Eigenvector based cooperative wideband spectrum sensing for cognitive radios

In this paper we address two cooperative wideband spectrum sensing schemes, namely the maximum eigen-vector based algorithm (Max-EV) and the multiple eigen-vector (Mul-EV) based algorithm. They serve to detect the primary user (PU) signals over multiple frequency subbands in the wideband licensed frequency band (LFB). A fusion centre (FC) is operating to collect the raw sensing observations from the second users (SU) in the network and make the final decisions over the consecutive subbands. The optimal weights of the proposed cooperative wideband sensing methods are the maximum eigenvector or the multiple eigenvectors of the signal sample autocorrelation matrix. The proposed algorithms demand no a prior knowledge of the noise power and the PU signal. Theoretical analysis and simulation results show the proposed methods are robust against the noise power uncertainty and require less sensing data to yield the same performance, compared with the conventional spectrum sensing methods.