Cooperative parallel spectrum sensing in cognitive radio networks using bipartite matching

Spectrum sensing is one of the key steps for implementing the cognitive radio-based systems. The efficiency and the effectiveness of spectrum sensing have a profound impact on the performance of the cognitive users. In this paper, we propose two cooperative-parallel spectrum sensing algorithms. The cooperation greatly reduces the sampling time for each secondary user and increases the efficiency. Our proposed algorithms utilize adaptive schemes as well as the graph theoretical analysis to obtain the best strategy for channel sensing in the secondary users. In this work, we model the cooperative spectrum sensing problem with a bipartite graph. Assigning channel sensing tasks to the secondary users corresponds to finding the perfect matching on that graph. Two different algorithms are developed based on the different complexity levels of the underlying matching algorithms. The performances of these algorithms are compared with each other and with other related algorithms from the literature.