基于多目标遗传算法的CRN集群协同频谱感知能量最小化

Ibrahim Salah, W. Saad, M. Shokair, M. Elkordy
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引用次数: 7

摘要

协同频谱感知能够提高频谱感知性能,降低衰落信道和阴影信道的误差概率,是认知无线电网络的重要组成部分。实际上,将聚类方案与协同频谱感知相结合,减少了报告信道的拥挤,提高了感知性能,降低了计算成本。基于聚类技术,提出了许多协同频谱感知方法。本文提出了一种基于聚类的方法,以最小化CRN的总功耗,从而进行频谱感知,将决策发送给簇头,并将最终决策发送给融合中心。该算法采用多目标遗传算法。仿真结果表明,与传统的基于聚类的协同频谱感知方案相比,该算法能获得更好的能量增益。此外,它还提高了CRN的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Minimizing energy of cluster-based Cooperative Spectrum Sensing in CRN using Multi Objective Genetic Algorithm
Cooperative spectrum sensing assumes an essential part in cognitive radio network due to having the capacity to enhance spectrum sensing performance and reduce probability of error in fading and shadowing channels. In fact, clustering scheme and cooperative spectrum sensing are combined to reduce Jostle of reporting channel, improve performance of sensing and reduce the computational cost. Many methods of cooperative spectrum sensing have been proposed based on clustering technique. In this paper, proposed approach will be suggested based on clustering to minimize the total power consumed by CRN in order to perform spectrum sensing, transmit decision to cluster head, and transmit the final decision to the fusion center. This is done by using multi objective genetic algorithm. Simulation results show that our proposed algorithm can achieve better energy gain which is less than conventional cluster based cooperative spectrum sensing scheme. Moreover, it increases performance of CRN.
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