Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81325
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dc.contributor.authorHu, Wuhuaen
dc.contributor.authorTay, Wee Pengen
dc.date.accessioned2016-01-04T05:44:08Zen
dc.date.accessioned2019-12-06T14:28:29Z-
dc.date.available2016-01-04T05:44:08Zen
dc.date.available2019-12-06T14:28:29Z-
dc.date.issued2015en
dc.identifier.citationHu, W., & Tay, W. P. (2015). Multi-Hop Diffusion LMS for Energy-Constrained Distributed Estimation. IEEE Transactions on Signal Processing, 63(15), 4022-4036.en
dc.identifier.issn1053-587Xen
dc.identifier.urihttps://hdl.handle.net/10356/81325-
dc.description.abstractWe propose a multi-hop diffusion strategy for a sensor network to perform distributed least mean-squares (LMS) estimation under local and network-wide energy constraints. At each iteration of the strategy, each node can combine intermediate parameter estimates from nodes other than its physical neighbors via a multi-hop relay path. We propose a rule to select combination weights for the multi-hop neighbors, which can balance between the transient and the steady-state network mean-square deviations (MSDs). We study two classes of networks: simple networks with a unique transmission path from one node to another, and arbitrary networks utilizing diffusion consultations over at most two hops. We propose a method to optimize each node’s information neighborhood subject to local energy budgets and a network-wide energy budget for each diffusion iteration. This optimization requires the network topology, and the noise and data variance profiles of each node, and is performed offline before the diffusion process. In addition, we develop a fully distributed and adaptive algorithm that approximately optimizes the information neighborhood of each node with only local energy budget constraints in the case where diffusion consultations are performed over at most a predefined number of hops. Numerical results suggest that our proposed multi-hop diffusion strategy achieves the same steady-state MSD as the existing one-hop adapt-then-combine diffusion algorithm but with a lower energy budget.en
dc.description.sponsorshipMOE (Min. of Education, S’pore)en
dc.format.extent15 p.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE Transactions on Signal Processingen
dc.rights© 2015 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. The published version is available at: [http://dx.doi.org/10.1109/TSP.2015.2424206].en
dc.subjectCombination weights; convergence rate; distributed estimation; energy constraints; mean-square deviation; multihop diffusion adaptation; sensor networksen
dc.titleMulti-hop diffusion LMS for energy-constrained distributed estimationen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1109/TSP.2015.2424206en
dc.description.versionAccepted versionen
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