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Intelligent clustering cooperative spectrum sensing based on Bayesian learning for cognitive radio network

机译：基于贝叶斯学习的认知无线电网络智能聚类协作频谱感知

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摘要

In cognitive radio network, cooperative spectrum sensing (CSS) can improve the sensing performance on the absence of a primary user, when the sensing channel is in severe fading and shadowing effect. However, CSS is sensitive to the fading reporting channel. In this paper, an intelligent clustering CSS based on Bayesian learning is proposed to improve sensing performance under both perfect and imperfect sensing reports as well as decrease the rate loss and cooperative overhead. The clustering CSS is performed by intra-cluster CSS and inter-cluster CSS. An optimal sensing threshold for the intra-cluster CSS is achieved by minimizing the total Bayesian cost. The total false alarm probability and detection probability for the inter-cluster CSS are obtained by the Bayesian fusion. A clustering algorithm based on K-means learning is proposed to classify the sensing nodes and select the cluster heads. The simulation results have shown that the proposed clustering CSS outperforms the traditional CSS without clustering in the aspects of sensing performance and time overhead. (C) 2019 Elsevier B.V. All rights reserved.
机译：在认知无线电网络中，当感测信道处于严重衰落和阴影效应时，协作频谱感测（CSS）可以在没有主要用户的情况下提高感测性能。但是，CSS对衰落的报告渠道很敏感。本文提出了一种基于贝叶斯学习的智能聚类CSS，以提高感知报告在完美和不完美情况下的感知性能，并减少速率损失和协作开销。集群CSS由集群内CSS和集群间CSS执行。通过使总贝叶斯成本最小化，可以实现集群内CSS的最佳检测阈值。集群间CSS的总虚警概率和检测概率通过贝叶斯融合获得。提出了一种基于K均值学习的聚类算法，对感知节点进行分类，选择簇头。仿真结果表明，在感知性能和时间开销方面，所提出的聚类CSS优于传统的聚类CSS。 （C）2019 Elsevier B.V.保留所有权利。

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