  Herein, we present a detailed analysis of an eigenvalue based sensing
technique in the presence of correlated noise in the context of a Cognitive
Radio (CR). We use a Standard Condition Number (SCN) based decision statistic
based on asymptotic Random Matrix Theory (RMT) for decision process. Firstly,
the effect of noise correlation on eigenvalue based Spectrum Sensing (SS) is
studied analytically under both the noise only and the signal plus noise
hypotheses. Secondly, new bounds for the SCN are proposed for achieving
improved sensing in correlated noise scenarios. Thirdly, the performance of
Fractional Sampling (FS) based SS is studied and a method for determining the
operating point for the FS rate in terms of sensing performance and complexity
is suggested. Finally, an SNR estimation technique based on the maximum
eigenvalue of the received signal's covariance matrix is proposed. It is shown
that proposed SCN-based threshold improves sensing performance in the presence
of correlated noise and SNRs upto 0 dB can be reliably estimated without the
knowledge of noise variance.
