Research on cognitive radio spectrum sensing method based on information geometry

Abstract

Making using of the emerging information geometry theory, we analyze the statistical properties of wireless spectrum signals received by secondary users, and propose a cognitive radio spectrum sensing method based on information geometry. We introduce a new detection structure, using the sample covariance matrix and corresponding to the points on the statistical manifold, by calculating the distance between them and make a decision, thus transforming the statistical detection problem into the geometric problem on the manifold. We also used two solutions: Constant False Alarm Rate (CFAR) Detector and Distance Detector (DD). The simulation results reveal that the performance of the information geometry method is superior to the traditional spectrum sensing algorithm, and research results will help us to explore the spectrum sensing problem from a new perspective.