Determinant of the Sample Covariance Matrix Based Spectrum Sensing Algorithm for Cognitive Radio

The presence of the primary signal changes not only the signal energy but also the correlation structure, a new spectrum sensing algorithm based on the determinant of the sample covariance matrix is then introduced. The new algorithm utilizes the fact that the determinant of the sample covariance matrixes of received signals is different from that of noise samples with high probability to detect whether the primary signal presents or not. Multivariate statistical theories are used to derive the theoretical decision threshold. The new method can execute spectrum sensing without the information about the primary signal and the communication channel. Simulation results show that the proposed method exhibits better performance than the maximum eigenvalue detection (MED) for moderate and low correlation received signals.