Title: Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification Show Chinese title

Title: 基于顺序卷积递归神经网络的快速自动调制分类

Abstract: A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design of hand-crafted expert features. With the intuition of convolutional layers with pooling serving as the role of front-end feature distillation and dimensionality reduction, sequential convolutional recurrent neural networks are developed to take complementary advantage of parallel computing capability of convolutional neural networks and temporal sensitivity of recurrent neural networks. Experimental results demonstrate that the proposed architecture delivers overall superior performance in signal to noise ratio range above -10~dB, and achieves significantly improved classification accuracy from 80\% to 92.1\% at high signal to noise ratio range, while drastically reduces the average training and prediction time by approximately 74% and 67%, respectively. Response patterns learned by the proposed architecture are visualized to better understand the physics of the model. Furthermore, a comparative study is performed to investigate the impacts of various sequential convolutional recurrent neural network structure settings on classification performance. A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification. Show Chinese abstract

Abstract: 提出了一种新颖且高效的端到端学习模型，用于无线频谱监测应用中的自动调制分类，该模型能够从时间域的同相和正交数据中自动学习，而无需设计手工制作的专业特征。 通过卷积层与池化层作为前端特征蒸馏和降维角色的直觉，开发了顺序卷积循环神经网络，以充分利用卷积神经网络的并行计算能力和循环神经网络的时间敏感性。 实验结果表明，所提出的架构在信噪比范围高于-10~dB时表现出总体优越的性能，并在高信噪比范围内将分类准确率从80%显著提高到92.1%，同时分别将平均训练时间和预测时间减少约74%和67%。 可视化了所提出的架构学习到的响应模式，以便更好地理解模型的物理特性。 此外，进行了对比研究，以调查各种顺序卷积循环神经网络结构设置对分类性能的影响。 开发了一个具有两层卷积神经网络和后续两层长短期记忆神经网络的代表性顺序卷积循环神经网络架构，以建议快速自动调制分类的选项。