Deep Learning Aided Method for Automatic Modulation Recognition

TL;DR: This paper proposes an effective AMR algorithm based on deep learning (DL) with capabilities of automatically extracting representative and effective features and shows that DL-AMR is much better than traditional algorithms under two fading channels.

Abstract: Automatic modulation recognition (AMR) is considered one of most important techniques in the non-cooperative wireless communication systems. Traditional algorithms, e.g., support vector machine (SVM) based on high order cumulants (HOC), are hard to achieve the reliable performance. In this paper, we propose an effective AMR algorithm based on deep learning (DL) with capabilities of automatically extracting representative and effective features. Our proposed method resorts to in-phase and quadrature (IQ) samples which are IQ components of received baseband signal, respectively. We adopt convolutional neural networks (CNN) and recurrent neural networks (RNN) to classify six types of signal modulations over additive white Gaussian noise (AWGN) channel and Rayleigh fading channel, respectively. Simulation results show that DL-AMR is much better than traditional algorithms under two fading channels.