Multi-Agent Dynamic Resource Allocation in 6G in-X Subnetworks with Limited Sensing Information

Multi-Agent Dynamic Resource Allocation in 6G in-X Subnetworks with Limited Sensing Information

In this paper, we investigate dynamic resource selection in dense deployments of the recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of the minimum capacity per inXS while minimizing overhead from intra-s...

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Bibliographic Details
Main Authors: Ramoni Adeogun, Gilberto Berardinelli
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Sensors
Subjects:
6G
reinforcement learning
in-X subnetworks
resource allocation
Q-learning
industrial control
Online Access:https://www.mdpi.com/1424-8220/22/13/5062

Internet

https://www.mdpi.com/1424-8220/22/13/5062

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