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

TL;DR

This paper analyzes eigenvalue-based spectrum sensing in cognitive radio under correlated noise, proposing new bounds and methods for improved detection and SNR estimation without prior noise variance knowledge.

Contribution

It introduces a novel SCN-based decision statistic, bounds for correlated noise scenarios, and an SNR estimation method using maximum eigenvalue, enhancing sensing performance.

Findings

SCN-based threshold improves sensing in correlated noise

Reliable SNR estimation up to 0 dB without noise variance knowledge

Performance analysis under noise correlation conditions

Abstract

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.