底层认知无线电非正交多址网络中继选择方案分析

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Distributed Sensor Networks Pub Date : 2022-05-01 DOI:10.1177/15501477211066304
Dongmei Yang, Cheng Li, Baoquan Ren, Hongjun Li, K. Guo
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引用次数: 1

摘要

本文研究了中继选择方案对协同底层认知无线电非正交多址网络的影响,该网络采用部分中继选择方案、最大最小中继选择方案和两级中继选择方案。此外,在传输中继上使用译码转发协议。此外,为了说明各方案对所考虑的网络的影响,导出了系统中断概率的封闭表达式和渐近表达式。进一步分析了完全和不完全连续干扰对消作用下的中断性能。数值结果说明了继电器选择方案、继电器个数、剩余干扰因数和功率分配因数对停电性能的影响。最后通过蒙特卡罗仿真验证了数值结果的准确性。
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Analysis of relay selection schemes in underlay cognitive radio non-orthogonal multiple access networks
This article investigates the impacts of relay selection schemes on cooperative underlay cognitive radio non-orthogonal multiple access networks, where the partial relay selection scheme, the max–min relay selection scheme and the two-stage relay selection scheme are applied in the network. Moreover, decode-and-forward protocol is used at the transmission relays. What’s more, in order to show the effect of the schemes on the considered network, the closed-form expressions and asymptotic expressions for the outage probability of the system are derived. Furthermore, the outage performance under the effect of perfect and imperfect successive interference cancellation is analysed. Numerical results are given to illustrate the impacts of the relay selection schemes, the number of relays, the residual interference factor and the power allocation factor on the outage performance. Finally, Monte Carlo simulations are presented to validate the accuracy of the numerical results.
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来源期刊
CiteScore
6.50
自引率
4.30%
发文量
94
审稿时长
3.6 months
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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