Alpha稳定分布噪声下基于特征值之差频谱感知算法

Eigenvalue difference spectrum sensing algorithm under Alpha stable distributed noise

针对基于特征值的谱感知算法在脉冲噪声的环境下感知性能不佳的问题,分析矩阵全部的特征值,引入矩阵特征值的几何均值,提出了基于分数低阶协方差矩阵的最大特征值与特征值几何均值之差(difference between maximum eigenvalue and geometric mean of eigenvalue,DMGM)的频谱感知算法。选择了Alpha稳定分布噪声模拟脉冲噪声环境,理论分析与仿真实验结果表明,在不增加算法复杂度的前提下,DMGM算法与其他算法相比,更适用于脉冲噪声环境,在低信噪比条件下具有更好的感知性能。

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.