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.

中文翻译：

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一种在认知无线电网络中考虑绿色通信和卓越感知性能的新算法

多节点协同感知可以有效提高频谱感知的性能。多节点协作会产生大量的本地数据，每个节点都会向融合中心发送自己的感知数据。融合中心将融合局部感知结果并做出全局决策。因此，节点越多，数据越多，当节点数量大时，全局决策会延迟。为了实现实时频谱感知，融合中心需要快速融合各个节点的数据。本文提出了一种大数据融合的快速算法，以提高全局决策的实时性。该算法通过减少重复计算来提高计算速度。强化学习机制用于标记处理后的数据。当出现相同的环境参数时，融合中心可以直接调用参数环境下的节点，无需再次进行感知操作。这大大减少了处理的数据量，提高了融合中心的数据处理效率。实验结果表明，本文算法可以在提高感知性能的同时减少计算时间。