Adaptive Two-Stage Sensing in Cognitive and Dynamic Spectrum Access Networks

In this paper, we first investigate and contrast the features of energy detection and cyclostationary feature detection for spectrum sensing. Combining the advantages of both, we propose an adaptive twostage sensing scheme which first performs spectrum sensing using an energy detector in cognitive and dynamic spectrum access networks. Then this scheme decides whether or not to implement cyclostationary feature detection based on the sensing results of the first stage. On the premise of meeting a given constraint on the probability of false alarm, our proposed scheme aim to optimize the probability of detection. In order to obtain the optimal detection thresholds, the detection model is formulated as a nonlinear optimization problem. Furthermore, the performance of our scheme in sensing speed is also analyzed. The simulation results show that the proposed scheme improves the performance of spectrum sensing compared with the ones where only energy detection or cyclostationary feature detection is performed.