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).
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).
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 formulate5G 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