A Q-learning strategy for federation of 5G services

被引：2

作者：

Antevski, Kiril ^{[1
]}

Martin-Perez, Jorge ^{[1
]}

Garcia-Saavedra, Andres ^{[2
]}

Bernardos, Carlos J. ^{[1
]}

Li, Xi ^{[2
]}

Baranda, Jorge ^{[3
]}

Mangues-Bafalluy, Josep ^{[3
]}

Martnez, Ricardo ^{[3
]}

Vettori, Luca ^{[3
]}

机构：

[1] Univ Carlos III Madrid, Madrid, Spain

[2] NEC Labs Europe GmbH, Heidelberg, Germany

[3] Ctr Tecnol Telecomunicac Catalunya CTTC CERCA, Madrid, Spain

来源：

ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) | 2020年

基金：

欧盟地平线“2020”;

关键词：

multi-domain; federation; NFV; algorithms; machine-learning;

D O I：

10.1109/icc40277.2020.9149082

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：6

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