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Sparse channel estimation problem is one of challenge technical issues instable broadband wireless communications. Based on square error criterion(SEC), adaptive sparse channel estimation (ASCE) methods, e.g., zero-attractingleast mean square error (ZA-LMS) algorithm and reweighted ZA-LMS (RZA-LMS)algorithm, have been proposed to mitigate noise interferences as well as toexploit the inherent channel sparsity. However, the conventional SEC-ASCEmethods 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, amixed square/fourth error criterion (SFEC) based improved ASCE methods areproposed to avoid aforementioned shortcomings. Specifically, the improvedSFEC-ASCE methods are realized with zero-attracting least mean square/fourtherror (ZA-LMS/F) algorithm and reweighted ZA-LMS/F (RZA-LMS/F) algorithm,respectively. Firstly, regularization parameters of the SFEC-ASCE methods areselected by means of Monte-Carlo simulations. Secondly, lower bounds of theSFEC-ASCE methods are derived and analyzed. Finally, simulation results aregiven to show that the proposed SFEC-ASCE methods achieve better estimationperformance than the conventional SEC-ASCE methods. 1

Improved adaptive sparse channel estimation using mixed square/fourth error criterion