Publication:
A Q-learning strategy for federation of 5G services

Thumbnail Image
Identifiers
URI: https://hdl.handle.net/10016/31319
ISBN: 978-1-7281-5089-5
DOI: https://doi.org/10.1109/ICC40277.2020.9149082
UXXI: CC/0000031100
Files
learning_ICC_2020_ps.pdf (330.68 KB)
Publication date
2020-07-27
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Impact
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
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 the decision problem that directly impacts the revenue of 5G administrative domains, and we propose as solution a Q-learning algorithm. The simulation results show near optimum profit maximization and a well-trained Q-learning algorithm can outperform the intuitive "greedy" approach in a realistic scenario.
Description
This paper has been presented at the 2020 IEEE International Conference on Communications
Keywords
Federation, Algorithms, Machine-Learning, Multi-domain, NFV
Bibliographic 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).
Collections
DIT - RYSC - Comunicaciones en Conferencias Internacionales / International Conferences