On the Use of Eigenvectors in Multi-Antenna Spectrum Sensing with Noise Variance Estimation

In this paper, a thorough comparison of multi-antenna spectrum sensing techniques is performed. We considered well known algorithms, such as Energy Detector (ED), eigenvalue based detectors, and an algorithm that uses the eigenvector associated to the largest eigenvalue of the covariance matrix. With the idea of auxiliary noise variance estimation, a hybrid approach for the eigenvector-based method is presented and compared against the hybrid Roy's Largest Root Test and hybrid ED. Performance results are evaluated in terms of Receiver Operating Characteristic (ROC) curves and performance curves, i.e., detection probability as a function of the Signal to Noise Ratio (SNR). It is shown that the the eigenvector-based algorithm and its hybrid variant are able approach the optimal Neyman-Pearson performance.