An Adaptive Spectrum Detection Mechanism for Cognitive Radio Networks in Dynamic Traffic Environments

Abstract

We propose an adaptive spectrum detection mechanism for cognitive radios in a dynamic traffic environment. Cognitive radios generate secondary calls, which opportunistically make use of channels left idle by primary traffic generated by the licensed radios in the system. Spectrum detection for the cognitive radios is formulated as a hypothesis testing problem based on the Bayes criterion to minimize average cost. The maximum likelihood estimates of the prior probabilities for the hypothesis test are obtained from the dynamics of both traffic types of traffic using a Markovian model of the system channel occupancy. The spectrum detection scheme is extended to incorporate cooperation among multiple secondary users. Our numerical results suggest that the adaptive spectrum detection scheme can achieve significantly better error performance than a static scheme that ignores the prior probabilities, especially under light primary traffic conditions. Cooperative spectrum detection among multiple users can further reduce the false alarm and misdetection probabilities.