An Overview on Application of Machine Learning Techniques in Optical Networks

TLDR

Telecommunication networks generate vast heterogeneous data from traffic, alarms, signal quality, and user behavior, and the increasing complexity of optical networks—driven by numerous adjustable parameters and advanced coherent technologies—necessitates advanced analytical tools such as machine learning for automated configuration and fault management. This paper offers an overview of machine‑learning applications in optical communications and networking. The authors classify and survey relevant literature while providing an introductory tutorial on machine learning for researchers and practitioners in the field. Despite a growing body of research, machine‑learning use in optical networks remains nascent, prompting the authors to suggest new research directions to stimulate further work.

Abstract

Todays 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 the paper proposing new possible research directions.