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

VTC2020Fall-WJ0318Haris.pdf (835.21 kB)

preprint

posted on 2020-04-21, 16:01 authored by Juan Wang, Yu WangYu Wang, Wenmei Li, G G, Fumiyuki Adachi, Haris Gacanin

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.

History

Nanjing University of Posts and Telecommunications