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.

迄今为止，作为认知无线电网络的重要技术，已经建立了许多频谱感测方法。但是，某些频谱感测方法似乎无法提供有效的信号特征和准确的决策阈值，从而影响频谱孔的检测精度。为了进一步提高光谱孔的识别率，提出了一种基于协方差矩阵分解和粒子群聚类的集中式光谱传感新方法。通过IQ分解方法和Cholesky分解技术提取了一个新颖的信号特征向量。此外，训练粒子群聚类算法以获得决策分类器，该决策分类器可用于识别许可频谱是否可用。此外，这里考虑两种不同的应用场景。确实，提供了仿真示例来证明所提出的算法可以大大改善集中频谱感测的感测性能。