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Joint block estimation and attention-based long short-term memory network for doppler shift mitigation in UWA
Qingxi Zeng1,2, Tieliang Guo3,4, Guojin Peng2,5
1College of Computer and Electronic Information, Guangxi University, Nanning, 530004, China.
Scientific Reports
|February 27, 2026
Summary
This study introduces an Attention-based Long Short-Term Memory Network (ALSTM) algorithm to combat Doppler shift in underwater acoustic (UWA) communications. The novel method significantly improves performance by accurately estimating and mitigating Doppler effects, enhancing UWA system reliability.
Area of Science:
- Underwater Acoustic Communications
- Signal Processing
- Machine Learning
Background:
- Doppler shift significantly degrades Orthogonal Frequency Division Multiplexing (OFDM) system performance in underwater acoustic (UWA) environments.
- Accurate Doppler shift estimation is critical for reliable UWA communication systems.
Purpose of the Study:
- To propose a novel two-step algorithm for mitigating the Doppler effect in UWA OFDM systems.
- To enhance the accuracy and robustness of Doppler shift estimation in challenging UWA conditions.
Main Methods:
- A two-step algorithm combining block estimation with an Attention-based Long Short-Term Memory Network (ALSTM).
- Coarse Doppler factor estimation using linear frequency modulation (LFM) signals.
- Neural network-based carrier frequency offset (CFO) prediction on resampled data.
Main Results:
- The proposed ALSTM algorithm demonstrates superior performance over traditional methods and basic neural networks.
- Significant improvements observed in mean square error (MSE) and bit error rate (BER).
- Achieved high accuracy and robustness in Doppler shift estimation.
Conclusions:
- The developed algorithm offers a reliable solution for mitigating Doppler effects in complex UWA communication environments.
- ALSTM-based approach effectively addresses performance degradation caused by Doppler shifts in UWA OFDM systems.
