Sparse Bayesian Learning for Channel Estimation in Time-Varying Underwater Acoustic OFDM Communication
Sparse Bayesian Learning for Channel Estimation in Time-Varying Underwater Acoustic OFDM Communication
复制标题
时变水声 OFDM 通信中信道估计的稀疏贝叶斯学习
DOI：
10.1109/access.2018.2873406
复制
发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Gan, Shuwei
中科院分区：
文献类型：
--
作者：
Qiao, Gang;Song, Qingjun;Gan, Shuwei
在本文中，我们研究了用于水声（UWA）正交频分复用（OFDM）通信系统中信道估计的稀疏贝叶斯学习（SBL）框架，与基于压缩感知（CS）的方法相比，该框架提供了防止结构错误的理想特性，并且稀疏信号重建的收敛误差更少。首先，我们设计了一个基于 SBL 的信道估计器，用于在每个块中独立使用信道稀疏结构进行逐块处理。然后，我们提出了用于多块联合处理的多普勒补偿后的联合信道模型，其中几个连续块的信道的延迟相似并且路径增益表现出时间相关性，并且我们表示路径增益的时间相关系数来评估相关性的强度。此外，我们提出了基于时间多重 SBL (TMSBL) 的信道估计器，通过利用连续 OFDM 块之间的信道相干性来联合估计信道。数值仿真和海试结果证明了SBL和TMSBL信道估计算法在时变UWA信道中的有效性，与现有的基于CS的方法（例如正交匹配追踪（OMP）和同时OMP）相比，取得了更好的信道估计性能和更低的误码率，特别是TMSBL估计器在强时间相关信道中实现了最佳性能，并在弱时间相关信道中保持了鲁棒性。
In this paper, we study the sparse Bayesian learning (SBL) framework for channel estimation in underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication systems, which provides a desirable property of preventing structural error with fewer convergence errors for sparse signal reconstruction compared with the compress sensing (CS)-based methods. First, we design a SBL-based channel estimator for block-by-block processing using the channel sparse structure independently in each block. Then, we propose a joint channel model after Doppler compensation for multi-block joint processing, where the delays of the channels for several consecutive blocks are similar and the path gains exhibit temporal correlation, and we denote a temporal correlation coefficient for path gains to evaluate the strength of the correlation. Furthermore, we propose the temporal multiple SBL (TMSBL)-based channel estimator to jointly estimate the channels by taking advantage of the channel coherence between consecutive OFDM blocks. Results of numerical simulation and sea trial demonstrate the effectiveness of the SBL and TMSBL channel estimator algorithms in time-varying UWA channel, which achieve better channel estimation performance and lower bit error rate compared with the existing CS-based methods, such as orthogonal matching pursuit (OMP) and simultaneous OMP, especially the TMSBL estimator achieves the best performance in strong temporal correlated channels and maintains robustness in weak temporal correlated channels.