An improved spectrum sensing algorithm based on energy detection and covariance detection

In cognitive radio networks, spectrum sensing is a necessary technique to detect the status of spectrum resources. Considering the actual usage of authorized spectrum, various spectrum sensing techniques make the authorized spectrum reused possible without disturbing the authorized user. Energy detection is widely used because of its simple and efficient, but it is easily affected by the uncertainty signal-to-noise ratio, known as the SNR wall. However, covariance detection just has an ability to overcome this weakness. For this reason, we expect to exploit the advantages of both energy and covariance detection to realize the fast and efficient spectrum sensing process. In this paper, an improved spectrum sensing based on energy detection and covariance detection is proposed. The algorithm consists of two parts, namely coarse detection and fine detection, to ensure the accuracy of detection results. Simulation results show that the detection performance will have more improvement. The complexity is also analyzed in this paper. And there is a tradeoff between the detection performance and the complexity.