Wang Diya, Zhang Yonglin, Tai Yupeng, Wu Lixin, Wang Haibin, Wang Jun, Luo Wenyu, Meriaudeau Fabrice, Yang Fan
State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China.
Institut de Chimie Moléculaire, Unité Mixte de Recherche, Centre National de la Recherche Scientifique 6302, University of Burgundy, 21078 Dijon, France.
J Acoust Soc Am. 2023 Sep 1;154(3):1757-1769. doi: 10.1121/10.0020861.
In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.
在水下声学（UWA）通信中，信道通常呈现出簇稀疏结构，其中大部分信道冲激响应接近于零，只有少数非零抽头聚集形成簇。已有几种算法利用UWA信道的时域稀疏特性来降低信道估计的复杂度并提高准确性。利用簇结构来增强信道估计性能提供了另一个有前景的研究方向。在这项工作中，提出了一种基于深度学习的UWA正交频分复用（OFDM）系统信道估计方法，该方法利用了簇结构信息。首先，引入了一种基于卷积神经网络的簇检测模型来检测UWA信道的簇。该方法在准确性和鲁棒性方面优于传统的Page测试算法，特别是在低信噪比条件下。基于簇检测模型，提出了一种簇感知分布式压缩感知信道估计方法，该方法通过利用相邻OFDM符号之间的联合稀疏性并限制信道延迟扩展的搜索空间来减少噪声引起的误差。提供了数值模拟和海试结果，以说明所提方法与现有稀疏UWA信道估计方法相比的优越性能。
