A deep-Q learning approach to mobile operator collaboration

Next-generation mobile connectivity services include a large number of devices distributed across vast geographical areas. Mobile network operators will need to collaborate to fulfill service requirements at scale. Existing approaches to multi-operator services assume already-established collaborati...

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Vydáno v:Journal of communications and networks Ročník 22; číslo 6; s. 455 - 466
Hlavní autoři: Karapantelakis, Athanasios, Fersman, Elena
Médium: Journal Article
Jazyk:angličtina
Vydáno: Seoul The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.12.2020
한국통신학회
Témata:
agent-based architectures
Collaboration
Customer services
deep reinforcement learning
Geographical distribution
Learning
mobile networks
Quality of service architectures
Telecommunications industry
전자/정보통신공학
ISSN:1229-2370, 1976-5541, 1976-5541
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Shrnutí:Next-generation mobile connectivity services include a large number of devices distributed across vast geographical areas. Mobile network operators will need to collaborate to fulfill service requirements at scale. Existing approaches to multi-operator services assume already-established collaborations to fulfill customer service demand with specific quality of service (QoS). In this paper, we propose an agent-based architecture, where establishment of collaboration for a given connectivity service is done proactively, given predictions about future service demand. We build a simulation environment and evaluate our approach with a number of scenarios and in context of a real-world use case, and compare it with existing collaboration approaches. Results show that by learning how to adapt their collaboration strategy, operators can fulfill a greater part of the service requirements than by providing the service independently, or through pre-established, intangible service level agreements.
Bibliografie:ObjectType-Article-1
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ISSN:1229-2370
1976-5541
1976-5541
DOI:10.23919/JCN.2020.000032