Probabilistical Robust Power Control for Cognitive Radio Networks under Interference Uncertainty Conditions

Lingling Chen, Zhiyi Fang, Xiaohui Zhao
{"title":"Probabilistical Robust Power Control for Cognitive Radio Networks under Interference Uncertainty Conditions","authors":"Lingling Chen, Zhiyi Fang, Xiaohui Zhao","doi":"10.3991/IJOE.V14I07.8967","DOIUrl":null,"url":null,"abstract":"<p class=\"0abstract\"><a name=\"OLE_LINK11\"></a><a name=\"OLE_LINK12\"></a><span lang=\"EN-US\">The focus of this paper is to find a robust power control strategy with uncertain noise plus interference (NI) in </span><span lang=\"EN-US\">cognitive radio networks (</span><span lang=\"EN-US\">CRNs</span><span lang=\"EN-US\">)in an </span><span lang=\"EN-US\">under orthogonal frequency-division multiplexing (OFDM) framework. The optimization problem is formulated to maximize </span><span lang=\"EN-US\">the </span><span lang=\"EN-US\">data rate of secondary users (SUs) under the constraints o</span><span lang=\"EN-US\">f</span><span lang=\"EN-US\"> transmission power of each SU, probabilistic the transmit rate of each SU at each subcarrier and robust interference constraint of primary user. In consideration of the feedback errors from the quantization </span><span lang=\"EN-US\">due to</span><span lang=\"EN-US\"> uniform distribution, the probabilistic constraint is transformed into closed forms. By using Lagrange relaxation of the coupling constraints method and subgradient iterative algorithm in a distributed way, we solve this dual problem. Numerical simulation results show that our proposed algorithm is superior to the robust power control scheme based on interference gain worst case approach and non-robust algorithm without quantization error in perfect channels in the improvement of data rate of each SU, convergence speed and computational complexity.</span></p>","PeriodicalId":387853,"journal":{"name":"Int. J. Online Eng.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Online Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3991/IJOE.V14I07.8967","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The focus of this paper is to find a robust power control strategy with uncertain noise plus interference (NI) in cognitive radio networks (CRNs)in an under orthogonal frequency-division multiplexing (OFDM) framework. The optimization problem is formulated to maximize the data rate of secondary users (SUs) under the constraints of transmission power of each SU, probabilistic the transmit rate of each SU at each subcarrier and robust interference constraint of primary user. In consideration of the feedback errors from the quantization due to uniform distribution, the probabilistic constraint is transformed into closed forms. By using Lagrange relaxation of the coupling constraints method and subgradient iterative algorithm in a distributed way, we solve this dual problem. Numerical simulation results show that our proposed algorithm is superior to the robust power control scheme based on interference gain worst case approach and non-robust algorithm without quantization error in perfect channels in the improvement of data rate of each SU, convergence speed and computational complexity.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
干扰不确定性条件下认知无线网络的概率鲁棒功率控制
本文的重点是在非正交频分复用(OFDM)框架下,寻找具有不确定噪声加干扰(NI)的认知无线网络(crn)的鲁棒功率控制策略。在每个辅助用户的传输功率、每个辅助用户在每个子载波上的传输速率的概率性和主用户的鲁棒性干扰约束的约束下,提出了辅助用户数据速率最大化的优化问题。考虑到均匀分布导致的量化反馈误差,将概率约束转化为封闭形式。利用拉格朗日耦合约束松弛法和分布的次梯度迭代算法,解决了这一对偶问题。数值仿真结果表明,该算法在提高各SU的数据速率、收敛速度和计算复杂度方面均优于基于干扰增益最坏情况法的鲁棒功率控制方案和完美信道下无量化误差的非鲁棒算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Infrared-based Short-Distance FSO Sensor Network System Real-Time Image Transmission Algorithm in WSN with Limited Bandwidth Path Planning for Unmanned Underwater Vehicle Based on Improved Particle Swarm Optimization Method Computer Assisted E-Laboratory using LabVIEW and Internet-of-Things Platform as Teaching Aids in the Industrial Instrumentation Course Towards Simulation Aided Online Teaching: Material Design for Applied Fluid Mechanics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1