SNR Estimation in Linear Systems with Gaussian Matrices

Mohamed Abdalla Elhag Suliman, Ayed Alrashdi, Tarig Ballal, Tareq Y. Al-Naffouri

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)1867-1871
Number of pages5
JournalIEEE Signal Processing Letters
Volume24
Issue number12
DOIs
StatePublished - Sep 27 2017

Bibliographical note

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.

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