Blind Matching Filtering Algorithm for Spectrum Sensing under Multi-Path Channel Environment

Matching filtering has been proven to be the optimal spectrum sensing algorithm under Gaussian white noise. However, the application of this algorithm is limited because of its dependence on prior information. In this paper, we propose a spectrum sensing algorithm based on blind matching filtering (BMF) by using the correlation between adjacent received signals under dispersive channels. Theoretical analysis shows that the proposed algorithm can achieve a performance comparable to that of the matching filtering algorithm without requiring the prior information of the primary user. Thus, this algorithm shows superior detection performance. Moreover, an improved BMF (IBMF) algorithm is proposed on the basis of the correlation between different time-delay signals. IBMF utilizes more comprehensive correlation information of the received signals and achieves better detection performance compared to BMF. Furthermore, the two proposed algorithms have lower computational complexity than the classical approaches based on the covariance matrix of the received signals. Numerical simulations confirm the superior performance of the proposed detectors and validate the theoretical analysis.