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dc.contributor.authorAbdi, Younes
dc.contributor.authorRistaniemi, Tapani
dc.date.accessioned2020-08-17T10:54:19Z
dc.date.available2020-08-17T10:54:19Z
dc.date.issued2020
dc.identifier.citationAbdi, Y., & Ristaniemi, T. (2020). The Max-Product Algorithm Viewed as Linear Data-Fusion : A Distributed Detection Scenario. <i>IEEE Transactions on Wireless Communications</i>, <i>19</i>(11), 7585-7597. <a href="https://doi.org/10.1109/twc.2020.3012910" target="_blank">https://doi.org/10.1109/twc.2020.3012910</a>
dc.identifier.otherCONVID_41697701
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/71401
dc.description.abstractIn this paper, we disclose the statistical behavior of the max-product algorithm configured to solve a maximum a posteriori (MAP) estimation problem in a network of distributed agents. Specifically, we first build a distributed hypothesis test conducted by a max-product iteration over a binary-valued pairwise Markov random field and show that the decision variables obtained are linear combinations of the local log-likelihood ratios observed in the network. Then, we use these linear combinations to formulate the system performance in terms of the false-alarm and detection probabilities. Our findings indicate that, in the hypothesis test concerned, the optimal performance of the max-product algorithm is obtained by an optimal linear data-fusion scheme and the behavior of the max-product algorithm is very similar to the behavior of the sum-product algorithm. Consequently, we demonstrate that the optimal performance of the max-product iteration is closely achieved via a linear version of the sum-product algorithm, which is optimized based on statistics received at each node from its one-hop neighbors. Finally, we verify our observations via computer simulations.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Transactions on Wireless Communications
dc.rightsIn Copyright
dc.subject.otherstatistical inference
dc.subject.otherdistributed systems
dc.subject.othermax-product algorithm
dc.subject.othersum-product algorithm
dc.subject.otherlinear data-fusion
dc.subject.otherMarkov random fields
dc.subject.otherfactor graphs
dc.subject.otherspectrum sensing
dc.titleThe Max-Product Algorithm Viewed as Linear Data-Fusion : A Distributed Detection Scenario
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202008175537
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange7585-7597
dc.relation.issn1536-1276
dc.relation.numberinseries11
dc.relation.volume19
dc.type.versionacceptedVersion
dc.rights.copyright© 2020 IEEE
dc.rights.accesslevelopenAccessfi
dc.subject.ysoalgoritmit
dc.subject.ysoMarkovin ketjut
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p13075
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/twc.2020.3012910
dc.type.okmA1


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