Studentized Extreme Eigenvalue Based Double Threshold Spectrum Sensing Under Noise Uncertainty

Abstract

The eigenvalue based spectrum sensing is a low-cost spectrum sensing method that detects the presence of the licensed user signal in desired frequency. Traditional single-threshold eigenvalue sensing methods, which are widely used in the literature, can exhibit inadequate performance under low SNR and noise uncertainty. Therefore, in this study an eigenvalue-based spectrum sensing method is proposed using a double threshold with the studentized extreme eigenvalue distribution function. The results that threshold values obtained for the proposed method were simulated in Rayleigh fading channels. The results were compared with traditional methods and they were observed to be more accurate.

Keywords

• cognitive radio
• covariance matrix
• noise uncertainty
• spectrum sensing
• Tracy-Widom distribution