The electromagnetic environments have become more complex with the development of wireless communication technology. Signal modulation classification has attracted extensive attention due to its application in electronic countermeasures and physical layer security threat prevention under complex electromagnetic environments. Excellent classification performance requirements challenge the adaptability of the method and the ability to extract modulation characteristics. This paper proposes an automated machine learning framework, AutoSMC, for signal modulation classification. An adaptive signal augmentation method is proposed to adapt to the network changes during the search process. In order to extract the modulation features effectively, an scalable convolutional random fourier feature block is proposed. Moreover, the initial search space of the framework is given. The Bayesian Optimization is used to drive hyperparameter optimization to achieve AutoSMC and obtain the optimal method state. Great experiments were carried out on RADIOML 2016.10A and RADIOML 2016.10B. Experimental evaluations on these datasets show that our approach AutoSMC achieves state-of-the-art results compared to the most relevant signal modulation classification methods.

中文翻译：

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AutoSMC：用于信号调制分类的自动化机器学习框架


随着无线通信技术的发展，电磁环境变得更加复杂。信号调制分类因其在复杂电磁环境下的电子对抗和物理层安全威胁防范中的应用而受到广泛关注。优异的分类性能要求对方法的适应性和提取调制特征的能力提出了挑战。本文提出了一种用于信号调制分类的自动化机器学习框架 AutoSMC。提出了一种自适应信号增强方法来适应搜索过程中的网络变化。为了有效地提取调制特征，提出了可扩展的卷积随机傅立叶特征块。此外，给出了框架的初始搜索空间。贝叶斯优化用于驱动超参数优化以实现AutoSMC并获得最佳方法状态。在RADIOML 2016.10A和RADIOML 2016.10B上进行了很好的实验。对这些数据集的实验评估表明，与最相关的信号调制分类方法相比，我们的方法 AutoSMC 实现了最先进的结果。