Alpha稳定分布噪声下基于特征值之差频谱感知算法

doi:10.12305/j.issn.1001-506X.2023.09.35

Abstract

Abstract:

Aiming at the problem that the spectral sensing algorithm based on eigenvalue has poor sensing performance in the environment of impulse noise. All eigenvalues of the matrix are analyzed and the geometric mean of the eigenvalues of the matrix is introduced. A spectrum sensing algorithm based on the difference between maximum eigenvalue and geometric mean of eigenvalue (DMGM) of the fractional low-order covariance matrix is proposed. Alpha stable distribution noise is selected to simulate the impulse noise environment. Theoretical analysis and simulation results show that DMGM has better perceptual performance than other algorithms in low signal to noise ratio environments, and has better perceptual performance under low signal to noise ratio conditions.

Key words: spectrum sensing, Alpha stable distributed, fractional low order moments, sampling covariance, geometric mean

CLC Number:

TN92

Zengmao CHEN, Kailin WANG, Zhiguo SUN, Rongchen SUN, Sileng AER. Eigenvalue difference spectrum sensing algorithm under Alpha stable distributed noise[J]. Systems Engineering and Electronics, 2023, 45(9): 2949-2955.

Figures/Tables 8

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Eigenvalue difference spectrum sensing algorithm under Alpha stable distributed noise

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