Modulation Classification of Underwater Communication with Deep Learning Network. (1st April 2019)

• Record Type: Journal Article
• Authors: Wang, Yan; Zhang, Hao; Sang, Zhanliang; Xu, Lingwei; Cao, Conghui; Gulliver, T. Aaron
• Other Names: Lawrynczuk Maciej Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2019(2019)
• Publication Date: 2019-04-01
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2019/8039632
• Languages: English
• ISSNs: 1687-5265

Abstract

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.