A Fusion Spectrum Sensing Algorithm Using Energy and Eigenvalues

Abstract

A novel fusion spectrum sensing algorithm using energy and eigenvalues is proposed, which employs the energy, maximum eigenvalue and minimum eigenvalue of the sample covariance matrix to construct test statistic. The proposed algorithm includes the MET, MME and EME algorithms as special cases, and it can be seen as a fusion of the test statistics of the MET and EME algorithms. In addition, the false alarm probability and threshold of the proposed method are derived using random matrix theory. The proposed algorithm is a more general algorithm. Simulation results show the effectiveness of the new algorithm.