Sparsity-aware complex-valued least mean kurtosis algorithms.

Complex-valued least mean kurtosis (CLMK) algorithm and its augmented version (ACLMK) have recently become popular as workhorse tools in the processing of complex-valued signals due to their superior performances. Unfortunately, they are not yet suitable for sparse system identification problems. In this paper, combining the well-known sparsity-promoting strategies into the cost function based on the negated kurtosis of the error signal, we introduce a suit of sparsity-aware CLMK algorithms, named l 0 -norm CLMK (l 0 -CLMK), l 0 -ACLMK, zero-attraction CLMK (ZA-CLMK), ZA-ACLMK, reweighted ZA-CLMK (RZA-CLMK), and RZA-ACLMK. Simulation results on synthetic and real-world sparse system identification scenarios in the complex domain show that the proposed algorithms outperform the existing sparsity-aware algorithms in terms of convergence rate, tracking, and steady-state error. • We design a novel family of sparsity-aware CLMK algorithms. • We leverage sparsity-promoting strategies and kurtosis cost function to design them. • Proposed sparsity-aware CLMK algorithms outperform their CLMS versions. [ABSTRACT FROM AUTHOR]