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.