SNR Estimation in Linear Systems with Gaussian Matrices

Handle URI:
http://hdl.handle.net/10754/625536
Title:
SNR Estimation in Linear Systems with Gaussian Matrices
Authors:
Suliman, Mohamed Abdalla Elhag ( 0000-0002-3447-1770 ) ; Alrashdi, Ayed Mofareh Bakheet ( 0000-0002-0066-8989 ) ; Ballal, Tarig; Al-Naffouri, Tareq Y.
Abstract:
This 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Suliman 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.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Signal Processing Letters
Issue Date:
27-Sep-2017
DOI:
10.1109/LSP.2017.2757398
Type:
Article
ISSN:
1070-9908; 1558-2361
Sponsors:
This 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.
Additional Links:
http://ieeexplore.ieee.org/document/8052123/
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSuliman, Mohamed Abdalla Elhagen
dc.contributor.authorAlrashdi, Ayed Mofareh Bakheeten
dc.contributor.authorBallal, Tarigen
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.date.accessioned2017-10-02T10:53:16Z-
dc.date.available2017-10-02T10:53:16Z-
dc.date.issued2017-09-27en
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.en
dc.identifier.issn1070-9908en
dc.identifier.issn1558-2361en
dc.identifier.doi10.1109/LSP.2017.2757398en
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.en
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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/8052123/en
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.en
dc.subjectCorrelationen
dc.subjectCost functionen
dc.subjectEstimationen
dc.subjectLinear systemsen
dc.subjectMIMOen
dc.subjectSignal processing algorithmsen
dc.subjectSignal to noise ratioen
dc.titleSNR Estimation in Linear Systems with Gaussian Matricesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Signal Processing Lettersen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Electrical Engineering, University of Hail, Saudi Arabiaen
kaust.authorSuliman, Mohamed Abdalla Elhagen
kaust.authorAlrashdi, Ayed Mofareh Bakheeten
kaust.authorBallal, Tarigen
kaust.authorAl-Naffouri, Tareq Y.en
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