NOMA系统下基于高阶累积量的叠加信号调制识别

Modulation Classification of Superimposed Signal in NOMA Systems Based onHigh-order Cumulants

为解决非正交多址系统中的信号调制识别问题,利用高阶累积量的特性,提出了一种基于高阶累积量的调制识别算法,主要包含特征提取、概率密度函数构建和信号分类三个模块。仿真结果表明,与已有算法相比,所提出的基于高阶累积量的调制识别算法,在中低信噪比下可有效改善系统的识别性能。最后,利用浮点数计算了所提出算法的复杂度,结果表明该算法相较传统的最大似然检测算法复杂度大大降低,证明了该算法的有效性。

In order to solve the problem of signal modulation classification in non-orthogonal multiple access systems,a modulation classification algorithm based on high-order cumulants is proposed by using the characteristicsof high-order cumulants, which mainly includes feature extraction, probability density function constructionand signal classification. Simulation results show that, compared with the existing algorithm, the proposedmodulation classification algorithm based on high-order cumulants can effectively improve the system recognitionperformance under medium to low SNR. Finally, the complexity of the algorithm proposed is calculated usingfloating-point numbers. The results show that the complexity of the proposed algorithm is significantly reducedcompared to traditional maximum likelihood detection algorithms, proving the effectiveness of the algorithm.