학술논문

Playing With a Multi Armed Bandit to Optimize Resource Allocation in Satellite-Enabled 5G Networks
Document Type
Periodical
Source
IEEE Transactions on Network and Service Management IEEE Trans. Netw. Serv. Manage. Network and Service Management, IEEE Transactions on. 21(1):341-354 Feb, 2024
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Satellites
Task analysis
Handover
Resource management
Games
Optimization
5G mobile communication
AI-based management
5G
satellite handover
multi armed bandit
game theory
Language
ISSN
1932-4537
2373-7379
Abstract
In this paper, we address issues associated with the effective management of handover events in satellite-enabled 5G network infrastructures. Namely, we devise a strategy for dynamically allocating 5G gNB available resources in the presence of a constellation of LEO satellites, based on several parameters collected and dispatched by an ad hoc orchestration platform. We propose to leverage a Combinatorial Multi-Armed Bandit approach to design a resource allocation game that helps make decisions over time under uncertainty conditions related to the incidence of several factors that can determine the quality of experience perceived on the user equipment side. The introduced approach does represent a pioneering one, since it allows us to model a joint optimization task as a competitive game in which agents typically share resources with other agents instead of occupying them exclusively. The designed task allows to dynamically enable and disable channels, taking care of the relationships with the lower layer, transparently managing the required handover operations, and considering possible interference with other channels. We discuss an implementation of the proposed solution in a simulated environment. We also analyze the performance it attains, by measuring both its efficiency and efficacy in a trial setup reproducing a scenario with near real-time temporal requirements. Results show that the proposed approach has a linear trend in terms of running time with respect to the number of user equipments and gNbs involved, while achieving a sub-optimal solution of the handover task in around 20–30 rounds.