Wideband Compressive Spectrum Sensing Without Reconstruction Based on Random Matrix Theory
Cao Kai-tian①② Gao Xi-qi① Wang Dong-lin③
①(National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China) ②(Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) ③(Department of Electrical and Computer Engineering, New York Institute of Technology (Nanjing Campus), Nanjing 210023, China)
Abstract：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.
曹开田, 高西奇, 王东林. 基于随机矩阵理论的非重构宽带压缩频谱感知方法[J]. 电子与信息学报, 2014, 36(12): 2828-2834.
Cao Kai-Tian, Gao Xi-Qi, Wang Dong-Lin. Wideband Compressive Spectrum Sensing Without Reconstruction Based on Random Matrix Theory. , 2014, 36(12): 2828-2834.
2014, Vol. 36 
