CAJ | 학술논문

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。
해문채용수궤구진이론(RMT)직접대압축채양득도적관측수거진행분석，설계출료일충기우엄의사연비검험(GLRT)적비중구관대압축빈보감지신산법。해산법무수임하선험지식취능대관대빈보중적매개자대진행맹검측。차외，위료감경차용호(SU)재수거획취화빈보감지과정중적통신개소，해문제출일충기우전감기절점(SN)보조감지적합작빈보감지가구。이론분석화방진결과균표명，여전통기우신호중구적GLRT감지산법이급Roy최대근검측(RLRT)산법상비，해산법불부구유계산복잡도저、개소소、감지성능은정등제다우점；이차지수교소적SN취능획득교호적검측성능。
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.