Performance Analysis of Spectrum Sensing Techniques in Nakagami and Rice Fading Channels

This paper aims at investigating the performance of four eigenvalue-based techniques for centralized data-fusion cooperative spectrum sensing in cognitive radio networks over flat Nakagami-m and Rice fading channels. The detection techniques are the generalized likelihood ratio test (GLRT), the maximum-minimum eigenvalue detection (MMED), the maximum eigenvalue detection (MED), and the energy detection (ED). In the case of Nakagami-m, arbitrary fading and phase parameters were assumed, and so was with the Rice parameter in the case of the Rician model.