A Novel Clustering Algorithm Based on Information Geometry for Cooperative Spectrum Sensing

Spectrum sensing is used to detect whether primary user are using authorized spectrums, which can be regarded as a key and core issue for opportunistic spectrum access in cognitive radio networks. In traditional information theory and clustering algorithm-based spectrum sensing methods, they need to evaluate noise environment for constructing a reference point. However, the reference point is fixed, which is unreasonable in dynamic cognitive radio environment. Moreover, these methods convert signal feature from manifold onto Euclidean space, which will cause to overall performance degradation, since sensing information losses. To address this problem, an information geometry (IG)-based K-means clustering algorithm, namely IGK, is developed, it clusters samples on manifold instead of Euclidean space with an unsupervised way to train a classifier for spectrum sensing. Specifically, secondary users observe and collect data from a selected authorized spectrum, which needs to be detected, and send these sensing data to a fusion center (FC). Then, the FC transforms these data into samples on the manifold to obtained a classifier by using the proposed IGK algorithm. According to the trained classifier, we can get related result of the authorized spectrum. Finally, in simulation section, the effectiveness of the proposed scheme is verified under different conditions.