A Q-learning strategy for federation of 5G services
Kiril Antevski, Jorge Martín‐Pérez, Andrés García‐Saavedra, Carlos J. Bernardos, Xi Li, Jorge Baranda, Josep Mangues‐Bafalluy, R. Martnez, Luca Vettori · 2020
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.