Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

TL;DR

This paper introduces a novel end-to-end deep learning model combining convolutional and recurrent neural networks for rapid and accurate automatic modulation classification in wireless spectrum monitoring, outperforming existing methods especially at high SNRs.

Contribution

The paper presents a new sequential convolutional recurrent neural network architecture that improves classification accuracy and reduces training and prediction times for automatic modulation classification.

Findings

Achieves 92.1% accuracy at high SNRs, up from 80%.

Reduces training time by approximately 74%.

Reduces prediction time by approximately 67%.

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.