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Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments

机译：非高斯环境下鲁棒信道估计的基于最大熵准则的稀疏自适应滤波算法

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摘要

Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS),which are robust under Gaussian assumption. In non-Gaussian environments, however, these methods are often no longer robust especially when systems are disturbed by random impulsive noises. To address this problem, we propose in this work a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation. Specifically, MCC is utilized to mitigate the impulsive noise while CIM is adopted to exploit the channel sparsity efficiently. Both theoretical analysis and computer simulations are provided to corroborate the proposed methods. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译：稀疏自适应信道估计问题由于其简单性和鲁棒性而成为宽带无线通信系统中最重要的主题之一。到目前为止，已经基于众所周知的最小均方误差（MMSE）准则，例如零吸引最小均方（ZALMS），开发了许多稀疏感知信道估计算法，这些算法在高斯假设下具有鲁棒性。但是，在非高斯环境中，这些方法通常不再具有鲁棒性，尤其是当系统受到随机脉冲噪声的干扰时。为了解决这个问题，我们在这项工作中提出了一种鲁棒的稀疏自适应滤波算法，该算法使用了熵诱导度量（CIM）罚分最大熵准则（MCC）代替了用于稳健信道估计的常规MMSE准则。具体来说，MCC用于缓解脉冲噪声，而CIM用于有效利用信道稀疏性。提供理论分析和计算机仿真以证实所提出的方法。 （C）2015富兰克林研究所。由Elsevier Ltd.出版。保留所有权利。

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  • 来源
    《Journal of the Franklin Institute》 |2015年第7期|2708-2727|共20页
  • 作者

    Ma Wentao; Qu Hua; Gui Guan; Xu Li; Zhao Jihong; Chen Badong;

  • 作者单位

    Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China;

    Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China;

    Akita Prefectural Univ, Dept Elect & Informat Syst, Akita 0150055, Japan;

    Akita Prefectural Univ, Dept Elect & Informat Syst, Akita 0150055, Japan;

    Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China;

    Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China;

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