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基于随机矩阵理论的DET合作频谱感知算法

曹开田 杨震

曹开田, 杨震. 基于随机矩阵理论的DET合作频谱感知算法[J]. 电子与信息学报, 2010, 32(1): 129-134. doi: 10.3724/SP.J.1146.2009.00517
引用本文: 曹开田, 杨震. 基于随机矩阵理论的DET合作频谱感知算法[J]. 电子与信息学报, 2010, 32(1): 129-134. doi: 10.3724/SP.J.1146.2009.00517
Cao Kai-tian, Yang Zhen. DET Cooperative Spectrum Sensing Algorithm Based on Random Matrix Theory[J]. Journal of Electronics & Information Technology, 2010, 32(1): 129-134. doi: 10.3724/SP.J.1146.2009.00517
Citation: Cao Kai-tian, Yang Zhen. DET Cooperative Spectrum Sensing Algorithm Based on Random Matrix Theory[J]. Journal of Electronics & Information Technology, 2010, 32(1): 129-134. doi: 10.3724/SP.J.1146.2009.00517

基于随机矩阵理论的DET合作频谱感知算法

doi: 10.3724/SP.J.1146.2009.00517

DET Cooperative Spectrum Sensing Algorithm Based on Random Matrix Theory

  • 摘要: 针对认知无线电系统中的频谱感知问题,该文采用随机矩阵理论(Random Matrix Theory, RMT)对多认知用户(Secondary User, SU)接收信号采样协方差矩阵的最大特征值的分布特性进行了分析和研究,提出了一种新的基于双特征值判决门限(Double Eigenvalue Threshold, DET)的合作频谱感知算法。由该算法感知性能的理论分析可知:DET合作感知算法无需主用户(Primary User, PU)发射机信号的先验知识,也不需要预先知道信道背景噪声功率。仿真结果表明,与传统的频谱感知方法相比,该方法只需较少的认知用户就能获得较高的感知性能,并且对噪声的不确定性具有较强的鲁棒性。
  • [1] Akyildiz I F, Lee Won-Yeol, and Vuran M C, et al.. Nextgeneration/dynamic spectrum access/cognitive radio wirelessnetworks: A survey [J].Computer Networks.2006, 50(13):2127-2159 [2] Zeng Yong-hong, Koh Choo Leng, and Liang Ying-chang.Maximum eigenvalue detection theory and application [C].IEEE International Conference on Communications, Beijing,May 19-23, 2008: 4160-4164. [3] Unnikrishnan J and Veeravalli V V. Cooperative sensing forprimary detection in cognitive radio [J].IEEE Journal ofSelected Topics in Signal Processing.2008, 2(1):18-27 [4] Zhang Wei, Mallik R K, and Ben Letaief K. Cooperativespectrum sensing optimization in cognitive radio networks [J].IEEE International Conference on Communications, Beijing,May 19-23, 2008: 3411-3415. [5] Ma Jun, Zhao Guo-dong, and Li Ye. Soft combination anddetection for cooperative spectrum sensing in cognitive radionetworks [J]. IEEE Transactions on WirelessCommunications, 2008, 7(11): 4502-4507. [6] Tulino A M and Verd S. Random Matrix Theory andWireless Communications [M]. Hanover, USA: Now PublisherInc., 2004: 3-73. [7] Johnstone I M. On the distribution of the largest eigenvaluein principle components analysis [J]. The Annals of statistics,2001, 29(2): 295-327. [8] Johansson K. Shape fluctuations and random matrices [J].Communications in Mathematical Physics.2000, 209(2):437-476 [9] Tracy C A and Widom H. On orthogonal and symplecticmatrix ensembles [J]. Communications in MathematicalPhysics, 1996, 177(3): 727-754. [10] Baik J, Arous G B, and Peche S. Phase transition of thelargest eigenvalue for nonnull complex sample covariancematrices [J].The Annals of Probability.2005, 33(5):1643-1697 [11] Baik J and Silverstein J W. Eigenvalues of large samplecovariance matrices of spiked population models [J].Journalof Multivariate Analysis.2006, 97(6):1382-1408
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出版历程
  • 收稿日期:  2009-04-10
  • 修回日期:  2009-09-28
  • 刊出日期:  2010-01-19

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