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dc.contributor.authorGu, Yuzhou
dc.contributor.authorPolyanskiy, Yury
dc.contributor.authorHosseini Roozbehani, Hajir
dc.date.accessioned2021-12-20T14:36:49Z
dc.date.available2021-11-05T19:37:54Z
dc.date.available2021-12-20T14:36:49Z
dc.date.issued2020-06
dc.identifier.urihttps://hdl.handle.net/1721.1/137601.2
dc.description.abstract© 2020 IEEE. We revisit the problem of broadcasting on d-ary trees: starting from a Bernoulli(1/2) random variable X 0 at a root vertex, each vertex forwards its value across binary symmetric channels BSC δ to d descendants. The goal is to reconstruct X 0 given the vector X Lh of values of all variables at depth h. It is well known that reconstruction (better than a random guess) is possible as h →∞ if and only if δ < δ c (d). In this paper, we study the behavior of the mutual information and the probability of error when δ is slightly subcritical. The innovation of our work is application of the recently introduced less-noisy channel comparison techniques. For example, we are able to derive the positive part of the phase transition (reconstructability when δ < δ c ) using purely information-theoretic ideas. This is in contrast with previous derivations, which explicitly analyze distribution of the Hamming weight of X Lh (a so-called Kesten-Stigum bound).en_US
dc.description.sponsorshipNational Science Foundation (Grants CCF-17-17842, CCF-09-39370)en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/isit44484.2020.9174464en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleBroadcasting on trees near criticalityen_US
dc.typeArticleen_US
dc.identifier.citationGu, Yuzhou, Roozbehani, Hajir and Polyanskiy, Yury. 2020. "Broadcasting on trees near criticality." IEEE International Symposium on Information Theory - Proceedings, 2020-June.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.relation.journalIEEE International Symposium on Information Theory - Proceedingsen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-03-10T14:14:25Z
dspace.orderedauthorsGu, Y; Roozbehani, H; Polyanskiy, Yen_US
dspace.date.submission2021-03-10T14:14:27Z
mit.journal.volume2020-Juneen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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