Cooperative spectrum sensing based on maximum eigenvalue

The existing spectrum sensing algorithms based on asymptotic spectrum theory usually only consider single user. And in low SNR environment, the eigenvalues of the covariance matrix of the received signal are compressed to the upper bound of the eigenvalues of the M-P law bulk. which makes it difficult to distinguish the eigenvalues corresponding to the noise and the signal, and results in poor detection performance. By adding auxiliary signals, a multi-user cooperative perception algorithm based on maximum eigenvalue is proposed, which makes the maximum eigenvalue corresponding to the target signal move right out of the upper boundary of M-P law bulk and obey the Gauss distribution. The theoretical and simulation results show that the proposed algorithm has better sensing performance than the existing RMT-based sensing algorithm, and it still has better sensing performance when the number of cognitive users or sampling points are limited.