A highly adaptable probabilistic model for self-diagnosis of GPON-FTTH access network

Serge Romaric Tembo Mouafo 1, 2, 3 Sandrine Vaton 1, 2 Jean-Luc Courant 3 Stephane Gosselin 3
1 ADOPNET - Advanced technologies for operated networks
IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES, Télécom Bretagne, UR1 - Université de Rennes 1
Abstract : Model-based approaches for self-diagnosing of telecommunication networks develop reasonings based on formal and explicit representation of network structure and network behavior. Network behavior modeling is a central issue for these methods. In a recent work, we have proposed a model of architecture and fault propagation of the FTTH (Fiber To The Home) access networks based on GPON (Gigabit capable Passive Optical Network). This model is based on a Bayesian network which encodes expert knowledge about the transport network and the connection network of subscribers. In this paper we extend this model by designing a model of the distribution network which fits to the various engineering techniques of the GPON-FTTH network. We carried out self-diagnosis of an operating GPON-FTTH network based on these two models. The performance of self-diagnostic of the new model is evaluated with respect to the previous model of the GPON-FTTH network.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01424652
Contributor : Bibliothèque Télécom Bretagne <>
Submitted on : Monday, January 2, 2017 - 4:08:42 PM
Last modification on : Tuesday, March 19, 2019 - 4:10:08 PM
Long-term archiving on : Tuesday, April 4, 2017 - 12:46:44 AM

File

SofComm.pdf
Files produced by the author(s)

Identifiers

Citation

Serge Romaric Tembo Mouafo, Sandrine Vaton, Jean-Luc Courant, Stephane Gosselin. A highly adaptable probabilistic model for self-diagnosis of GPON-FTTH access network. SoftCOM 2016 : 24th International Conference on Software, Telecommunications and Computer Networks, Sep 2016, Split, Croatia. pp.1 - 5, ⟨10.1109/SOFTCOM.2016.7772106⟩. ⟨hal-01424652⟩

Share

Metrics

Record views

652

Files downloads

261