Reduced complexity adaptive filtering algorithms with applications to communications systems

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Werner, Stefan
dc.date.accessioned 2012-02-10T09:39:40Z
dc.date.available 2012-02-10T09:39:40Z
dc.date.issued 2002-11-15
dc.identifier.isbn 951-22-6087-5
dc.identifier.issn 1458-6401
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2233
dc.description.abstract This thesis develops new adaptive filtering algorithms suitable for communications applications with the aim of reducing the computational complexity of the implementation. Low computational complexity of the adaptive filtering algorithm can, for example, reduce the required power consumption of the implementation. A low power consumption is important in wireless applications, particularly at the mobile terminal side, where the physical size of the mobile terminal and long battery life are crucial. We focus on the implementation of two types of adaptive filters: linearly-constrained minimum-variance (LCMV) adaptive filters and conventional training-based adaptive filters. For LCMV adaptive filters, normalized data-reusing algorithms are proposed which can trade off convergence speed and computational complexity by varying the number of data-reuses in the coefficient update. Furthermore, we propose a transformation of the input signal to the LCMV adaptive filter, which properly reduces the dimension of the coefficient update. It is shown that transforming the input signal using successive Householder transformations renders a particularly efficient implementation. The approach allows any unconstrained adaptation algorithm to be applied to linearly constrained problems. In addition, a family of algorithms is proposed using the framework of set-membership filtering (SMF). These algorithms combine a bounded error specification on the adaptive filter with the concept of data-reusing. The resulting algorithms have low average computational complexity because coefficient update is not performed at each iteration. In addition, the adaptation algorithm can be adjusted to achieve a desired computational complexity by allowing a variable number of data-reuses for the filter update. Finally, we propose a framework combining sparse update in time with sparse update of filter coefficients. This type of partial-update (PU) adaptive filters are suitable for applications where the required order of the adaptive filter is conflicting with tight constraints for the processing power. en
dc.format.extent 184
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Report / Helsinki University of Technology, Signal Processing Laboratory en
dc.relation.ispartofseries 37 en
dc.subject.other Electrical engineering en
dc.title Reduced complexity adaptive filtering algorithms with applications to communications systems en
dc.type G4 Monografiaväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Electrical and Communications Engineering en
dc.contributor.department Sähkö- ja tietoliikennetekniikan osasto fi
dc.subject.keyword set-membership filtering en
dc.subject.keyword data selective en
dc.subject.keyword normalized data-reusing algorithms en
dc.subject.keyword linearly-constrained adaptive filters en
dc.subject.keyword partial update algorithms en
dc.identifier.urn urn:nbn:fi:tkk-002038
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (monografia) fi
dc.type.ontasot Doctoral dissertation (monograph) en
dc.contributor.lab Laboratory of Signal Processing Technology en
dc.contributor.lab Signaalinkäsittelytekniikan laboratorio fi


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