Centralized spectrum sensing based on covariance matrix decomposition and particle swarm clustering

Abstract As the essential technology of cognitive radio networks, numerous spectrum sensing approaches have been established to date. However, some spectrum sensing methods seem to fail in providing an effective signal feature and an accurate decision threshold, which affects the detection accuracy of the spectrum holes. In this article, to further ameliorate the recognition rate of the spectrum holes, a novel centralized spectrum sensing method based on covariance matrix decomposition and particle swarm clustering is presented. A novel signal feature vector is extracted by the IQ decomposition method and the Cholesky decomposition technique. Moreover, particle swarm clustering algorithm is trained to obtain a decision classifier, which can be used to identify whether the licensed spectrum is available or not. Additionally, two different application scenarios are considered here. Indeed, simulations examples are provided to demonstrate that the proposed algorithm can considerably ameliorate the sensing performance for centralized spectrum sensing.