认知云网络中基于相位补偿的协同频谱感知算法

Li Wang, Sissi Xiaoxiao Wu, Shibing Zhang, Guodong Zhang, Zhihua Bao
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引用次数: 0

摘要

频谱感知是认知无线电网络中的关键技术。针对认知云网络中的频谱感知问题,提出了一种基于相位补偿的协同频谱感知算法,以提高认知云网络的频谱感知性能。该算法将感知节点接收到的信号发送到云端,进行相位补偿并合并为一个信号。然后,使用组合信号来检验假设,主要用户是否存在。它充分利用了所有感知节点的可用信息,有效地提高了频谱检测的性能。仿真结果表明,该算法在信噪比上比其他算法有2 ~ 4db的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cooperative Spectrum Sensing Algorithm Based on Phase Compensation in Cognitive Cloud Networks
Spectrum sensing is the key technology in cognitive radio networks. This paper focused on the spectrum sensing in cognitive cloud networks and proposed a cooperative spectrum sensing algorithm based on phase compensation to improve the spectrum sensing performance. In the algorithm, the received signals in the sensing nodes are sent to the cloud, compensated in phase and combined into one signal. Then, the combined signal is used to test the hypothesis, whether the primary user is present. It uses all available information of all sensing nodes and improves the performance of spectrum detection effectively. The simulation results show that the proposed algorithm has 2–4 dB advantage in signal-to-noise ratio over other algorithms.
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