Sparsity-aware complex-valued least mean kurtosis algorithms

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dc.contributor.authorOzince, Nazim
dc.contributor.authorMenguc, Engin Cemal
dc.contributor.authorEmlek, Alper
dc.date.accessioned2024-11-07T13:25:24Z
dc.date.available2024-11-07T13:25:24Z
dc.date.issued2025
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractComplex-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 /0 0-norm CLMK (/0-CLMK), / 0-CLMK), / 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.
dc.identifier.doi10.1016/j.sigpro.2024.109637
dc.identifier.issn0165-1684
dc.identifier.issn1872-7557
dc.identifier.scopus2-s2.0-85200759681
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.sigpro.2024.109637
dc.identifier.urihttps://hdl.handle.net/11480/14675
dc.identifier.volume226
dc.identifier.wosWOS:001292170300001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofSignal Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectComplex-valued least mean kurtosis
dc.subjectComplex-valued signals
dc.subjectSparse system identification
dc.subjectAugmented statistics
dc.titleSparsity-aware complex-valued least mean kurtosis algorithms
dc.typeArticle

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