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Partial-diffusion recursive least-squares estimation over adaptive networks

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dc.contributor.authorArablouei, R.
dc.contributor.authorWerner, S.
dc.contributor.authorDogancay, K.
dc.contributor.conferenceIEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (15 Dec 2013 - 18 Dec 2013 : Saint Martin, France)
dc.date.issued2013
dc.description.abstractIn the diffusion strategies for distributed estimation over adaptive networks, each node calculates a weighted average of the intermediate parameter estimates of its neighboring nodes. Thus, all the nodes should continuously share their intermediate estimates with their neighbors. In this paper, we consider exchanging a predetermined number of elements of each intermediate estimate vector at each iteration rather than the entire vectors. We examine two different schemes, i.e., stochastic and sequential partial-diffusion schemes, for selecting the to-be-diffused elements at each iteration. Accordingly, we propose a partial-diffusion recursive least-squares (PDRLS) algorithm that can alleviate internode communications at the expense of estimation performance. Simulation results show that the communication-performance trade-off offered by the proposed algorithm is indeed lucrative.
dc.identifier.citation2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013, iss.6714014, pp.89-92
dc.identifier.doi10.1109/CAMSAP.2013.6714014
dc.identifier.isbn9781467331449
dc.identifier.orcidDogancay, K. [0000-0003-3373-6295]
dc.identifier.urihttps://hdl.handle.net/1959.8/153056
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.fundingAcademy of Finland
dc.rightsCopyright 2013 IEEE
dc.source.urihttps://doi.org/10.1109/CAMSAP.2013.6714014
dc.subjectadaptive networks
dc.subjectdiffusion adaptation
dc.subjectdistributed estimation
dc.subjectpartial diffusion
dc.subjectrecursive least-squares
dc.titlePartial-diffusion recursive least-squares estimation over adaptive networks
dc.typeConference paper
pubs.publication-statusPublished
ror.mmsid9915909949501831

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