基于随机矩阵理论的非重构宽带压缩频谱感知方法  

Wideband Compressive Spectrum Sensing Without Reconstruction Based on Random Matrix Theory

作　　者：曹开田[1,2] 高西奇[1] 王东林 

机构地区：[1]东南大学移动通信国家重点实验室,南京210096 [2]南京邮电大学宽带无线通信与传感网技术教育部重点实验室,南京210003 [3]美国纽约理工学院(南京校区)电子与计算机工程系,南京210023

出　　处：《电子与信息学报》2014年第12期2828-2834,共7页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61201161;61271335);国家973计划项目(2011CB302903);江苏省博士后科研资助计划(1301002B)资助课题

摘　　要：该文采用随机矩阵理论(RMT)直接对压缩采样得到的观测数据进行分析,设计出了一种基于广义似然比检验(GLRT)的非重构宽带压缩频谱感知新算法。该算法无需任何先验知识就能对宽带频谱中的每个子带进行盲检测。此外,为了减轻次用户(SU)在数据获取和频谱感知过程中的通信开销,该文提出一种基于传感器节点(SN)辅助感知的合作频谱感知架构。理论分析和仿真结果均表明,与传统基于信号重构的GLRT感知算法以及Roy最大根检测(RLRT)算法相比,该算法不仅具有计算复杂度低、开销小、感知性能稳定等诸多优点;而且只需较少的SN就能获得较好的检测性能。This paper proposes a novel wideband compressive spectrum sensing scheme based on the Generalized Likelihood Ratio Test （GLRT）, in which the GLRT statistic and the decision threshold are derived according to Random Matrix Theory （RMT）. The proposed scheme exploits only compressive measurements to detect the occupancy status of each sub-band in a wide spectral range without requiring signal reconstruction or priori information. In addition, to alleviate the communication and data acquisition overhead of Secondary Users （SUs）, a Sensor Node （SN）-assisted cooperative sensing framework is also addressed. In this sensing framework, the sensor nodes perform compressive sampling instead of the SUs at the sub-Nyquist rate. Both theoretical analysis and simulation results show that compared with the traditional GLRT algorithm with signal reconstruction and the Roy＇s Largest Root Test （RLRT） algorithm, the proposed scheme not only has lower computational complexity and cost and more robust sensing performance, but also can achieve better detection performance with a fewer number of SNs.

关 键 词：认知无线电 宽带频谱感知 随机矩阵理论 压缩感知 非重构 

分 类 号：TN92[电子电信—通信与信息系统]