Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

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dc.contributor.authorTizikara, Dativa K.
dc.contributor.authorSerugunda, Jonathan
dc.contributor.authorKatumba, Andrew
dc.date.accessioned2022-11-30T20:13:09Z
dc.date.available2022-11-30T20:13:09Z
dc.date.issued2022
dc.description.abstractFuture 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.en_US
dc.identifier.citationTizikara, 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.2021.756513en_US
dc.identifier.other10.3389/frcmn.2021.756513
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/5581
dc.language.isoenen_US
dc.publisherFrontiers in Communications and Networksen_US
dc.subjectMachine learningen_US
dc.subjectOptical performance monitoringen_US
dc.subjectReservoir computingen_US
dc.subjectModulation format recognitionen_US
dc.subjectBitrate identificationen_US
dc.titleMachine Learning-Aided Optical Performance Monitoring Techniques: A Reviewen_US
dc.typeArticleen_US
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