Automatic Modulation Recognition Based on Deep Learning

Abstract

Automatic modulation recognition (AMR) is critical to realize efficient spectrum sensing,spectrum management and spectrum utilization in non-cooperative communication scenarios.It is also an important prerequisite for efficient signal proces-sing.Traditional AMR methods based on pattern recognition need to extract features manually,which faces many problems such as high design complexity,low recognition accuracy and weak generalization ability.Therefore,practitioners turn to deep learning (DL) methods,which are good at extracting hidden features from the data,and propose a number of AMR-oriented deep neural network (ADNN) architectures.Compared with traditional methods,ADNN has achieved higher recognition accuracy,higher generalization ability and wider application range.This paper provides a comprehensive survey of ADNN to help practitioners understand the current research status in this field,and analyzes the future directions after pinpointing several open issues.Firstly,typical deep learning methods involved in ADNN design are introduced.Secondly,a few traditional AMR methods are briefly described.Thirdly,typical ADNNs are introduced in detail.Finally,a series of experiments are conducted on an open dataset to compare typical proposals,and several key research directions in this field are put forward.

Keywords

• deep learning|deep neural network|automatic modulation recognition|security