Automatic Modulation Classification Method for Multiple Antenna System Based on Convolutional Neural Network

Abstract

In order to transmit communication signals of different properties, quickly, effectively, and accurately, various different modulation styles can be adopted. Accurate recognition of signal modulation is required at the receive side. Automatic modulation recognition (AMR) is a key technique to identify various styles of modulation of signals received in wireless channels. It can be used in many kinds of communication systems, including single antenna system and multiple antenna system. In this paper, we propose a convolutional neural networks (CNN) aided AMR method for multiple antenna system. Compared with the high order cumulants (HOC) and artificial neural networks (ANN) aided traditional AMR classification method, both with two specific combination strategies, such as relative majority voting method and arithmetic mean method, the proposed AMR with arithmetic mean method has the best classification performance. The experimental results obtained verify that the CNN, one of the representative algorithms of deep learning, has a strong ability to exploit dominant features and classify the modulation styles.

Authors 5

1. Juan Wang Aachen Electrical Engineering and Information Technology

   Nanjing University of Posts and Telecommunications · RWTH Aachen University

   Affiliation as printed

   College of Telecommunications and Information Engineering , NJUPT , Nanjing 210003 , China

   Electrical Engineering and Information Technology , RWTH Aachen University , Aachen 55-52062 , Germany

2. Yu Wang Aachen Electrical Engineering and Information Technology

   Nanjing University of Posts and Telecommunications · RWTH Aachen University

   Affiliation as printed

   College of Telecommunications and Information Engineering , NJUPT , Nanjing 210003 , China

   Electrical Engineering and Information Technology , RWTH Aachen University , Aachen 55-52062 , Germany

3. Wenmei Li
   Affiliation as printed

   School of Geographic and Biologic Information , NJUPT , Nanjing 210023 , China

4. Fumiyuki Adachi

   Tohoku University

   Affiliation as printed

   Research Organization of Electrical Communication , Tohoku University , Sendai 980-8577 , Japan

5. Haris Gacanin Aachen Electrical Engineering and Information Technology

   Nanjing University of Posts and Telecommunications · RWTH Aachen University

   Affiliation as printed

   College of Telecommunications and Information Engineering , NJUPT , Nanjing 210003 , China

   Electrical Engineering and Information Technology , RWTH Aachen University , Aachen 55-52062 , Germany