Wireless sensor network enhances the classic features of wireless communication with cognitive capabilities for efficient spectrum usage. This work focuses on the dynamic correlation between the secondary users (SUs) based on their statistical behaviour while performing the cooperative communication in cognitive radio sensor network. The proposed approach addresses the problem of uneven and repetitive communication between the SUs in a cooperative communication scenario. The authors’ objective is to use a novel approach based on the Gaussian copula theory and advanced particle swarm optimisation algorithm to analyse the dependencies of time-varying spectrum sensing behaviour of multiple SUs. Here, time delay in prediction reduces due to the analysis of the dynamic correlation between the time delay in spectrum sensing results for the same set of channels. The simulation results show the performance of the proposed approach outperforming the other well-known techniques.

中文翻译：

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基于自适应粒子群优化的认知无线电传感器网络中次级节点的能效动态相关行为

无线传感器网络通过认知功能增强了无线通信的经典功能，可有效利用频谱。这项工作着重于在认知无线电传感器网络中执行协作通信时，基于二级用户（SU）的统计行为之间的动态关联。所提出的方法解决了在协作通信场景中SU之间的通信不均匀和重复的问题。作者的目的是使用一种基于高斯copula理论和高级粒子群优化算法的新颖方法来分析多个SU的时变频谱感知行为的相关性。这里，由于分析了同一组信道的频谱感测结果中的时间延迟之间的动态相关性，因此预测的时间延迟减少了。仿真结果表明，该方法的性能优于其他知名技术。