基于双特征值极限分布的合作频谱感知算法

doi:10.3969/j.issn.1000-0801.2014.04.012

摘要/Abstract

摘要：

以最大最小特征值之差作为检测统计量，从提高算法判决门限估计精度出发，利用双特征值极限分布，用双特征值估计判决门限，提出了基于双特征值极限分布的频谱感知算法。相比于单特征值门限方法，理论上证明了用双特征值极限分布估计的门限小于或等于用单特征值极限分布估计的门限，提高了算法检测性能。仿真结果表明，该算法不受噪声不确定性影响，不需要主用户信息，并且在低虚警概率、采样次数相对少的情况下，检测性能优于最大最小特征值之差频谱感知算法。

关键词: 频谱感知, 随机矩阵理论, 双特征值极限分布, 门限估计

Abstract:

A spectrum sensing algorithm using double eigenvalue limiting（DEL）distributions was presented. The difference between the maximum and the minimum eigenvalue was exploited as the test statistic. The double eigenvalue were used to estimate a threshold based on the double eigenvalue limiting distributions to heighten an accuracy of the threshold. Compared with the algorithm of single eigenvalue, the theoretical analyses demonstrate that the threshold estimated by DEL was less than its threshold and the performance of DEL was improved. In addition, simulations results show the proposed algorithm needs neither the prior acknowledge of the signal transmitted from primary user, nor the noise power in advance, and gains better performance against the short of sampling number than the difference between the maximum and the minimum eigenvalue（DMM）algorithm.

Key words: spectrum sensing, random matrix theory, double eigenvalue limiting distribution, threshold estimation

赵知劲,胡伟康,王海泉. 基于双特征值极限分布的合作频谱感知算法[J]. 电信科学, 2014, 30(4): 82-87.

Zhijin zhao,Weikang Hu,Haiquan Wang. Cooperative Spectrum Sensing Algorithm Based on Double Eigenvalue Limiting Distribution[J]. Telecommunications Science, 2014, 30(4): 82-87.

图/表 5

参考文献 19

1	Akyildiz I F , Lee W , Vuran M C , et al. Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Computer Networks, 2006, 50（13）: 2127～2159
2	Mitola J , Maguire G Q . Cognitive radio: making software radios more personal. IEEE Personal Communications, 1999,6（4）: 13～18
3	Cardoso L S , Debbah M , Bianchi P , et al. Cooperative spectrum sensing using random matrix theory. Proceedings of International Symposium on Wireless Pervasive Computing, Santorini, Greece, May 2008: 334～338
4	顾彬，杨震，胡海峰. 基于压缩感知信道能量观测的协作频谱感知算法. 电子与信息学报， 2012, 34（1）: 14～19
5	金明，李有明，高洋. 基于压缩感知信道能量观测的协作频谱感知算法. 通信学报， 2013, 34（1）: 105～110
6	庞娜. 认知无线电频谱感知技术研究. 北华大学学报（自然科学版）, 2012, 13（5）: 609～612
7	Zeng Y H , Liang Y C . Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 2009, 57（6）: 1784～1793
8	Zeng Y H , Liang Y C . Maximum rigenvalue detection theory and application. Proceedings of IEEE International Conference on Communications, Beijing, China, May 2008: 4160～4164
9	Wang L , Zheng B Y , Cui J W , et al. Cooperative spectrum sensing using free probability theory. Proceedings of IEEE Global Telecommunications Conference, Honolulu, USA, 2009: 1～5
10	许炜阳，徐婷．认知无线电系统中存在载波频偏的OFDM 频谱感知技术. 电子科技大学学报， 2013, 42（3）: 355～359
11	Penna F , Garello R , Spirito M A . Probability of missed detection in eigenvalue ratio spectrum sensing. Proceedings of IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, Marrakech,Morocco, Oct 2009: 117～122
12	王润亮，李浩，黄焱等. 一种基于最大最小特征值的频谱感知改进算法. 计算机应用研究， 2012, 29（7）: 2638～2641
13	曹开田，杨震. 基于随机矩阵理论的DET合作频谱感知算法. 电子与信息学报， 2010, 32（1）: 129～134
14	王颖喜，卢光跃. 基于最大最小特征值之差的频谱感知技术研究. 电子与信息学报， 2010, 32（11）: 2571～2575
15	邹卫霞，丁奇，周正等. 基于特征值极限分布的双门限频谱感知算法. 系统工程与电子技术， 2012, 34（3）: 588～591
16	Tulina A M , Verdu S . Random Matrix Theory and Wireless Communication. Hanover: Now Publisher Inc 2004
17	Johansson K . Shape fluctuations and random matrices. Communications in Mathematical Physics, Marrakech,Morocco, 2000, 209（2）: 437～476
18	Johnstone I M . On the distribution of the largest eigenvalue in principlecomponents analysis. The Annals of Statistics, 2001, 29（2）: 295～327
19	Penna F , Garello R . Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in Wishart matrices. IEEE Communications Letters, 2009, 13（7）: 507～509

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