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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.
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.
SponsorsThis 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.
JournalIEEE Signal Processing Letters