A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> A. Related works <s> Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> A. Related works <s> Today’s telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users’ behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions. <s> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> A. Related works <s> © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). <s> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> A. Related works <s> Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues. <s> BIB004
Recently, several tutorials released in ML for OC: BIB001 , with a network layer view, provided a comprehensive survey on investigations in AI in OC networks. It first reviewed applications related to physical layer, including optical performance monitoring, nonlinearity mitigation, and estimating quality of transmission (QoT). Then reviewed applications such as optical network planning and operation in both transport and access networks. BIB002 , again with a network layer view, reviewed the application of ML for OC and networking.  , again took a network view, and reviewed works on ML for OC network and discussed algorithm choices, as well as data and model management strategies. BIB003 , again with a physical layer view reviewed ML investigations on CO-OFDM and focused on practical aspects and comparison with the DSP techniques. Also a comparison is done over computational complexity of ML and DSP. BIB004 described the mathematical formulations of ML algorithms from communication theory view; further it reviewed some of investigations on ML for OC with a physical layer view. Although there are published several surveys on ML for OC, however, none of them had ML view, and number of reviewed works in them were limited. Considering that ML for OC investigations require knowledge and awareness in both ML and OC aspects, and the fact that there is lack of investigation that clarify the ML techniques used in ML for OC, this paper aims to fill this gap, and proposed a comprehensive investigation over a lot of released works in this field. The rest of this paper is organized as follows: sections II, III, and IV are devoted to supervised learning, unsupervised learning, and reinforcement learning. There is a lack of semi-supervised learning research in this area. Section V is the conclusion of this work.