非高斯噪声中基于分数低阶矩协方差MME检测的频谱感知算法

Spectrum Sensing Under Non-Gaussian Noise Using Fractional Lower Order Moments Covariance MME Detection

针对传统的最大最小特征值之比的频谱感知算法(MME)在非高斯噪声频谱感知性能下降乃至失效的问题,提出了一种基于分数低阶矩采样协方差的改进MME算法。该算法先用分数低阶矩对观测数据进行预处理,获得分数低阶矩协方差矩阵,再求矩阵的最大最小特征值之比作为统计量。本文采用了Alpha分布和Laplace分布拟合非高斯噪声环境,蒙特卡罗(Monte Carlo)仿真结果表明,非高斯噪声中基于分数低阶矩的协方差MME频谱感知算法的检测性能明显优于MME。

In view of the problem that the performance of the traditional spectrum sensing algorithm of the of the maximum minimum eigenvalues （MME） （noise environment is assumed to be a Ganssian distribution） may degrade severely due to the heavy tail characteristics of the probability density function （PDF） of the non-Gaussian noise in the actual environment. To this end, an improved the fractional lower order moment sampling eovariance MME that does not require any a priori knowledge about the signal, channels and noise is presented in this paper. The algorithm use fractional lower order moment of observation data preprocessing, scoring low moments of covariance matrix, and maximum ratio of the minimum eigenval- ue of matrix as a statistic. This paper adopted the Alpha and Laplace distribution fitting non-Gaussian noise environment, Monte Carlo simulation results show that the performance of the fractional lower order moment of covariance MME is superi- or to MME in non-Ganssian situation.