基于叠加法和n-out- k规则的af -认知无线网络和速率最大化

Md. Sipon Miah, M. Schukat, E. Barrett
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引用次数: 9

摘要

频谱感知策略在放大前向认知无线网络中起着至关重要的作用。然而,当现有的传感策略应用于AF-CRN时,AF-CRN无法获得最大的吞吐量。在本文中,我们提出了一种af - crn中的叠加方法,其中首先,辅助用户(SU)将其感知时间延长到其报告时隙开始之前,然后每个SU将包含放大报告的测量结果发送给簇头(CH),而具有软融合报告的CH则转发给融合中心(FC)。利用这种扩展的感知间隔和放大的报告,可以获得比传统刚性策略更好的感知性能。除此之外,还研究了叠加法和n-out- k规则的主网络和次网络的和速率。数值实验表明,与传统策略相比,该策略在任何条件下都能保证最大的和率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximization of sum rate in AF-cognitive radio networks using superposition approach and n-out-of-k rule
The spectrum sensing strategy plays a vital role in Amplify-Forward (AF)-Cognitive Radio Networks (CRNs). However, AF-CRN cannot obtain maximal throughput, when existing sensing strategies are applied to AF-CRNs. In this paper, we present a superposition approach in AF-CRNs, in which firstly a Secondary User (SU) extends its sensing time until right before the beginning of its reporting time slot, and secondly each SU sends its measurement results containing amplified reports to the Cluster Head (CH), while the CH with soft-fusion report is forwarded to the Fusion Center (FC). With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained than with the conventional rigid strategy. In addition to this, the sum rate of primary and secondary networks is also investigated for the superposition approach and the n-out-of-k rule. Numerical experiments show that the proposed strategy guarantees maximum sum rate compare to the conventional strategy under any condition.
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