2022
DOI: 10.36227/techrxiv.19550920.v1
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Intelligent Multi-Agent Resource Allocation in 6G in-X Subnetworks with Limited Sensing Information

Abstract: In this letter, we investigate dynamic resource selection in dense deployments of a recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of per inXS sum capacities. Since inXSs are expected to be autonomous, selection decisions are made by each inXS based on its local information without signalling from other inXSs. A multi-agent Q-learning (MAQL) method based on limited sensing information (SI) is then developed resulti… Show more

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