Underwater Acoustic Rapidly Time-Varying Channel Equalization Technique Integrating Deep Learning and Domain Knowledge

DENG Ke; WANG Shuaijun; YU Hua; ZHANG Jian; CHEN Junfan; WU Zhouping

doi:10.11993/j.issn.2096-3920.2024-0163

Volume 33 Issue 2

May 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(2): 299-306

DENG Ke, WANG Shuaijun, YU Hua, ZHANG Jian, CHEN Junfan, WU Zhouping. Underwater Acoustic Rapidly Time-Varying Channel Equalization Technique Integrating Deep Learning and Domain Knowledge[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 299-306. doi: 10.11993/j.issn.2096-3920.2024-0163

Citation:

DENG Ke, WANG Shuaijun, YU Hua, ZHANG Jian, CHEN Junfan, WU Zhouping. Underwater Acoustic Rapidly Time-Varying Channel Equalization Technique Integrating Deep Learning and Domain Knowledge[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 299-306. doi: 10.11993/j.issn.2096-3920.2024-0163

Citation:

DENG Ke, WANG Shuaijun, YU Hua, ZHANG Jian, CHEN Junfan, WU Zhouping. Underwater Acoustic Rapidly Time-Varying Channel Equalization Technique Integrating Deep Learning and Domain Knowledge[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 299-306. doi: 10.11993/j.issn.2096-3920.2024-0163

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Underwater Acoustic Rapidly Time-Varying Channel Equalization Technique Integrating Deep Learning and Domain Knowledge

doi: 10.11993/j.issn.2096-3920.2024-0163

DENG Ke^{1, 2
,},
WANG Shuaijun^{1, 2
,
,},
YU Hua^{1, 3
,},
ZHANG Jian²,
CHEN Junfan^{1, 2
,},
WU Zhouping^2
,

1.
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510000, China
2.
Exploration of Future Technology Laboratory, Guangzhou Haige Communications Group Incorporated Company, Guangzhou 510000, China
3.
Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China

Received Date: 2024-12-11
Accepted Date: 2025-02-17
Rev Recd Date: 2025-02-06

Available Online: 2025-03-10

Abstract

Abstract

Multicarrier communication schemes, such as orthogonal frequency division multiplexing(OFDM), are the mainstream solutions for achieving high spectral efficiency in underwater acoustic transmissions. These schemes effectively address frequency-selective fading caused by multipath acoustic propagation in underwater environments. However, in rapidly time-varying scenarios, inter-carrier interference(ICI) can severely compromise transmission reliability. To mitigate the time-frequency doubly-selective fading in such underwater acoustic rapidly time-varying channels and reduce the bit error rate(BER) of OFDM systems, this paper proposed an underwater acoustic rapidly time-varying channel equalization method that combined deep learning with domain knowledge. Different from regarding the outcomes of traditional channel estimation and equalization detection as preprocessing results or supplementary information sources for deep neural networks(DNNs), this paper employed the structured information from classical frequency-domain equalization models to assist in the training and inference of DNN models, so as to counteract the adverse effects of ICI and adapt to scenarios where there is a mismatch between the actual deployment channel environment and the training channel environment. Numerical simulation and sea trial results confirm that the proposed approach can effectively reduce the BER of receivers and achieve faster model convergence, and the potential to achieve stronger generalization performance under unknown channel conditions.
- underwater acoustic communication,
- rapidly time-varying channel,
- deep learning,
- orthogonal frequency division multiplexing

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References(23)

References

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