A deep-Q learning approach to mobile operator collaboration - Karapantelakis, Athanasios; Fersman, Elena (Journal of communications and networks, 2020)
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.