Compressive SNR Estimation for Wideband Cognitive Radio under Correlated Scenarios

Estimating the Signal to Noise Ratio (SNR) of
the Primary Users’ (PUs) signals over a wideband spectrum
accurately is crucial in order to fully exploit an under-utilized
primary spectrum using underlay Cognitive Radio (CR) techniques.
In this context, we study an SNR estimation problem for a
wideband CR under practical correlated scenarios in compressive
settings. We carry out detailed theoretical analysis for the
considered scenarios and then derive the expressions for the
asymptotic eigenvalue probability distribution function (aepdf)
of the measured signal’s covariance matrix using asymptotic
Random Matrix Theory. Subsequently, based on the derived
aepdfs, we present a technique to estimate the PU SNR over
a wideband spectrum with compressive measurements. The
performance of the proposed technique is evaluated in terms
of normalized Mean Square Error (MSE) and it is shown that
the SNR of the PU signals over the wideband spectrum can be
reliably estimated using the proposed technique.