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

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.

Joint modulation format recognition and optical performance monitoring for efficient fiber-optic communication links using ensemble deep transfer learning

“…Successfully, ML implementations are also performed to transform future intelligent optical networks. 8,10 Results from a study [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] prove the advantages of using MLbased methods that can be used for futuristic predictions. This paradigm shift results from the various possible advantages provided by ML.…”

“…2 Machine learning methods employed in OPM. [8][9][10][11][12][13][14][15][16][17] DNN-based OPM to estimate the performance parameters simultaneously rather than using individual networks. Tanimura et al 22 proposed a CNN-based OSNR estimator in coherently received optical networks and 21 estimated OSNR using an inception module and finite impulse response filter combination and proposing the OptInception CNN method.…”

A review of machine learning-based failure management in optical networks

“…Driven by the growth of data volumes and improvement of computing power, ML has successfully evolved into DL to handle more complex and large-scale problems with robust, adaptable, and powerful solutions [17]. As the subset of ML, DL can be generally understood as deep neural networks (DNN) with multiple nonlinear layers.…”

Aligning an optical interferometer with beam divergence control and continuous action space

“…For example, Sun et al [20] used an RL agent to stabilize a mode-locked laser by controlling waveplates and polarizers. RL algorithms are also routinely used to optimize optical communications [21], e.g. to route traffic in optical transport networks [22,23].…”