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李鹏,闵慧.CRSNs中基于传感器选择的高能效频谱感知算法[J].重庆邮电大学学报(自然科学版),2018,30(6):760-767. 本文二维码信息
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CRSNs中基于传感器选择的高能效频谱感知算法
High energy efficient spectrum sensing algorithm based on sensor selection in cognitive radio sensor networks
投稿时间:2018-01-09  修订日期:2018-11-02
DOI: 10.3979/j.issn.1673-825X.2018.06.005
中文关键词:  认知无线电传感器网络  能耗  频频感知  传感器  聚类  启发式算法
English Keywords:cognitive radio sensor networks  energy consumption  spectrum sensing  sensor  clustering  heuristic algorithm
基金项目:国家重点研发项目(2017YFC1703300);湖南省教育厅优秀青年基金(13B079)
作者单位E-mail
李鹏 湖南中医药大学 信息科学与工程学院,长沙 410208 lpchs617@csu.edu.cn 
闵慧 湖南信息职业技术学院 计算机工程系,长沙 410200  
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中文摘要:
      协同频谱感知(cooperative spectrum sensing, CSS)是认知无线电传感器网络中解决频谱资源稀缺的主要手段,针对现有频谱感知方法在降低能耗方面存在的不足,考虑一种从主要用户接收的具有不同的信噪比值的传感器所组成的真实网络场景,提出一种基于传感器选择的高能效频谱感知算法。该算法将可被传感器用来传输数据的信道按时间分成相等的框架,每框架中包含3个阶段:频谱感知、报告和数据传输;通过聚类得到所有能够满足CSS所需精度的传感器子集,并计算在每次协同频谱感知中这些传感器的平均能耗;在考虑传感器剩余能量的基础上,将每个框架中使用最少数量的传感器参与CSS过程建模为面向能耗的优化问题,并提出一种启发式算法进行求解。仿真实验结果表明,与现有的其他频谱感知方法相比,提出的算法在能量利用方面具有更好的性能。
English Summary:
      Cooperative spectrum sensing (CSS) is the main means to solve the scarcity of spectral resources in cognitive radio sensor networks. In view of the shortcomings of existing spectrum sensing methods in reducing energy consumption, a real network scene is composed of sensors with different Signal-Noise Ratio values received from the primary users is considered in this paper, a high energy efficiency spectrum sensing algorithm based on sensor selection is proposed. Firstly, the channel used for data transmission by sensors is divided into equal frames according to the time. Each frame contains three phases: spectrum sensing, reporting and data transmission. Then, all the sensor subsets which can satisfy the precision of CSS are obtained by clustering, and the average energy consumption of these sensors is calculated in each CSS. Finally, considering the residual energy of sensors, the process of using the least number of sensors in each frame to participate in the CSS is modeled as an optimization problem for energy consumption, and a heuristic algorithm is proposed to solve it. The simulation results show that the proposed algorithm has better performance in terms of energy utilization compared with other existing spectrum sensing methods.
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