As the demand for internet of things (IoT) and device-to-device (D2D) applications in next generation communication systems increases, we are confronted with a challenge of spectrum scarcity. One promising solution to this problem is cognitive radio network (CRN), where the key element is the spectrum - a valuable and sharable natural resource that should not be wasted. To design efficient and sustainable networks for the future, it is crucial to ensure that spectrum sensing is not only accurate and rapid, but also energy-efficient. Spectrum sensing is a critical aspect of CRNs, and this study is mainly focused on it. In this research, we employ the supervised machine learning algorithm, support vector machine (SVM), to detect primary users (PU). We investigate different variants of SVM, including linear, polynomial, and Gaussian radial basic function (RBF), and employ an ensemble classification-based approach to improve the classifier's performance and productivity. The simulation results demonstrate that the ensemble classifier achieves the highest performance.

翻译：随着下一代通信系统中物联网（IoT）与设备到设备（D2D）应用需求的增长，我们面临着频谱稀缺的挑战。认知无线电网络（CRN）是解决此问题的一种有前景的方案，其核心要素是频谱——一种宝贵、可共享且不应浪费的自然资源。为设计高效且可持续的未来网络，确保频谱感知不仅准确、快速，而且节能至关重要。频谱感知是CRN的关键环节，本研究主要聚焦于此。在本研究中，我们采用监督式机器学习算法——支持向量机（SVM）来检测主用户（PU）。我们研究了SVM的不同变体，包括线性、多项式和高斯径向基函数（RBF）核，并采用基于集成分类的方法以提升分类器的性能与效率。仿真结果表明，集成分类器实现了最佳性能。