摘要
针对认知无线电中频谱感知问题,利用随机矩阵理论(random matrix theory,RMT)最新研究成果,提出了一种基于采样协方差矩阵最小特征值(smallest eigenvalue,SE)的合作频谱感知新算法。该算法采用的最小特征值分布函数比目前所采用的最大特征值的近似分布函数更精确。理论分析表明,与MED(maximum eigenvalue detection)算法和能量检测法(ED)相比,SE算法具有合理性更强、判决门限更低以及感知灵敏度更高的特点。仿真结果也显示,该算法不仅漏检概率更低,感知性能更好,而且在认知用户数较少、样本较小的情况下,也可获得较好的检测性能。
In this paper,a novel cooperative spectrum sensing algorithm based on the smallest eigenvalue（SE） of the received signal covariance matrix is presented.The SE scheme makes use of the probability distribution function of the smallest eigenvalue,which is much more accurate than that of the maximum eigenvalue;therefore,the proposed algorithm theoretically has stronger rationality,lower decision threshold and higher sensitivity compared with MED and ED schemes.In addition,numerical simulations also show that SE not only has lower probability of missing detection and better sensing performance,but also obtains higher detection performance with a few secondary users and samples.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2011年第4期736-741,共6页
Chinese Journal of Scientific Instrument
基金
国家863计划(2009AA01Z241)
国家自然科学基金(61071092
61001077)
南京邮电大学校科研基金(NY210035)资助项目
关键词
认知无线电
合作频谱感知
随机矩阵理论
采样协方差矩阵
最小特征值
cognitive radio
cooperative spectrum sensing
random matrix theory
sample covariance matrix
smallest eigenvalue
