Knowledge based cooperative spectrum sensing using polynomial classifiers in cognitive radio networks

This paper proposes a cooperative spectrum sensing scheme for cognitive radio networks using a polynomial classifier. The proposed scheme utilizes either coherent or cyclostationary features in the received signal at each node and the extracted features are used by a central node to make to a global decision about the availability of spectrum holes for use by the cognitive radio network. The paper presents a system model and simulation results for the detection probability of spectrum holes under fading channel conditions. The system is optimized to maximize the detection probability while keeping the false alarm at a fixed rate leading to improved spectrum utilization by the cognitive network while minimizing the interference to primary users.