ICC 2020 - 2020 IEEE International Conference on Communications (ICC) 2020
DOI: 10.1109/icc40277.2020.9149082
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A Q-learning strategy for federation of 5G services

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 th… Show more

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Cited by 9 publications
(12 citation statements)
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“…Within the 5Growth project (and its predecessor 5G-TRANSFORMER), several static and dynamic federation mechanisms have already been proposed, validated and evaluated in [33] This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.…”
Section: ) Communication Service Levelmentioning
confidence: 99%
“…Within the 5Growth project (and its predecessor 5G-TRANSFORMER), several static and dynamic federation mechanisms have already been proposed, validated and evaluated in [33] This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.…”
Section: ) Communication Service Levelmentioning
confidence: 99%
“…As the radius of operation of both selected use cases spans across multiple geographic locations, vertical services extend outside the local domain using any of the aforementioned interactions, to achieve full connectivity that guarantees the performance KPIs and SLAs. Reinforcement Learning, and specifically Q-Learning, can be applied to achieve profit maximization for the vertical service deployment, relying on data such as the available resources in the local and peering providers, and price of a service [12]. The complete 5G solution for both use cases is depicted in Figure 2.…”
Section: B 5growth-based Enhancements To Selected Use Casesmentioning
confidence: 99%
“…In [19], an initial attempt to apply Q-Learning to the service federation problem was presented. In this paper, we go beyond by formulating the problem as an MDP to obtain the optimal solution and we propose a new average-reward learning algorithm that outperforms Q-Learning under all evaluated scenarios and whose performance is not as sensitive to parameter tuning (e.g., discount factor) as Q-learning.…”
Section: Related Workmentioning
confidence: 99%