Regularization selection method for LMS-type sparse multipath channel estimation
Regularization selection method for LMS-type sparse multipath channel estimation
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DOI：
10.1109/apcc.2013.6766029
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Zhengxing Huang;Guan Gui;An-min Huang;D. Xiang;F. Adachi
中科院分区：
文献类型：
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作者：
Zhengxing Huang;Guan Gui;An-min Huang;D. Xiang;F. Adachi
最小均方(LMS)型自适应稀疏算法由于具有计算复杂度低和可靠性高等优点，在稀疏多径信道估计(SMPC)领域受到了广泛的关注。将ℓ1范数稀疏约束函数引入最小二乘算法，提出了最小二乘算法的零吸引最小二乘算法和加权零吸引最小均方算法。众所周知，SMPC的性能由正则化参数决定，正则化参数平衡了信道估计误差和稀疏惩罚强度。然而，这两种算法都没有考虑最优的正则化参数选择问题。基于压缩感知理论，本文解释了Lasso和LMS型自适应稀疏算法之间的数学关系。在此基础上，分别针对ZA-LMS和RZA-LMS提出了一种近似最优调节参数选择方法。基于蒙特卡罗的计算机仿真结果表明了该方法的有效性。
Least mean square (LMS)-type adaptive sparse algorithms have been attracting much attention on sparse multipath channel estimation (SMPC) due to their two advantages: low computational complexity and reliability. By introducing ℓ1 -norm sparse constraint function into LMS algorithm, both zero-attracting least mean square (ZA-LMS) and reweighted zero-attracting least mean square (RZA-LMS) have been proposed for SMPC. It is well known that the performance of the SMPC is decided by regularization parameter which balances channel estimation error and sparse penalty strength. However, optimal regularization parameter selection has not yet considered in the two proposed algorithms. Based on the compressive sensing theory, in this paper, we explain the mathematical relationship between Lasso and LMS-type adaptive sparse algorithms. Later, an approximate optimal regulation parameter selection method is proposed for ZA-LMS and RZA-LMS, respectively. Monte Carlo based computer simulations are presented to show the effectiveness of our propose method.