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dc.contributor.authorSuliman, Mohamed Abdalla Elhag
dc.contributor.authorAlrashdi, Ayed
dc.contributor.authorBallal, Tarig
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.date.accessioned2017-10-02T10:53:16Z
dc.date.available2017-10-02T10:53:16Z
dc.date.issued2017-09-27
dc.identifier.citationSuliman MA, Alrashdi AM, Ballal T, Al-Naffouri TY (2017) SNR Estimation in Linear Systems with Gaussian Matrices. IEEE Signal Processing Letters: 1–1. Available: http://dx.doi.org/10.1109/LSP.2017.2757398.
dc.identifier.issn1070-9908
dc.identifier.issn1558-2361
dc.identifier.doi10.1109/LSP.2017.2757398
dc.identifier.urihttp://hdl.handle.net/10754/625536
dc.description.abstractThis letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results show that the proposed method is very accurate.
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2016-KKI-2899.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8052123/
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
dc.subjectCorrelation
dc.subjectCost function
dc.subjectEstimation
dc.subjectLinear systems
dc.subjectMIMO
dc.subjectSignal processing algorithms
dc.subjectSignal to noise ratio
dc.titleSNR Estimation in Linear Systems with Gaussian Matrices
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Signal Processing Letters
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Electrical Engineering, University of Hail, Saudi Arabia
kaust.personSuliman, Mohamed Abdalla Elhag
kaust.personAlrashdi, Ayed
kaust.personBallal, Tarig
kaust.personAl-Naffouri, Tareq Y.
refterms.dateFOA2018-06-13T18:47:31Z


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