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首页> 外文期刊>Signal processing >Channel prediction based temporal multiple sparse bayesian learning for channel estimation in fast time-varying underwater acoustic OFDM communications
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Channel prediction based temporal multiple sparse bayesian learning for channel estimation in fast time-varying underwater acoustic OFDM communications

机译：基于信道预测的时间多稀疏贝叶斯学习，用于快速时变水下声学OFDM通信中的信道估计

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摘要

In recent years, the multi-blocks joint channel estimation methods for underwater acoustic (UWA) OFDM systems have been proposed to improve the performance by using time correlation across consecutive OFDM blocks. However, the assumption of common sparse support (multi-path delays are invariant across consecutive blocks) in these methods is difficult to be met in fast time-varying channels. Therefore, we propose a channel prediction based joint channel estimation method for UWA fast time-varying channels, where multi-path delays and gains change significantly across consecutive OFDM blocks. Firstly we define a channel offset parameters model by the clustering property of UWA channels, and adopt Orthogonal Matching Pursuit (OMP) algorithm to estimate the channel offset parameters. Then we reconstruct a virtual current received signal based on the prediction method. Finally we combine the virtual received signal and the actual one into a joint estimation model, and utilize temporal multiple sparse Bayesian learning (TMSBL) method to jointly estimate the channel. Results of simulations and sea trial demonstrate the effectiveness of the proposed method in fast time-varying UWA channels, which achieves better performance than both the TMSBL method which is based on the joint processing of multi-blocks, and the OMP method which is based on block-by-block processing.
机译：近年来，已经提出了用于水下声学（UWA）OFDM系统的多块联合通道估计方法，以通过使用连续OFDM块的时间相关性来改善性能。然而，在快速时变通道中难以满足在这些方法中难以满足常见稀疏支持的假设（多路径延迟在连续块中不变）。因此，我们提出了一种基于频道预测的基于信道预测的联合信道估计方法，用于UWA快速时变信道，其中多路径延迟和增益在连续的OFDM块中显着变化。首先，我们通过UWA通道的群集属性来定义信道偏移参数模型，并采用正交匹配追求（OMP）算法来估计信道偏移参数。然后我们基于预测方法重建虚拟电流接收信号。最后，我们将虚拟接收信号和实际一个结合到联合估计模型中，并利用时间多个稀疏贝叶斯学习（TMSBL）方法联合估计信道。仿真和海上试验结果证明了所提出的方法在快速时变UWA通道中的有效性，这实现了比基于多块的关节处理的TMSBL方法更好的性能，以及基于的OMP方法逐块处理。

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  • 来源
    《Signal processing》 |2020年第10期|107668.1-107668.9|共9页
  • 作者

    Gang Qiao; Qingjun Song; Lu Ma; Zongxin Sun; Jiarong Zhang;

  • 作者单位

    Acoustic Science and Technology Laboratory Harbin Engineering University Harbin 150001 China Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Ministry of Industry and Information Technology Harbin 150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin 150001 China;

    Acoustic Science and Technology Laboratory Harbin Engineering University Harbin 150001 China Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Ministry of Industry and Information Technology Harbin 150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin 150001 China;

    Acoustic Science and Technology Laboratory Harbin Engineering University Harbin 150001 China Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Ministry of Industry and Information Technology Harbin 150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin 150001 China;

    Acoustic Science and Technology Laboratory Harbin Engineering University Harbin 150001 China Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Ministry of Industry and Information Technology Harbin 150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin 150001 China;

    Systems Engineering Research Institute Beijing 100000 China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Time-varying UWA channels; Channel prediction; Sparse channel estimation; Temporal multiple sparse bayesian learning; Orthogonal frequency division multiplexing;

    机译：时变的UWA频道;信道预测;稀疏信道估计;颞倍稀疏的贝叶斯学习;正交频分复用;

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