• Yan Wang, Hao Zhang, Zhanliang Sang, Lingwei Xu, Conghui Cao, and T Aaron Gulliver.
• Department of Electrical Engineering, Ocean University of China, Qingdao 266100, China.
• Comput Intell Neurosci. 2019 Jan 1; 2019: 8039632.

AbstractAutomatic 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.

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