Cooperative Spectrum Sensing Using Weighted Graph Sparsity

Abstract

Graph has been proven to be an emerging tool for spectrum sensing (SS), with detection performance closely related to the graph characteristics. Existing graph-based SS has been mainly investigated based on the unweighted graph for single user scenario, which leads to the poor performance at the low signal-to-noise. To address this issue, we introduce a weighted graph-based cooperative spectrum sensing method in this letter. Specifically, a signal-to-weighted-graph (STWG) mechanism for multi-user is proposed, which converts the signals of different users into a single weighted graph. To characterize the features of the weighted graph, graph sparsity is employed to represent the graph connectivity, upon which a test statistic is constructed. Moreover, a simple but practical method is proposed to estimate the detection threshold. Experimental results verify the theoretical analysis and demonstrate the superior performance of the proposed method.