Blind Channel Identification Aided Generalized Automatic Modulation Recognition Based on Deep Learning

Abstract

Automatic modulation recognition (AMR) plays an important role in cognitive radio (CR), which relies on AMR responding to changes in the surrounding environment and then adjust strategies simultaneously. Deep learning based reliable AMR method have been developed in recent years. However, all of their AMR training models are considered in a specialized channel rather than generalized channel. Hence, these AMR methods are hard to be applied in general scenarios. In this paper, we propose a blind channel identification (BCI) aided generalized AMR (GenAMR) method based on deep learning which is conducted by two independent convolutional neural networks (CNNs). The first CNN is trained on in-phase and quadrature (IQ) sampling signals, which is utilized to distinguish channel categories like BCI behaviors. The second CNN is trained by line of sight (LOS) model and non-line of sight (NLOS) model, respectively. Simulation results confirm that our proposed generalized AMR method is significantly better than conventional method.

Keywords

• Automatic modulation recognition (AMR)
• deep learning
• convolutional neural network (CNN)
• in-phase and quadrature (IQ) samples
• blind channel identification