Meta-ensemble learning for OPM in FMF systems

Optical performance monitoring (OPM) is crucial for facilitating the management of future few-mode fiber (FMF)-based transmissions. OPM deploys fault detection and link diagnosis by measuring the physical layer states and provides feedback to the controller. Recently, machine learning (ML) has gaine...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied optics (2004) 2022-07, Vol.61 (21), p.6249-6256
Hauptverfasser: Amirabadi, M. A., Nezamalhosseini, S. A., Kahaei, M. H.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Optical performance monitoring (OPM) is crucial for facilitating the management of future few-mode fiber (FMF)-based transmissions. OPM deploys fault detection and link diagnosis by measuring the physical layer states and provides feedback to the controller. Recently, machine learning (ML) has gained a lot of attention for OPM, and various ML algorithms were developed, wherein the selection of the proper method is a challenge. Ensemble learning (EL) solves this challenge by combining different ML models; however, this simultaneous employment suffers from increased complexity and dependency on the performance of each individual model. Meta-ensemble learning (MEL) provides a promising solution by intelligently selecting the proper ensemble at each instance. In this work, we employ MEL for OPM in FMF systems. We compare the proposed MEL-based OPM method with naive EL (NEL), which is a well-known EL method. The obtained results indicate that proposed MEL-based OPM method provides better performance with the loss data set size compared with NEL-based OPM. Furthermore, the proposed MEL-based OPM method does not need the feature preprocessing, which is an essential step in other ML algorithms such as NEL-based OPM.
ISSN:1559-128X
2155-3165
1539-4522
DOI:10.1364/AO.461473