Robust automatic modulation classification based on convolutional and recurrent fusion network

Abstract

Automatic modulation classification (AMC) is a critical step to recognize the unknown signal modulation types. It is widely applied in non-cooperative communication systems with diverse modulation types and complex communication environments. In recent years, existing deep learning based AMC methods have been developed to improve the performance of classification in additive white Gaussian noise (AWGN) or Rayleigh channel. However, these methods do not consider the interference problem caused by the Doppler shift in high mobile communication environment. In this paper, we propose a deep learning based robust AMC (RAMC) method to effectively suppress the Doppler shift and achieve classification performance. The proposed convolutional and recurrent fusion network (CRFN) consists of the combination of convolutional neural network (CNN) and simple recurrent unit (SRU) to classify eight types of modulation signal under additive white Gaussian noise (AWGN) and different Doppler shifts. We consider four kinds of fusion networks, i.e., SRU series CNN (SSC), CNN series SRU (CSS), CNN parallel SRU (CPS) and weight average (WA). Simulation results show that the proposed CRFN-CSS method achieves the best performance and brings better performance than either CNN or SRU in 100 Hz Doppler shift. What is more, CRFN-CSS is also robust in the small range of Doppler shifts.