Cross-layer design for cognitive radio sensor networks based on belief weight clustering

Abstract

In order to enhance the accuracy of spectrum sensing and achievable throughput of cognitive radio sensor networks, a cross-layer design scheme based on belief weight clustering is proposed. The clustering problem is mapped to constraint maximum-weight edge biclique decomposition problem based on belief weight. Then the transmission time and transmit power of the secondary users are combined optimized in each cluster through the cross-layer design. The optimal transmission time and transmit power allocation scheme are finally obtained. The simulation results show that, compared with the maximum weight unilateral bipartite graph algorithm (MWB), the algorithm proposed in this paper can significantly improve the perception performance under the premise of little difference in system throughput.