中文摘要：

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（GLRTl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。

英文摘要：

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.