A new algorithm for considering green communication and excellent sensing performance in cognitive radio networks

Abstract

Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance.