A blind spectrum sensing based on low-rank and sparse matrix decomposition

As a crucial component in Cognitive Radio (CR) networks, spectrum sensing has been attracting lots of attention. Some conventional methods for spectrum sensing are sensitive to uncertain signal and noise, its applicability is limited thereof. In this paper, a novel blind spectrum sensing method is proposed, where low-rank and sparse matrix decomposition is applied to the observation signal of a CR in the frequency domain. Then the ratio of the energy of the sparse part and the received signal in the time domain is considered as the criterion to decide whether the radio frequency band is idle by means of a comparison with a predefined threshold. The proposed method is independent of prior knowledge of signal and white noise, and has a better detection performance. Simulation experiments verify the performance of the proposed method in additive white Gaussian noise (AWGN), Rayleighand Rician channels.