基于最小特征值分布的频谱感知算法

doi:10.11772/j.issn.1001-9081.2015.02.0354

计算机应用 ›› 2015, Vol. 35 ›› Issue (2): 354-357.DOI: 10.11772/j.issn.1001-9081.2015.02.0354

基于最小特征值分布的频谱感知算法

杨智, 徐家品

四川大学电子信息学院, 成都 610065

收稿日期:2014-08-25 修回日期:2014-11-13 出版日期:2015-02-10 发布日期:2015-02-12
通讯作者: 徐家品
作者简介:杨智(1987-),男,湖南岳阳人,硕士,主要研究方向:通信与信息系统; 徐家品(1957-),男,四川成都人,教授,主要研究方向:通信与信息系统。

Spectrum sensing algorithm based on least eigenvalue distribution

YANG Zhi, XU Jiapin

College of Electronics and Information Engineering, Sichuan University, Chengdu Sichuan 610065, China

Received:2014-08-25 Revised:2014-11-13 Online:2015-02-10 Published:2015-02-12

摘要/Abstract

摘要：

现有的频谱感知算法中,能量检测容易实现,但检测性能依赖噪声功率。基于随机矩阵理论的频谱感知算法巧妙地规避了噪声不确定性对检测性能带来的影响,但大都采用的是最大特征值的近似分布规律,所得到阈值表达式的精度有待进一步提高。针对上述问题,通过利用随机矩阵理论的最新研究成果,提出一种基于接收信号样本协方差矩阵最小特征值分布的频谱感知算法。最小特征值的分布函数不基于渐近假设,更加符合实际的通信情境。推导所得的阈值表达式是虚警概率的函数,在小样本情况下,对它的有效性和优越性进行了分析与验证。根据单一变量原则,分别在低样本点、低协作用户数、低信噪比和低虚警概率条件下对提出算法与最大最小特征值算法的检测性能进行了仿真比较,检测概率最多可以提高0.2左右。结果表明,该算法能够显著改善系统的检测性能。

关键词: 频谱感知, 随机矩阵理论, 阈值表达式, 样本协方差矩阵, 最小特征值

Abstract:

Among the existing spectrum sensing algorithms, energy detection can be implemented easily, but its detection performance depends on noise power. Spectrum sensing algorithms based on random matrix theory can skillfully avoid the influence of noise uncertainty on detection performance, but most of them make use of approximate distribution of the largest eigenvalue. The accuracy of threshold expression derived from it needs to be further improved. Aiming to above problems, by using the latest research results about random matrix theory, a spectrum sensing algorithm based on distribution of the least eigenvalue of sample covariance matrix of received signals was proposed. Cumulative distribution function of the least eigenvalue is not based on asymptotical assumptions, which is more suitable for realistic communication scenarios. The threshold expression derived from it was a function of false alarm probability, whose effectiveness and superiority were analyzed and verified with few samples. Simulations complied with single variable principle were conducted under the situation of few samples, few collaborative users, low signal to noise ratio and low false alarm probability, in comparison with classic maximum-minimum eigenvalue algorithm. Detection probability of the proposed algorithm was increased by 0.2 or so. The results show that the proposed algorithm can significantly improve the detection performance of system.

Key words: spectrum sensing, random matrix theory, threshold expression, sample covariance matrix, least eigenvalue

中图分类号:

TN92

杨智, 徐家品. 基于最小特征值分布的频谱感知算法[J]. 计算机应用, 2015, 35(2): 354-357.

YANG Zhi, XU Jiapin. Spectrum sensing algorithm based on least eigenvalue distribution[J]. Journal of Computer Applications, 2015, 35(2): 354-357.

参考文献

[1] URKOWITZ H. Energy detection of unknown deterministic signals [J]. Proceedings of the IEEE, 1967, 55(4): 523-531.
[2] ZHANG W, MALLIK R K, LETAIEF K. Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2009, 8(12): 5761-5766.
[3] LIU S, CHEN C, LI H, et al. Double thresholds energy detection based cooperative spectrum sensing for cognitive radios [J]. Chinese Journal of Radio Science, 2013, 28(3): 420-424. (刘仕奇, 陈崇, 李海宏, 等. 基于双门限能量检测的认知无线电协作频谱感知[J]. 电波科学学报, 2013, 28(3): 420-424.)
[4] ZHU Q, SONG C, TAN C, et al. Double-threshold cooperative spectrum sensing algorithm based on multi-fusion rule [J]. Journal of Computer Applications, 2011, 31(8): 2040-2043. (朱卿,宋春林,谈彩萍, 等. 基于多融合准则的双门限协作频谱感知算法[J]. 计算机应用, 2011, 31(8):2040-2043.)
[5] CARDOSO L S, DEBBAH M, BIANCHI P, et al. Cooperative spec-trum sensing using random matrix theory [C]//ISWPC 2008: Proceedings of the 3rd International Symposium on Wireless Pervasive Computing. Piscataway: IEEE, 2008: 334-338.
[6] ZENG Y, LIANG Y. Eigenvalue-based spectrum sensing algorithms for cognitive radio [J]. IEEE Transactions on Communications, 2009, 57(6): 1784-1793.
[7] WANG L, ZHENG B, LI L. Cooperative spectrum sensing based on random matrix theory [J]. Journal of Electronics and Information Technology, 2009, 31(8): 1925-1929. (王磊, 郑宝玉, 李雷. 基于随机矩阵理论的频谱感知[J]. 电子与信息学报, 2009, 31(8): 1925-1929.)
[8] YANG M, AN J, BU X, et al. An improved eigenvalue-based algorithm for cooperative spectrum sensing [C]//WiCOM 2010: Proceedings of the 2010 6th International Conference on Wireless Communications Networking and Mobile Computing. Piscataway: IEEE, 2010: 1-4.
[9] SAHAI A, HOVEN N, TANDRA R. Some fundamental limits on cognitive radio [C/OL]//Proceedings of the Forty-First Annual Allerton Conference on Communication, Control, and Computing. Urbana: University of Illinois at Urbana-Champaign, Coordinated Science Laboratory, 2003. [2014-06-22]. http://www.eecs.berkeley.edu/~sahai/Papers/cognitive_radio_preliminary.pdf.
[10] CABRIC D, MISHRA S M, BRODERSEN R W. Implementation issues in spectrum sensing for cognitive radios [C]//Proceedings of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers. Piscataway: IEEE, 2004, 1: 772-776.
[11] SONNENSCHEIN A, FISHMAN P M. Radiometric detection of spread-spectrum signals in noise of uncertain power [J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(3): 654-660.
[12] ZANELLA A, CHIANI M, WIN M Z. Performance of MIMO MRC in correlated rayleigh fading environments [C]//VTC 2005-Spring: Proceedings of the 2005 IEEE 61st Vehicular Technology Conference. Piscataway: IEEE, 2005, 3: 1633-1637.
[13] PENNA F, GARELLO R, FIGLIOLI D, et al. Exact non-asymptotic threshold for eigenvalue-based spectrum sensing [C]//CROWNCOM '09: Proceedings of the 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications. Piscataway: IEEE, 2009: 1-5.
[14] TULINO A M, VERDU S. Random matrix theory and wireless communications [M]. Boston: Now Publishers Inc., 2004: 6-13.

基于最小特征值分布的频谱感知算法

Spectrum sensing algorithm based on least eigenvalue distribution

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