A Robust Detection Algorithm Based on Maximum-Minimum Eigenvalue for Cognitive Radio

Spectrum sensing is one of the most significant problems in cognitive radio (CR). In this paper, a robust detection scheme is proposed based on the eigenvalues of the covariance matrix computed from the received signals by secondary users. The combination of the maximum eigenvalue and minimum eigenvalue of the sample covariance matrix is used as the statistic to detect the primary signal. For a target probability of false alarm, we can find the decision threshold using the random matrix theory (RMT). Simulation results show that the proposed scheme can achieve higher detection performance compared with the energy detection and the other two eigenvalue-based sensing methods.