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

Abstract

The least mean p-power (LMP) is one of the most 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 l 0 -norm can be used as a sparsity penalty term. The proposed sparsity-aware LMP algorithms include the l 1 -norm, reweighted l 1 -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 l 0 -norm. B Wentao Ma

Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis

“…In these experiments, we compare the BER and MSE of our methods with those of the conventional DFE and the state-of-the-art algorithms in the literature including: reweighted zero-attracting least mean p-power (RZALMP) [35], improved least sum of exponentials (ILSE) [34], l 0 -RLS [32] (indicated as LZRLS in the figures), as well as the conventional SA, RLS and LMS algorithms. We emphasize that all of these algorithms are designed to combat the impulsive noise through minimization of different cost functions summarized in Table 2.…”

“…In [34], a hyperbolic function, e.g., C (e m ) = cosh(e m ), is used as the cost function to inherently combine different powers of the error. Moreover, [35] uses a sparse least mean p-power (LMP) algorithm to adaptively provide a robust estimate of the underwater channel.…”

Variable kernel‐based computing algorithms for estimating sparse multipath channels

“…Please notice that the steady‐state stability of the proposed computing algorithm is very important in different channel sparsity. Some existing algorithms are used to encounter instability problem in uncertain number of nonzero taps, especially in the case of dense channel . Hence, the stability of the conventional channel estimation algorithms highly depends on channel sparsity ( K ).…”

A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

“…Moreover, a collection of zero-attraction (ZA) algorithms, such as the ZA-LMS and its reweighted form (RZA-LMS), ZA-AP, and RZA-AP algorithms, etc. [26][27][28][29][30][31][32][33], are developed based on the concept of compressed sensing (CS) theory [34] for sparse SI.…”