Penalty based weighted cooperative spectrum sensing using normal factor graph

Guman Kanwar Shekhawat, P. Karmakar
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引用次数: 1

Abstract

In this paper, we have considered the problem of spectrum sensing in Cognitive Radio Networks (CRN). In these networks, Cooperative Spectrum Sensing (CSS) technique is used to overcome the problem of hidden terminal, fading and shadowing. In this paper, we have proposed the Weighted Cooperative Spectrum Sensing (WCSS) framework using Normal Factor Graph (NFG). The WCSS framework in CRN improves the sensing ability and accuracy of the network. The proposed probabilistic inference modeling of WCSS algorithm deals with the up gradation of weight factor values based on Secondary Users (SU) performance. Simulation result shows that the performance of the proposed scheme is better than the performance of CSS using NFG in time varying fading environment.
基于惩罚的法向因子图加权协同频谱感知
本文研究了认知无线网络(CRN)中的频谱感知问题。在这些网络中,采用协同频谱感知技术(CSS)来克服终端隐藏、衰落和阴影等问题。本文提出了一种基于正态因子图(NFG)的加权协同频谱感知框架。CRN中的WCSS框架提高了网络的感知能力和精度。所提出的WCSS算法的概率推理建模处理了基于辅助用户(Secondary Users, SU)性能的权重因子值的上阶问题。仿真结果表明,该方案在时变衰落环境下的性能优于基于NFG的CSS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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