Ahead of Print article · Journal article
An Overview on Application of Machine Learning Techniques in Optical Networks
Polytechnic University of Milan1
Università della Svizzera italiana2
University College Dublin3
Trinity College Dublin4
Centre of Excellence for Silicon Photonics for Optical Communications, Centers, Technical University of Denmark5
Department of Photonics Engineering, Technical University of Denmark6
Ultra-fast Optical Communication, Department of Photonics Engineering, Technical University of Denmark7
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2019 |
Pages: | 1383-1408 |
ISSN: | 2373745x and 1553877x |
Types: | Ahead of Print article and Journal article |
DOI: | 10.1109/COMST.2018.2880039 |
ORCIDs: | Zibar, Darko |
Machine learning Network monitoring. Neural networks Nonlinear optics Optical Signal-to-Noise Ratio Optical communications and networking Optical fiber networks Optical modulation
Adaptive optics Artificial neural networks bit error rate data analytics heterogeneous data learning (artificial intelligence) machine learning techniques network monitoring network traffic network-data analysis neural networks optical communication networks optical communications and networking optical fibre networks optical networks optical signal-to-noise ratio telecommunication computing telecommunication network management telecommunication networks telecommunication traffic