基于混合平方/四阶误差准则的自适应稀疏信道估计
Improved adaptive sparse channel estimation using mixed square/fourth error criterion
摘要：
稀疏信道估计问题是稳定宽带无线通信中具有挑战性的技术问题之一。基于平方误差准则（SEC），自适应稀疏信道估计（ASCE）方法，例如，零吸引最小均方误差（ZA-LMS）算法和重加权ZA-LMS（RZA-LMS）算法被提出来减轻噪声干扰以及利用固有的信道稀疏性。然而，传统的SEC-ASCE方法容易受到1）输入训练信号的随机缩放; 2）由于梯度下降法的固定步长，收敛速度和稳态均方误差（MSE）性能之间的不平衡。本文提出了一种基于混合平方/四分之一误差准则（SFEC）的改进ASCE方法来避免上述缺点。具体地说，改进的SFEC-ASCE方法分别用零吸引最小均方/四阶误差（ZA-LMS/F）算法和重加权ZA-LMS/F（RZA-LMS/F）算法实现。首先，通过Monte-Carlo模拟，选择SFEC-ASCE方法的正则化参数。其次，推导并分析了SFEC-ASCE方法的下界。仿真结果表明，所提出的SFEC-ASCE方法比传统的SEC-ASCE方法具有更好的估计性能。1
英文摘要：
Sparse channel estimation problem is one of challenge technical issues in stable broadband wireless communications. Based on square error criterion (SEC), adaptive sparse channel estimation (ASCE) methods, e.g., zero-attracting least mean square error (ZA-LMS) algorithm and reweighted ZA-LMS (RZA-LMS) algorithm, have been proposed to mitigate noise interferences as well as to exploit the inherent channel sparsity. However, the conventional SEC-ASCE methods are vulnerable to 1) random scaling of input training signal; and 2) imbalance between convergence speed and steady state mean square error (MSE) performance due to fixed step-size of gradient descend method. In this paper, a mixed square/fourth error criterion (SFEC) based improved ASCE methods are proposed to avoid aforementioned shortcomings. Specifically, the improved SFEC-ASCE methods are realized with zero-attracting least mean square/fourth error (ZA-LMS/F) algorithm and reweighted ZA-LMS/F (RZA-LMS/F) algorithm, respectively. Firstly, regularization parameters of the SFEC-ASCE methods are selected by means of Monte-Carlo simulations. Secondly, lower bounds of the SFEC-ASCE methods are derived and analyzed. Finally, simulation results are given to show that the proposed SFEC-ASCE methods achieve better estimation performance than the conventional SEC-ASCE methods. 1
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