Eigenvalue-Based Sensing and SNR Estimation for Cognitive Radio in Presence of Noise Correlation

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 standard-condition-number (SCN)-based decision statistics based on asymptotic random matrix theory (RMT) for the decision process. First, the effect of noise correlation on eigenvalue-based spectrum sensing (SS) is analytically studied under both the noise-only and signal-plus-noise hypotheses. Second, new bounds for the SCN are proposed to achieve improved sensing in correlated noise scenarios. Third, the performance of fractional-sampling (FS)-based SS is studied, and a method to determine the operating point for the FS rate in terms of sensing performance and complexity is suggested. Finally, a signal-to-noise ratio (SNR) estimation technique based on the maximum eigenvalue of the covariance matrix of the received signal is proposed. It is shown that the proposed SCN-based threshold improves sensing performance in correlated noise scenarios, and SNRs up to 0 dB can be reliably estimated with a normalized mean square error (MSE) of less than 1% in the presence of correlated noise without the knowledge of noise variance.