VTC2020Fall-WJ0318Haris.pdf (835.21 kB)
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Automatic Modulation Classification Method for Multiple Antenna System Based on Convolutional Neural Network

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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.


Email Address of Submitting Author

Submitting Author's Institution

Nanjing University of Posts and Telecommunications

Submitting Author's Country

  • China