Automatic Modulation Classification (AMC), as a crucial technique in modern non-cooperative communication networks, plays a key role in various civil and military applications. However, existing AMC methods usually are complicated and can work in batch mode only due to their high computational complexity. This paper introduces a new online AMC scheme based on Isolation Distributional Kernel. Our method stands out in two aspects. Firstly, it is the first proposal to represent baseband signals using a distributional kernel. Secondly, it introduces a pioneering AMC technique that works well in online settings under realistic time-varying channel conditions. Through extensive experiments in online settings, we demonstrate the effectiveness of the proposed classifier. Our results indicate that the proposed approach outperforms existing baseline models, including two state-of-the-art deep learning classifiers. Moreover, it distinguishes itself as the first online classifier for AMC with linear time complexity, which marks a significant efficiency boost for real-time applications.

翻译：自动调制分类（AMC）作为现代非协作通信网络中的关键技术，在各类民用和军用场景中发挥着核心作用。然而，现有AMC方法通常因计算复杂度较高而结构复杂，且仅能工作在批处理模式。本文提出一种基于隔离分布核的新型在线AMC方案。本方法的创新性主要体现在两方面：首次提出使用分布核对基带信号进行表征，并开创性地实现了在真实时变信道条件下高效运行的在线AMC技术。通过在在线场景下的广泛实验，我们验证了所提分类器的有效性。实验结果表明，该方法优于现有基线模型（包括两种前沿深度学习分类器），且作为首个具有线性时间复杂度的在线AMC分类器，标志着实时应用领域的重大效率突破。