Blind Modulation Recognition in Complex Electromagnetic Environment

DOI

10.2991/assehr.k.200401.059How to use a DOI?

Keywords

automatic modulation recognition, cognitive radio, one-dimensional convolutional neural network, ResNet, DenseNet, deep learning

Abstract

With the continuous development of wireless communication technology, the wireless electromagnetic environment is increasingly complex, which results in the difficulty of modulation recognition of communication signals. In this paper, combining the advantages of ResNet and DenseNet, we propose a blind modulation recognition model based on deep learning. In this model, we reduce the two-dimensional convolution neural network in ResNet into the one-dimensional convolution neural network and then embed it into DenseNet. The identity mapping of ResNet and the dense connection of DenseNet, which strengthen feature propagation and encourage feature reuse and reduce the number of parameters, make the model take full advantage of multi-layer features to improve the ability of feature extraction and reduce the computational complexity. The experimental results on the RadioML2016.10b dataset show that the recognition accuracy of the model can reach 93.7% at high SNR.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhen Liu
AU  - Cuitao Zhu
AU  - Xianfeng Zou
PY  - 2020
DA  - 2020/04/06
TI  - Blind Modulation Recognition in Complex Electromagnetic Environment
BT  - Proceedings of the International Conference on Education, Economics and Information Management (ICEEIM 2019)
PB  - Atlantis Press
SP  - 275
EP  - 279
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200401.059
DO  - 10.2991/assehr.k.200401.059
ID  - Liu2020
ER  -