Spectrum Sensing Using Non-asymptotic Behavior of Eigenvalues
The classical random matrix theory is mainly focused on asymptotic spectral properties of random matrices when their dimensions tend to infinity. At the same time, many recent applications, like convex geometry, functional analysis and information theory, operate with random matrices of fixed dimensions. In this paper, we investigate a recently developed nonasymptotic behavior of eigenvalues of random matrices, which is about spectral properties of random sub-Gaussian matrices of fixed dimensions. Then, a new spectrum sensing scheme for cognitive radio is proposed by using the non-asymptotic behavior of eigenvalues. Simulation results show that the proposed scheme has a better detection performance than the classical energy detection technique and the scheme based on asymptotic behavior of eigenvalues of random matrices, even in the case of a small sample of observations.
Spectrum Sensing Random Matrix Nonasymptotic Eigenvalues
Lei Wang Baoyu Zheng Jingwu Cui Wenjing Yue
Institute of Signal Processing and Transmission, Nanjing University of Posts & Telecommunications, N Institute of Signal Processing and Transmission, Nanjing University of Posts & Telecommunications, N
国际会议
南京
英文
1-5
2011-11-09（万方平台首次上网日期，不代表论文的发表时间）