A spectrum sensing method for cognitive network using Kernel principal component analysis and random forest

Spectrum sensing is a critical prerequisite in envisioned applications of wireless cognitive sensor networks which promise to resolve the perceived bandwidth scarcity versus under-utilization dilemma. The Kernel method is a very powerful tool in machine learning. The trick of kernel has been effectively and extensively applied in many areas of machine learning. In this paper, we propose a novel spectrum sensing method based on the kernel principal component analysis (KPCA) and random forest(RF) to detect the received radio signal. A set of cyclic spectrum features are first calculated, and the KPCA is applied to extract the most discriminant feature vector for classification. Furthermore, the detecting signal is classified by the trained random forest. Simulation results show the better performance of the our proposed algorithm in low signal-to-noise ratio.