Automatic modulation classification in the manner of pattern recognition has caught massive attention due to its simplicity in workflow design. Recently, enormous interest has been aroused because of the boosting development of deep learning and numerous network structures, leading to various substantial contributions. However, to the best of our knowledge, current approaches fail to handle two essential concerns simultaneously: the statistical distribution of the noise and the computational complexity due to the network structure. Since these factors play critical roles when facing practical applications, a modified constellation concept based on the Score function of Cauchy distribution is proposed as a robust feature to impulsive noise. Besides, a lightweight structure based on Shuffle Unit and Gated Recurrent Unit is proposed as the recognizer to lower the potential risk of overfitting and cope with the time-consuming problem. Monte-Carlo experiments involving multiple comparison algorithms are executed under different conditions, and results verify the superior performance of the proposed AMC scheme.

中文翻译：

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脉冲噪声下基于柯西分数星座和轻量级网络的自动调制分类


以模式识别方式进行的自动调制分类由于其工作流程设计的简单性而引起了广泛的关注。近年来，由于深度学习和众多网络结构的快速发展，引起了人们的极大兴趣，并产生了各种实质性贡献。然而，据我们所知，当前的方法无法同时处理两个基本问题：噪声的统计分布和网络结构导致的计算复杂性。由于这些因素在实际应用中起着至关重要的作用，因此提出了一种基于柯西分布得分函数的修改星座概念，作为对脉冲噪声的鲁棒特征。此外，提出了一种基于洗牌单元和门控循环单元的轻量级结构作为识别器，以降低过拟合的潜在风险并应对耗时问题。在不同条件下进行了涉及多种比较算法的蒙特卡罗实验，结果验证了所提出的AMC方案的优越性能。