AUTHOR=Wang Danshi , Zhang Min TITLE=Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning JOURNAL=Frontiers in Communications and Networks VOLUME=2 YEAR=2021 URL=https://www.frontiersin.org/journals/communications-and-networks/articles/10.3389/frcmn.2021.656786 DOI=10.3389/frcmn.2021.656786 ISSN=2673-530X ABSTRACT=

Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.