Robust Sparse Channel Estimation Based on Maximum Mixture Correntropy Criterion

The sparse channel estimation problem is drawing increasing attention in broadband wireless communication. Sparsity nature in structure and noise is one of the most important issues in such problems. Researchers have devised several sparsity penalty terms such as zero-attracting (ZA) and correntropy induced metric (CIM) to exploit potential sparse structure information and improve sparse channel estimate accuracy. To combat impulsive (sparse) noises, several adaptive filtering algorithms based on the maximum correntropy criterion (MCC) have been developed, which can achieve excellent performance, especially in heavy-tailed noises. Recently, the concept of mixture correntropy and the maximum mixture correntropy criterion (MMCC) were proposed to further promote the robustness of the MCC against impulsive noises. In this paper, a new robust and sparse adaptive filtering algorithm is developed to estimate the sparse channels with impulsive noises by combining the MMCC and CIM penalty. Thanks to the desirable property of the mixture correntropy, the proposed method behaves quite well with excellent convergence performance. Simulation results show that the new method can outperform several existing methods in sparse channel estimation including the MCC based methods.