Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

Danshi Wang,Min Zhang

doi:10.3389/frcmn.2021.656786

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.

Highlights

Machine learning (ML) techniques have been developed and applied to optical communication in both the physical layer and network layer for years (Musumeci et al, 2018; Khan et al, 2019)
This paper reports the progress of artificial intelligence (AI) in optical communication from machine learning (ML) to deep learning (DL)
Unlike other review papers about conventional ML algorithms, the presentation focuses on state-of-the-art DL techniques and aims to highlight the contributions of DL to optical communication for both the physical layer and the network layer

Summary

INTRODUCTION

Machine learning (ML) techniques have been developed and applied to optical communication in both the physical layer and network layer for years (Musumeci et al, 2018; Khan et al, 2019). Rapid advances in information technology have made great strides and parallel developments in computation and low-cost computing hardware have made big data modeling possible. Unlike other review papers about conventional ML algorithms, the presentation focuses on state-of-the-art DL techniques and aims to highlight the contributions of DL to optical communication for both the physical layer and the network layer. DRL is considered for various decisionmaking tasks, including routing, resource allocation, and automatic configuration

CONVOLUTIONAL NEURAL NETWORK FOR IMAGE DATA

RECURRENT NEURAL NETWORK FOR SEQUENTIAL DATA

GENERATIVE ADVERSARIAL NETWORK FOR DATA AUGMENTATION

DEEP REINFORCEMENT LEARNING FOR NETWORK AUTOMATION

CONCLUSIONS