Modulation Classification of Underwater Communication with Deep Learning Network.

Journal: Computational intelligence and neuroscience
Published Date:

Abstract

Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.

Authors

  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Zhanliang Sang
    Technical Engineering Department of CRRC Qingdao Sifang Co., Ltd., Qingdao 266111, China.
  • Lingwei Xu
    Department of Information Science Technology, Qingdao University of Science and Technology, Qingdao 266061, China.
  • Conghui Cao
    Department of Electrical Engineering, Ocean University of China, Qingdao 266100, China.
  • T Aaron Gulliver
    Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada V8W 2Y2.