Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

Kaisheng Liao,Jie Gu,Yaping Zhang,Yaodong Zhao,Yi Zhong

doi:10.1109/access.2021.3053427

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.

Highlights

Wireless spectrum monitoring over time, space and frequency is important for effective use of the scarce spectral resources in various commercial areas [1]–[5]
It can be seen that the proposed sequential convolutional recurrent neural network (SCRNN) model delivers a significantly improved accuracy of 92.1% at high signal to noise ratio range (SNR)
The convolutional neural networks (CNNs) and long short-term memory (LSTM) model as baselines are compared to the proposed SCRNN model

Summary

INTRODUCTION

Wireless spectrum monitoring over time, space and frequency is important for effective use of the scarce spectral resources in various commercial areas [1]–[5]. The authors in [19], [24] demonstrated that convolutional neural networks (CNNs) trained on time domain in-phase and quadrature (IQ) data significantly outperform conventional expert feature-based approaches. In autonomous wireless spectrum monitoring systems, online learning is fundamental for accommodating new emerging modulation types and complex environmental circumstances Those RNN models delivering high classification accuracy suffer from computational complexity and long training time. We develop a novel and efficient sequential convolutional recurrent neural network (SCRNN) architecture combining parallel computing capability of CNNs with temporal sensitivity of RNNs. Experimental results demonstrate that our approach outperforms the state-of-the-art on classification performance, while significantly improves the rate of convergence compared with the CNN and RNN alone architectures. Adam optimizer with a learning rate of 0.001 is utilized due to its computational efficiency

IMPLEMENTATION DETAILS

RESULTS AND DISCUSSION

CONCLUSION