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Conference Paper: On the convergence behavior of the noise-constrained NLMS algorithm

TitleOn the convergence behavior of the noise-constrained NLMS algorithm
Authors
KeywordsAlgorithms
Computer Simulation
Interference Suppression
Issue Date2009
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2009, p. 2049-2052 How to Cite?
AbstractThis paper studies the convergence behaviors of the noise-constrained normalized least mean squares (NCNLMS) algorithm recently proposed in [1]. Like its LMS counterpart, the NCNLMS algorithm employs the prior knowledge of the additive noise to adjust its step-size. Following [2], the convergence behaviors of the NCLMS under the noise mismatch cases are firstly derived. Using a novel transformation approach and the small step-size properties of the NCNLMS algorithm at convergence, the mean and mean squares behaviors of this algorithm are derived. The validity of the proposed analysis is verified well by computer simulations and the relative merits of the NCLMS and NCNLMS algorithms are also compared. ©2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/143325
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorChu, YJen_HK
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorZhou, Yen_HK
dc.date.accessioned2011-11-22T08:30:23Z-
dc.date.available2011-11-22T08:30:23Z-
dc.date.issued2009en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2009, p. 2049-2052en_HK
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143325-
dc.description.abstractThis paper studies the convergence behaviors of the noise-constrained normalized least mean squares (NCNLMS) algorithm recently proposed in [1]. Like its LMS counterpart, the NCNLMS algorithm employs the prior knowledge of the additive noise to adjust its step-size. Following [2], the convergence behaviors of the NCLMS under the noise mismatch cases are firstly derived. Using a novel transformation approach and the small step-size properties of the NCNLMS algorithm at convergence, the mean and mean squares behaviors of this algorithm are derived. The validity of the proposed analysis is verified well by computer simulations and the relative merits of the NCLMS and NCNLMS algorithms are also compared. ©2009 IEEE.en_HK
dc.languageengen_US
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systemsen_HK
dc.subjectAlgorithmsen_US
dc.subjectComputer Simulationen_US
dc.subjectInterference Suppressionen_US
dc.titleOn the convergence behavior of the noise-constrained NLMS algorithmen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.emailZhang, ZG: zhangzg@hku.hken_HK
dc.identifier.emailZhou, Y: yizhou@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityZhou, Y=rp00213en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/ISCAS.2009.5118196en_HK
dc.identifier.scopuseid_2-s2.0-70350169453en_HK
dc.identifier.hkuros159411-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70350169453&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage2049en_HK
dc.identifier.epage2052en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridChu, YJ=35098281800en_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridZhou, Y=55209555200en_HK

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