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dc.contributor.authorRajput, Kunwar
dc.contributor.authorVerma, Yogesh
dc.contributor.authorDasanadoddi Venkategowda, Naveen Kumar
dc.contributor.authorJagannatham, Aditya
dc.contributor.authorVarshney, Pramod K.
dc.date.accessioned2021-03-10T10:24:51Z
dc.date.available2021-03-10T10:24:51Z
dc.date.created2021-01-20T16:07:11Z
dc.date.issued2020
dc.identifier.citationGLOBECOM 2020 - 2020 IEEE Global Communications Conferenceen_US
dc.identifier.isbn978-1-7281-8298-8
dc.identifier.urihttps://hdl.handle.net/11250/2732581
dc.description.abstractThis work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) wireless sensor network. The proposed designs that minimize the mean squared error (MSE) of the parameter estimate at the fusion center are based on majorization theory, which leads to non-iterative closed-form solutions for the precoders and combiners. Various scenarios are considered for parameter estimation such as networks with ideal high precision sensors as well as noisy non-ideal sensors. Moreover, inter parameter correlation is also incorporated, which makes the analysis comprehensive. The Bayesian Cramer-Rao bound (BCRB) and centralized MMSE bound are determined to characterize the estimation performance. Simulation results demonstrate the improved performance and also corroborate our analytical formulations.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartof2020 IEEE Global Communications Conference (GLOBECOM)
dc.titleLinear MMSE Precoder Combiner Designs for Decentralized Estimation in Wireless Sensor Networksen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.identifier.doi10.1109/GLOBECOM42002.2020.9322457
dc.identifier.cristin1875801
dc.relation.projectNorges forskningsråd: 274717en_US
dc.description.localcode© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
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