Gray space detection for cognitive radio networks

In cognitive radio (CR) networks, the gray spaces sensing scheme for classifying whether the energy source is the primary user directly affects quality of service of CR users and the throughput for the secondary networks. In this paper, we proposed a two-stage sensing scheme consisting of a series of consecutive energy detection based on generalized likelihood ratio test (GLRT) detection. Simulation results showed that the proposed sensing scheme could detect primary signal in lower signal-to-nose ratio (SNR) than energy detection, but it needs more sample numbers.