Block Bayesian sparse learning algorithms with application to estimating channels in OFDM systems
Block Bayesian sparse learning algorithms with application to estimating channels in OFDM systems
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DOI：
10.1109/wpmc.2014.7014823
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Guan Gui;Li Xu;Lin Shan
中科院分区：
文献类型：
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作者：
Guan Gui;Li Xu;Lin Shan
频率选择性衰落宽带通信系统中经常存在稀疏信道。其主要原因是接收端附近存在少量反射面，导致接收散射波形呈现簇状结构。传统的稀疏信道估计方法都是针对一般的稀疏信道模型提出的，没有考虑潜在的簇稀疏结构信息。在本文中，我们研究了最先进的正交频分复用（OFDM）系统中的簇稀疏信道估计（CS-CE）问题。提出了一种新的贝叶斯聚类稀疏信道估计（BCS-CE）方法，利用块稀疏贝叶斯学习（BSBL）算法来利用聚类稀疏结构。所提出的方法利用了训练矩阵中的聚类相关性，从而提高了估计性能。此外，与我们以前的方法使用统一的块划分信息，所提出的方法可以很好地工作时，通道的先验块划分信息是未知的。计算机仿真结果表明，与已有方法相比，该方法具有上级性能。
Cluster-sparse channels often exist in frequency-selective fading broadband communication systems. The main reason is received scattered waveform exhibits cluster structure which is caused by a few reflectors near the receiver. Conventional sparse channel estimation methods have been proposed for general sparse channel model which without considering the potential cluster-sparse structure information. In this paper, we investigate the cluster-sparse channel estimation (CS-CE) problems in the state of the art orthogonal frequency-division multiplexing (OFDM) systems. Novel Bayesian cluster-sparse channel estimation (BCS-CE) methods are proposed to exploit the cluster-sparse structure by using block sparse Bayesian learning (BSBL) algorithm. The proposed methods take advantage of the cluster correlation in training matrix so that they can improve estimation performance. In addition, different from our previous method using uniform block partition information, the proposed methods can work well when the prior block partition information of channels is unknown. Computer simulations show that the proposed method has a superior performance when compared with the previous methods.