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dc.contributor.authorChung, Hye Won
dc.contributor.authorSadler, Brian M
dc.contributor.authorZheng, Lizhong
dc.contributor.authorHero, Alfred O
dc.date.accessioned2021-10-27T20:06:06Z
dc.date.available2021-10-27T20:06:06Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/1721.1/134668
dc.description.abstract© 2017 IEEE. In this paper, we propose an open-loop unequal-error-protection querying policy based on superposition coding for the noisy 20 questions problem. In this problem, a player wishes to successively refine an estimate of the value of a continuous random variable by posing binary queries and receiving noisy responses. When the queries are designed non-adaptively as a single block and the noisy responses are modeled as the output of a binary symmetric channel, the 20 questions problem can be mapped to an equivalent problem of channel coding with unequal error protection (UEP). A new non-adaptive querying strategy based on UEP superposition coding is introduced, whose estimation error decreases with an exponential rate of convergence that is significantly better than that of the UEP repetition coding introduced by Variani et al. (2015). With the proposed querying strategy, the rate of exponential decrease in the number of queries matches the rate of a closed-loop adaptive scheme, where queries are sequentially designed with the benefit of feedback. Furthermore, the achievable error exponent is significantly better than that of random block codes employing equal error protection.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.isversionof10.1109/TIT.2017.2760634
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourcearXiv
dc.titleUnequal Error Protection Querying Policies for the Noisy 20 Questions Problem
dc.typeArticle
dc.identifier.citationHye Won, Chung, et al. "Unequal Error Protection Querying Policies for the Noisy 20 Questions Problem [Arxiv]." arXiv (2016): 43 pp.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.relation.journalIEEE Transactions on Information Theory
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2019-07-08T18:20:10Z
dspace.orderedauthorsChung, HW; Sadler, BM; Zheng, L; Hero, AO
dspace.date.submission2019-07-08T18:20:11Z
mit.journal.volume64
mit.journal.issue2
mit.metadata.statusAuthority Work and Publication Information Needed


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