Machine Learning-Aided Optical Performance Monitoring Techniques: A Review
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers in Communications and Networks
Abstract
Future communication systems are faced with increased demand for high capacity,
dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements,
networks have become more complex and thus require new design methods and
monitoring techniques, as they evolve towards becoming autonomous. Machine
learning has come to the forefront in recent years as a promising technology to aid in
this evolution. Optical fiber communications can already provide the high capacity required
for most applications, however, there is a need for increased scalability and adaptability to
changing user demands and link conditions. Accurate performance monitoring is an
integral part of this transformation. In this paper, we review optical performance monitoring
techniques where machine learning algorithms have been applied. Moreover, since many
performance monitoring approaches in the optical domain depend on knowledge of the
signal type, we also review work for modulation format recognition and bitrate
identification. We additionally briefly introduce a neuromorphic approach as an
emerging technique that has only recently been applied to this domain.
Description
Keywords
Machine learning, Optical performance monitoring, Reservoir computing, Modulation format recognition, Bitrate identification
Citation
Tizikara, D. K., Serugunda, J., & Katumba, A. (2022). Machine learning-aided optical performance monitoring techniques: A review. Frontiers in Communications and Networks, 63. doi: 10.3389/frcmn.2021.756513