Least mean square/fourth algorithm for adaptive sparse channel estimation
Least mean square/fourth algorithm for adaptive sparse channel estimation
复制标题
DOI：
10.1109/pimrc.2013.6666149
复制
发表时间：
2013-11
期刊：
影响因子：
--
通讯作者：
Guan Gui;A. Mehbodniya;F. Adachi
中科院分区：
文献类型：
--
作者：
Guan Gui;A. Mehbodniya;F. Adachi
频率选择性衰落信道上的宽带信号传输通常需要接收器处的准确信道状态信息。最吸引人的自适应信道估计（ACE）方法之一是最小均方（LMS）算法。然而，它的性能通常会因输入训练信号的随机缩放而降低。为了克服这种退化，在本文中，我们考虑使用标准最小均方/第四（LMS/F）算法。由于宽带信道通常由稀疏信道模型描述，因此可以利用这种稀疏性作为先验信息。首先，我们通过在成本函数中引入ℓ1范数稀疏约束，提出了一种具有零吸引LMS/F（ZA-LMS/F）算法的自适应稀疏信道估计（ASCE）方法。然后，为了更有效地利用稀疏性，提出了一种带有重新加权零吸引LMS/F（RZA-LMS/F）的改进ASCE算法。对于不同的信道稀疏度，我们提出了一种蒙特卡罗方法，用于 RA-LMS/F 和 RZA-LMS/F 中的正则化参数选择，以实现更好的稳态估计性能。仿真结果表明，所提出的ASCE方法比传统方法具有更好的估计性能。
Broadband signal transmission over frequency-selective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation (ACE) methods is least mean square (LMS) algorithm. However, its performance is often degraded by random scaling of input training signal. To overcome this degradation, in this paper we consider the use of standard least mean square/fourth (LMS/F) algorithm. Since the broadband channel is often described by sparse channel model, such sparsity could be exploited as prior information. First, we propose an adaptive sparse channel estimation (ASCE) method with zero-attracting LMS/F (ZA-LMS/F) algorithm by introducing an ℓ1-norm sparse constraint into the cost function. Then, to exploit the sparsity more effectively, an improved ASCE with reweighted zero-attracting LMS/F (RZA-LMS/F) algorithm is proposed. For different channel sparsity, we propose a Monte Carlo method for a regularization parameter selection in RA-LMS/F and RZA-LMS/F to achieve better steady-state estimation performance. Simulation results show that the proposed ASCE methods achieve better estimation performance than the conventional one.