Automatic Modulation Classification (AMC) is an approach to identify an observed signal's most likely modulation scheme without any a priori knowledge of the intercepted signal. In this research, the authors present a new direction for both stages of feature-based (FB) approach. In the feature extraction stage, the authors design a new architecture that 1) removes the bias issue for the estimator of fourth-order cumulants, and 2) extracts polar-transformed information of the received $IQ$ symbols, and finally 3) forms a unique dataset to be used in the labeling stage. Furthermore, the authors contribute to increasing the classification accuracy in low signal-to-noise ratio (SNR) conditions by employing the deep belief network (DBN) platform in addition to the spiking neural network (SNN) platform to overcome execution latency concerns associated with deep learning architectures. For this research, the authors first study each individual FB AMC classifier to derive their respective upper and lower performance bounds and then propose an adaptive framework that is built and developed with these findings. This framework aims to efficiently classify the modulation scheme by intelligently switching between these different FB classifiers to achieve an optimal balance between accuracy and execution latency for any observed channel conditions derived from the main receiver's equalizer. Subsequently, a performance analysis is conducted using the standard RadioML dataset to achieve a realistic evaluation. Numerical results indicate a notably higher classification accuracy, by 16.02% on average, when DBN is employed, whereas SNN requires significantly lower execution latency to label the modulation scheme when compared against two other modified FB classifiers that are built upon convolutional and recurrent neural networks, shown to be reduced by 34.31%.

中文翻译：

---

一种用于自适应自动调制分类的新型深度学习和极坐标变换框架

自动调制分类 (AMC) 是一种无需对截获信号有任何先验知识即可识别观察信号最可能的调制方案的方法。在这项研究中，作者为基于特征 (FB) 方法的两个阶段提出了一个新方向。在特征提取阶段，作者设计了一种新的架构，1）消除四阶累积量估计器的偏差问题，2）提取接收到的$IQ$符号的极坐标变换信息，最后3）形成一个在标记阶段使用的唯一数据集。此外，作者通过使用深度置信网络 (DBN) 平台和尖峰神经网络 (SNN) 平台来克服与深度学习相关的执行延迟问题，有助于提高低信噪比 (SNR) 条件下的分类精度架构。在这项研究中，作者首先研究了每个单独的 FB AMC 分类器，以推导出它们各自的性能上限和下限，然后提出一个基于这些发现构建和开发的自适应框架。该框架旨在通过在这些不同的 FB 分类器之间进行智能切换来有效地对调制方案进行分类，从而在从主接收器均衡器导出的任何观察到的信道条件下实现准确性和执行延迟之间的最佳平衡。随后，使用标准 RadioML 数据集进行性能分析以实现实际评估。数值结果表明，当使用 DBN 时，分类准确度显着提高，平均提高 16.02%，而与其他两个基于卷积和循环神经网络构建的改进 FB 分类器相比，SNN 需要显着更低的执行延迟来标记调制方案，显示减少了 34.31%。