Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise

The leastmean p-power (LMP) is one of themost popular adaptive filtering algorithms. With a proper p value, the LMP can outperform the traditional least mean square (p = 2), especially under the impulsive noise environments. In sparse channel estimation, the unknown channel may have a sparse impulsive (or frequency) response. In this paper, our goal is to develop new LMP algorithms that can adapt to the underlying sparsity and achieve better performance in impulsive noise environments. Particularly, the correntropy induced metric (CIM) as an excellent approximator of the l0-norm can be used as a sparsity penalty term. The proposed sparsity-aware LMP algorithms include the l1-norm, reweighted l1-norm and CIM penalized LMP algorithms, which are denoted as ZALMP, RZALMP and CIMLMP respectively. The mean and mean square convergence of these algorithms are analysed. Simulation results show that the proposed new algorithms perform well in sparse channel estimation under impulsive noise environments. In particular, the CIMLMP with suitable kernel width will outperform other algorithms significantly due to the superiority of the CIM approximator for the l0-norm. B Wentao Ma [email protected] Badong Chen [email protected] Hua Qu [email protected] Jihong Zhao [email protected] 1 School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China