Please use this identifier to cite or link to this item: https://doi.org/10.1109/JOE.2012.2221811
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dc.titleNew sparse adaptive algorithms based on the natural gradient and the L 0-norm
dc.contributor.authorPelekanakis, K.
dc.contributor.authorChitre, M.
dc.date.accessioned2014-10-07T04:33:07Z
dc.date.available2014-10-07T04:33:07Z
dc.date.issued2013
dc.identifier.citationPelekanakis, K., Chitre, M. (2013). New sparse adaptive algorithms based on the natural gradient and the L 0-norm. IEEE Journal of Oceanic Engineering 38 (2) : 323-332. ScholarBank@NUS Repository. https://doi.org/10.1109/JOE.2012.2221811
dc.identifier.issn03649059
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/82756
dc.description.abstractA new algorithmic framework for sparse channel identification is proposed. Although the focus of this paper is on sparse underwater acoustic channels, this framework can be applied in any field where sequential noisy signal samples are obtained from a linear time-varying system. A suit of new algorithms is derived by minimizing a differentiable cost function that utilizes the underlying Riemannian structure of the channel as well as the L0-norm of the complex-valued channel taps. The sparseness effect of the proposed algorithms is successfully demonstrated by estimating a mobile shallow-water acoustic channel. The clear superiority of the new algorithms over state-of-the-art sparse adaptive algorithms is shown. Moreover, the proposed algorithms are employed by a channel-estimate-based decision-feedback equalizer (CEB DFE). These CEB DFE structures are compared with a direct-adaptation DFE (DA DFE), which is based on sparse and nonsparse adaptation. Our results confirm the improved error-rate performance of the new CEB DFEs when the channel is sparse. © 1976-2012 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/JOE.2012.2221811
dc.sourceScopus
dc.subjectAcoustic echo cancellation
dc.subjectimproved- proportionate normalized least mean square (IPNLMS)
dc.subjectimproved-proportionate affine projection algorithm (IPAPA)
dc.subjectL0-norm
dc.subjectproportionate algorithms
dc.subjectsparse equalization
dc.subjectsparse recursive least squares (RLS)
dc.subjectunderwater acoustic communications
dc.subject{L} 1-RRLS
dc.typeArticle
dc.contributor.departmentTROPICAL MARINE SCIENCE INSTITUTE
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/JOE.2012.2221811
dc.description.sourcetitleIEEE Journal of Oceanic Engineering
dc.description.volume38
dc.description.issue2
dc.description.page323-332
dc.description.codenIJOED
dc.identifier.isiut000317920200010
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