Department of Electrical Engineering, Ocean University of China, Qingdao 266100, China.
School of Physics and Electronic Engineering, Taishan University, No. 525 Dongyue Street, Tai'an City, China.
Comput Intell Neurosci. 2019 Apr 1;2019:8039632. doi: 10.1155/2019/8039632. eCollection 2019.
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.
自动调制识别已经成功地使用了各种机器学习方法，并取得了一定的成果。作为机器学习的一个分支，深度学习近年来取得了巨大的进展，并在图像和语言处理领域取得了显著的成果。深度学习需要大量的数据支持。作为一个拥有大量数据的通信领域，应用深度学习具有内在的优势。然而，深度学习在通信领域的广泛应用尚未得到充分发展，特别是在水声通信领域。本文主要讨论了通信过程中的一个重要部分——调制识别过程，该过程采用了深度学习方法。与需要特征提取的常见机器学习方法不同，深度学习方法不需要特征提取，并取得了比常见机器学习方法更好的效果。
