Random Matrix Theory (RMT) is a valuable tool for describing the asymptotic behavior of multiple systems,especially for large matrices. In this paper,using asymptotic random matrix theory,a new cooperative Multiple-In...Random Matrix Theory (RMT) is a valuable tool for describing the asymptotic behavior of multiple systems,especially for large matrices. In this paper,using asymptotic random matrix theory,a new cooperative Multiple-Input Multiple-Output (MIMO) scheme for spectrum sensing is proposed,which shows how asymptotic free property of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for Cognitive Radios (CRs). Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance compared with the energy detection techniques even in the case of a small sample of observations.展开更多
针对小采样数据长度下,采样协方差矩阵对统计协方差矩阵估计不准,影响传统最大最小特征值(MME)检测算法检测性能的问题,提出一种基于逼近收缩(OAS)矩阵估计的改进MME检测算法。首先利用OAS估计量对采样数据做协方差矩阵估计,再对估计协...针对小采样数据长度下,采样协方差矩阵对统计协方差矩阵估计不准,影响传统最大最小特征值(MME)检测算法检测性能的问题,提出一种基于逼近收缩(OAS)矩阵估计的改进MME检测算法。首先利用OAS估计量对采样数据做协方差矩阵估计,再对估计协方差矩阵特征值分解,将最大最小特征值之比作为检测统计量,克服了传统MME算法检测门限随采样点大幅波动的缺陷,提高了检测门限的鲁棒性。仿真结果表明,所提算法的检测门限具有鲁棒性,检测性能提高了1 d B^2 d B。展开更多
Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based comp...Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based compressive wideband spectrum sensing(ECWSS) scheme using random matrix theory(RMT) was proposed in this paper.The ECWSS directly utilized the compressive measurements based on compressive sampling(CS) theory to perform wideband spectrum sensing without requiring signal recovery,which could greatly reduce computational complexity and data acquisition burden.In the ECWSS,to alleviate the communication overhead of secondary user(SU),the sensors around SU carried out compressive sampling at the sub-Nyquist rate instead of SU.Furthermore,the exact probability density function of extreme eigenvalues was used to set the threshold.Theoretical analyses and simulation results show that compared with the existing eigenvalue-based sensing schemes,the ECWSS has much lower computational complexity and cost with no significant detection performance degradation.展开更多
基金The National Natural Science Foundation of China(Grant No.61102089)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.11C1058)the New Courses Project of Jishou University(Grant No.2011KCB03)
基金Supported by the National Natural Science Foundation of China (No.60972039)Natural Science Foundation of Jiangsu Province (No.BK2007729)Natural Science Funding of Jiangsu Province (No.06KJA51001)
文摘Random Matrix Theory (RMT) is a valuable tool for describing the asymptotic behavior of multiple systems,especially for large matrices. In this paper,using asymptotic random matrix theory,a new cooperative Multiple-Input Multiple-Output (MIMO) scheme for spectrum sensing is proposed,which shows how asymptotic free property of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for Cognitive Radios (CRs). Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance compared with the energy detection techniques even in the case of a small sample of observations.
文摘针对小采样数据长度下,采样协方差矩阵对统计协方差矩阵估计不准,影响传统最大最小特征值(MME)检测算法检测性能的问题,提出一种基于逼近收缩(OAS)矩阵估计的改进MME检测算法。首先利用OAS估计量对采样数据做协方差矩阵估计,再对估计协方差矩阵特征值分解,将最大最小特征值之比作为检测统计量,克服了传统MME算法检测门限随采样点大幅波动的缺陷,提高了检测门限的鲁棒性。仿真结果表明,所提算法的检测门限具有鲁棒性,检测性能提高了1 d B^2 d B。
基金National Natural Science Foundations of China(Nos.61201161,61271335)Postdoctoral Science Foundation of Jiangsu Province of China(No.1301002B)
文摘Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based compressive wideband spectrum sensing(ECWSS) scheme using random matrix theory(RMT) was proposed in this paper.The ECWSS directly utilized the compressive measurements based on compressive sampling(CS) theory to perform wideband spectrum sensing without requiring signal recovery,which could greatly reduce computational complexity and data acquisition burden.In the ECWSS,to alleviate the communication overhead of secondary user(SU),the sensors around SU carried out compressive sampling at the sub-Nyquist rate instead of SU.Furthermore,the exact probability density function of extreme eigenvalues was used to set the threshold.Theoretical analyses and simulation results show that compared with the existing eigenvalue-based sensing schemes,the ECWSS has much lower computational complexity and cost with no significant detection performance degradation.
