| 基于分簇协作频谱感知的认知无线网能量效率研究 |
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| 引用本文： | 胡琳娜,蒋益锋,蔡雪.基于分簇协作频谱感知的认知无线网能量效率研究[J].数据采集与处理,2019,34(6):1086-1093. |
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| 作者姓名： | 胡琳娜 蒋益锋 蔡雪 |
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| 作者单位： | 1.南京理工大学紫金学院，南京，210046;2.河海大学计算机与信息学院，南京，210098;3.江苏理工学院信息中心，常州，213001;4.苏州大学电子信息学院，苏州，215006 |
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| 基金项目： | 国家自然科学基金
61701167┫资助项目
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江苏省高校“青蓝工程”资助项目
国家自然科学基金（61701167）资助项目；江苏省高校“青蓝工程”资助项目。 |
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| 摘 要： | 针对认知无线网能量消耗引起的能量效率较低问题，在研究认知无线网分簇协作频谱感知能量效率的基础上，提出了一种最优功率分配算法来最大化次用户系统的能量效率。通过建立基于次用户能量效率最大化传输优化模型，在考虑传输功率、感知时间以及干扰限制的情况下，利用拉格朗日函数及KKT条件，得到最优次用户发射功率分配算法，以达到系统能量效率最大化的目的，同时分析了不同参数对能量效率的影响。仿真结果表明，本文所提算法能有效提高次用户系统的能量效率、减少系统开销。
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| 关 键 词： | 认知无线网 协作频谱感知 分簇 能量效率 |
| 收稿时间： | 2019/6/12 0:00:00 |
| 修稿时间： | 2019/9/18 0:00:00 |
Research on Energy Efficiency of Cognitive Radio Network Based on Cluster Cooperative Spectrum Sensing |
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| Hu Linn,Jiang Yifeng,Cai Xue.Research on Energy Efficiency of Cognitive Radio Network Based on Cluster Cooperative Spectrum Sensing[J].Journal of Data Acquisition & Processing,2019,34(6):1086-1093. |
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| Authors: | Hu Linn Jiang Yifeng Cai Xue |
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| Affiliation: | 1.Zijin College, Nanjing University of Science & Technology, Nanjing, 210046, China;2.Computer & Information College, Hohai University, Nanjing, 210098, China;3.Information Center, Jiangsu University of Technology, Changzhou, 213001, China;4.School of Electronic and Information Engineering, Suzhou University, Suzhou, 215006, China |
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| Abstract: | Aiming at the low energy efficiency caused by the energy consumption of cognitive radio network, an optimal power allocation algorithm is proposed to maximize the energy efficiency of secondary user system based on clustering cooperative spectrum sensing. By establishing the energy efficiency maximization transmission model and considering transmission power, perceived time constraints and interference constraints, the optimal transmitted power allocation algorithm can be obtained to achieve the purpose of the system energy efficiency maximization through using Lagrange function and KKT condition. Then the influence of different parameters on energy efficiency is analyzed. Simulation results show that the proposed algorithm can reduce energy consumption and improve energy efficiency. |
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| Keywords: | cognitive radio network cooperative spectrum sensing clustering energy efficiency |
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