Spectrum Sensing based on the Difference between the Maximum Eigenvalue and the Geometric Mean of the Eigenvalue

Spectrum sensing technology is the cornerstone in the architecture of cognitive radio networks. In the field of spectrum sensing, various eigenvalue-based algorithms are proposed by analyzing the distribution of eigenvalues of the sampling covariance matrix. In those algorithms, the detection performance is limited by the number of equipped antennas under low signal-to-noise (SNR) ratio condition. This paper proposes a spectrum sensing algorithm based on the cyclic reorganization of the sampled signal using the difference between the maximum eigenvalue and the geometric mean of the eigenvalues (CRDMGM). By cyclically shifting the sampled signal data, the number of virtual antennas can be increased to obtain more information related to the sampling signal. As a result, the estimation accuracy of the eigenvalue is improved, which mitigates the impacts on the detection performance caused by the insufficient number of antennas. Simulation results demonstrate that under the conditions of limited antenna numbers and low SNR, the CRDMGM algorithm significantly outperforms the algorithm of the difference between the received maximum eigenvalue and the geometric mean of the eigenvalue (DMGM) and the improved version (IDMGM) based on split reorganization.