机构
[1] Xiamen University,National and Local Joint Engineering Research Center for Navigation and Location Service Technology
[2] Xiamen University,College of Ocean and Earth Sciences
[3] Georgia Institute of Technology,School of Electrical and Computer Engineering
[4] Hong Kong University of Science and Technology,Cheng Kar
来源
Journal of Marine Science and Application | 2023年 / 22卷
关键词
Long short-term memory (LSTM); Attention mechanism; Underwater acoustic communication; Underwater acoustic channel; Channel prediction;
D O I
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中图分类号
学科分类号
摘要
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.
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页码:650 / 658
页数:8
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