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计算机工程

• 移动互联与通信技术 • 上一篇    下一篇

基于特征值极限分布的双门限DMM频谱感知算法

高鹏 1,刘芸江 1,高维廷 1,李曼 2   

  1. (1.空军工程大学 信息与导航学院,西安 710077; 2.西安航空学院,西安 710077)
  • 收稿日期:2017-01-17 出版日期:2017-09-15 发布日期:2017-09-15
  • 作者简介:高鹏(1992—),男,硕士研究生,主研方向为认知无线电频谱感知;刘芸江,副教授、博士;高维廷,讲师、博士;李曼,副教授、博士。
  • 基金资助:
    国家自然科学基金(61571364);中国博士后科学基金(2016M603044)。

Double Threshold DMM Spectrum Sensing Algorithm Based on Limiting Eigenvalue Distribution

GAO Peng 1,LIU Yunjiang 1,GAO Weiting 1,LI Man 2   

  1. (1.College of Information and Navigation,Air Force Engineering University,Xi’an 710077,China; 2.Xi’an Aeronautical University,Xi’an 710077,China)
  • Received:2017-01-17 Online:2017-09-15 Published:2017-09-15

摘要: 采用随机矩阵特征结构理论,分析并研究多认知用户采样协方差矩阵的特征极限值分布,提出一种基于最大最小特征值之差(DMM)的双门限频谱感知算法。根据最大与最小特征极限值分布推导检测双门限,对双门限内外部分分别采用软判决与硬判决综合得到最终判决结果。利用特征值噪声估计实现检测门限的自适应,克服噪声不确定性对频谱感知的影响。仿真结果表明,在低信噪比、虚警率和采样频率的情况下,该算法检测性能优于DMM算法与能量检测算法,且稳定性好、鲁棒性强。

关键词: 认知无线电, 频谱感知, 随机矩阵理论, 特征值极限分布, 最大最小特征值之差

Abstract: This paper uses the Random Matrix Theory(RMT) of eigen structure,analyzes and researches the limiting eigenvalue distribution of sampling covariance matrix for multiple cognitive users,and proposes a double threshold spectrum sensing algorithm based on Difference Between the Maximum Eigenvalue and the Minimum Eigenvalue(DMM).The double thresholds are obtained by using the limiting eigenvalue distribution of both maximum and minimum eigenvalues.The soft decision and hard decision are adopted in both internal and external parts of the double threshold to achieve the final decision result.The self-adaptability of detected thresholds is realized by using eigenvalue noise estimation,which overcomes the impact of noise uncertainty on spectrum sensing.The simulation result shows that the algorithm has better detection performance than the DMM algorithm and Energy Detection(ED) algorithm under the situation of low signal noise ratio,low false alarm probability and relatively small number of sampling points,and it has good stability and strong robustness.

Key words: Cognitive Radio(CR), spectrum sensing, Random Matrix Theory(RMT), limiting eigenvalue distribution, Difference Between the Maximum Eigenvalue and the Minimum Eigenvalue(DMM)

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