ML-based Joint Doppler Estimation and Compensation in Underwater Acoustic Communications
ML-based Joint Doppler Estimation and Compensation in Underwater Acoustic Communications
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DOI：
10.1145/3567600.3568139
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Yung-Ting Hsieh;Zhuoran Qi;D. Pompili
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文献类型：
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作者：
Yung-Ting Hsieh;Zhuoran Qi;D. Pompili
近年来，随着机器学习（ML）的快速发展，越来越多的技术问题，通常是基于模型的解决方案，有机会解决数据驱动的解决方案。针对水下多普勒效应，提出了一种基于模型的水下多普勒运动跟踪方法，并对多径多普勒效应在通信中的干扰进行了补偿。然而，过于复杂的模型，恶劣的水下条件下，导致大量的计算，成为实时多普勒补偿的障碍。在本研究中，我们采用ML技术来解决水下多普勒问题。我们提出了ML为基础的跟踪和跟踪辅助ML为基础的补偿。结果表明，在不同功率比的双优势路径条件下，采用细树、线性支持向量机、二次型支持向量机和三次型支持向量机，联合跟踪补偿方法具有较好的抽头选择精度。
With the rapid growth of Machine Learning (ML) in recent years, more and more technical issues, which were usually solved by model-based solutions, have an opportunity to be solved with data driven solutions. Underwater Doppler effect was tackled with model-based solutions in tracking the motion and compensating the interference caused by multipath Doppler effect in communications. However, a too complex model for the harsh underwater conditions leads to massive computation and becomes an obstacle for the real-time Doppler compensation. In this research, we adopt ML techniques to solve underwater Doppler issues. We propose ML-based tracking and a tracking-aid ML-based compensation. The results show that joint tracking and compensation method have tap choosing accuracy , , and in different power ratios of the two-dominant path condition with fine tree, linear Support Vector Machine (SVM), quadratic SVM and cubic SVM.