TY - JOUR

T1 - SNR Estimation in Linear Systems with Gaussian Matrices

AU - Suliman, Mohamed Abdalla Elhag

AU - Alrashdi, Ayed

AU - Ballal, Tarig

AU - Al-Naffouri, Tareq Y.

N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.

PY - 2017/9/27

Y1 - 2017/9/27

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/625536

UR - http://ieeexplore.ieee.org/document/8052123/

UR - http://www.scopus.com/inward/record.url?scp=85030753922&partnerID=8YFLogxK

U2 - 10.1109/LSP.2017.2757398

DO - 10.1109/LSP.2017.2757398

M3 - Article

VL - 24

SP - 1867

EP - 1871

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

SN - 1070-9908

IS - 12

ER -