Detail publikace

GPON PLOAMd Message Analysis Using Supervised Neural Networks

TOMAŠOV, A. HOLÍK, M. OUJEZSKÝ, V. HORVÁTH, T. MÜNSTER, P.

Originální název

GPON PLOAMd Message Analysis Using Supervised Neural Networks

Anglický název

GPON PLOAMd Message Analysis Using Supervised Neural Networks

Jazyk

en

Originální abstrakt

This paper discusses the possibility of analyzing the orchestration protocol used in gigabit-capable passive optical networks (GPONs). Considering the fact that a GPON is defined by the International Telecommunication Union Telecommunication sector (ITU-T) as a set of recommendations, implementation across device vendors might exhibit few differences, which complicates analysis of such protocols. Therefore, machine learning techniques are used (e.g., neural networks) to evaluate differences in GPONs among various device vendors. As a result, this paper compares three neural network models based on different types of recurrent cells and discusses their suitability for such analysis.

Anglický abstrakt

This paper discusses the possibility of analyzing the orchestration protocol used in gigabit-capable passive optical networks (GPONs). Considering the fact that a GPON is defined by the International Telecommunication Union Telecommunication sector (ITU-T) as a set of recommendations, implementation across device vendors might exhibit few differences, which complicates analysis of such protocols. Therefore, machine learning techniques are used (e.g., neural networks) to evaluate differences in GPONs among various device vendors. As a result, this paper compares three neural network models based on different types of recurrent cells and discusses their suitability for such analysis.

Plný text v Digitální knihovně

Dokumenty

BibTex


@article{BUT166029,
  author="Adrián {Tomašov} and Martin {Holík} and Václav {Oujezský} and Tomáš {Horváth} and Petr {Münster}",
  title="GPON PLOAMd Message Analysis Using Supervised Neural Networks
",
  annote="This paper discusses the possibility of analyzing the orchestration protocol used in gigabit-capable passive optical networks (GPONs). Considering the fact that a GPON is defined by the International Telecommunication Union Telecommunication sector (ITU-T) as a set of recommendations, implementation across device vendors might exhibit few differences, which complicates analysis of such protocols. Therefore, machine learning techniques are used (e.g., neural networks) to evaluate differences in GPONs among various device vendors. As a result, this paper compares three neural network models based on different types of recurrent cells and discusses their suitability for such analysis.",
  address="MDPI",
  chapter="166029",
  doi="10.3390/app10228139",
  howpublished="online",
  institution="MDPI",
  number="22",
  volume="10",
  year="2020",
  month="november",
  pages="1--12",
  publisher="MDPI",
  type="journal article in Web of Science"
}