dc.contributor.author | Gu, Yuzhou | |
dc.contributor.author | Polyanskiy, Yury | |
dc.contributor.author | Hosseini Roozbehani, Hajir | |
dc.date.accessioned | 2021-12-20T14:36:49Z | |
dc.date.available | 2021-11-05T19:37:54Z | |
dc.date.available | 2021-12-20T14:36:49Z | |
dc.date.issued | 2020-06 | |
dc.identifier.uri | https://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.sponsorship | National Science Foundation (Grants CCF-17-17842, CCF-09-39370) | en_US |
dc.language.iso | en | |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | 10.1109/isit44484.2020.9174464 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | arXiv | en_US |
dc.title | Broadcasting on trees near criticality | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Gu, 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.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems | en_US |
dc.relation.journal | IEEE International Symposium on Information Theory - Proceedings | en_US |
dc.eprint.version | Original manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2021-03-10T14:14:25Z | |
dspace.orderedauthors | Gu, Y; Roozbehani, H; Polyanskiy, Y | en_US |
dspace.date.submission | 2021-03-10T14:14:27Z | |
mit.journal.volume | 2020-June | en_US |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Publication Information Needed | en_US |