A Q-learning strategy for federation of 5G services

Antevski, Kiril; Martín Pérez, Jorge; Garcia Saavedra, Andres; Bernardos Cano, Carlos Jesús; Li, Xi; Baranda, Jorge; Mangues-Bafalluy, Josep; Martínez, Ricardo; Vettori, Luca

e-Archivo Home
→
Investigación
→
Departamentos
→
Departamento de Ingeniería Telemática
→
Grupo de Redes y Servicios de Comunicaciones (RYSC)
→
DIT - RYSC - Comunicaciones en Conferencias Internacionales / International Conferences
→
View Item

A Q-learning strategy for federation of 5G services

Author(s): Antevski, Kiril; Martín Pérez, Jorge; Garcia Saavedra, Andres; Bernardos Cano, Carlos Jesús; Li, Xi; Baranda, Jorge; Mangues-Bafalluy, Josep; Martínez, Ricardo; Vettori, Luca

Publisher: IEEE

Issued date: 2020-07-27

Citation: Antevski, K., Martín Pérez, J., García Saavedra, ... Vettori, L. (2020). A Q-learning strategy for federation of 5G services. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

ISBN: 978-1-7281-5089-5

DOI: https://doi.org/10.1109/ICC40277.2020.9149082

xmlui.dri2xhtml.METS-1.0.item-contributor-funder: European Commission

Sponsor: This work has been partially funded by the EC H2020 5G-TRANSFORMER Project (grant no. 761536) and the EU H2020 5GROWTH Project (grant no. 856709).

Project: info:eu-repo/grantAgreement/EC/H2020/761536
info:eu-repo/grantAgreement/EC/H2020/856709

Keywords: Federation , Algorithms , Machine-Learning , Multi-domain , NFV

URI: http://hdl.handle.net/10016/31319

Abstract:

5G networks aim to provide orchestration of services across multiple administrative domains through the concept of federation. In this paper, we are exploring the federation feature of a platform for 5G transport network of vertical services. Then we formulate [+]