  This paper 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 are provided, and show that the proposed method is very accurate.
