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dc.contributor.authorTruong, LV
dc.contributor.authorScarlett, J
dc.date.accessioned2020-02-05T00:30:17Z
dc.date.available2020-02-05T00:30:17Z
dc.date.issued2019-08
dc.identifier.isbn9781538669006
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301725
dc.description.abstractThe support recovery problem consists of determining a sparse subset of variables that is relevant in generating a set of observations. In this paper, we study the support recovery problem in the phase retrieval model consisting of noisy phaseless measurements, which arises in a diverse range of settings such as optical detection, X-ray crystallography, electron microscopy, and coherent diffractive imaging. Our focus is on informationtheoretic fundamental limits under an approximate recovery criterion, with Gaussian measurements and a simple discrete model for the sparse non-zero entries. Our bounds provide sharp thresholds with near-matching constant factors in several scaling regimes on the sparsity and signal-to-noise ratio.
dc.publisherIEEE
dc.rightsAll rights reserved
dc.titleOn the Information-Theoretic Limits of Noisy Sparse Phase Retrieval
dc.typeConference Object
prism.publicationDate2019
prism.publicationName2019 IEEE Information Theory Workshop, ITW 2019
dc.identifier.doi10.17863/CAM.48797
dcterms.dateAccepted2019-06-13
rioxxterms.versionofrecord10.1109/ITW44776.2019.8989363
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-08-01
dc.contributor.orcidTruong, Lan [0000-0002-7756-3464]
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.conference-name2019 IEEE Information Theory Workshop (ITW)
pubs.conference-start-date2019-08-25
cam.orpheus.successThu Nov 05 11:55:49 GMT 2020 - Embargo updated
pubs.conference-finish-date2019-08-28
rioxxterms.freetoread.startdate2020-08-01


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