This paper introduces a novel automatic modulation classification (AMC) method using the graph-based fractional lower-order cyclic-spectrum analysis in the $\alpha$-stable noise environment. The noise in the practical communication scenario usually exhibits impulse characteristics in the statistical sense, which could be modeled as the $\alpha$-stable distribution. This would make the second- or higher-order statistics of the received signal vanish, and thus the performances of the conventional AMC algorithms designed for Gaussian noise significantly deteriorate when directly employed in the $\alpha$-stable noise. In our proposed framework, the fractional lower-order cyclic spectrum (FLOCS) analysis is first invoked to acquire the polyspectra of the signal corrupted by the $\alpha$-stable noise. Then, the graph-based AMC mechanism is systematically established upon the graph representation of the FLOCS to identify the modulation type according to the discrepancies between the graph features derived from the training and test data. The performance of our proposed new algorithm is theoretically analyzed, and the correct classification probability $P_{\text{cc}}$ over the modulation candidate set is formulated analytically. The remarkable scalability and efficiency of our proposed approach for the modulation candidate set variation are also theoretically investigated in detail. Monte Carlo simulation results demonstrate the effectiveness and superiority of the proposed AMC scheme.

中文翻译：

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使用基于图的分数低阶循环谱分析对 α 稳定噪声进行鲁棒调制分类

本文介绍了一种新的自动调制分类 (AMC) 方法，该方法使用基于图的分数低阶循环谱分析在 $\alpha$- 稳定的噪音环境。实际通信场景中的噪声通常表现出统计意义上的脉冲特征，可以建模为$\alpha$- 稳定的分布。这将使接收信号的二阶或更高阶统计量消失，因此当直接用于高斯噪声时，为高斯噪声设计的传统 AMC 算法的性能显着恶化。$\alpha$- 稳定的噪音。在我们提出的框架中，首先调用分数低阶循环谱 (FLOCS) 分析来获取被破坏的信号的多谱。$\alpha$- 稳定的噪音。然后，在 FLOCS 的图表示上系统地建立基于图的 AMC 机制，以根据从训练和测试数据得出的图特征之间的差异来识别调制类型。从理论上分析了我们提出的新算法的性能，正确分类概率$P_{\text{cc}}$对调制候选集进行分析制定。我们提出的调制候选集变化方法的显着可扩展性和效率也在理论上进行了详细研究。Monte Carlo 仿真结果证明了所提出的 AMC 方案的有效性和优越性。