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This paper addresses the problem of enabling inter-machine Ultra-ReliableLow-Latency Communication (URLLC) in future 6G Industrial Internet of Things(IIoT) networks. As far as the Radio Access Network (RAN) is concerned,centralized pre-configured resource allocation requires scheduling grants to bedisseminated to the User Equipments (UEs) before uplink transmissions, which isnot efficient for URLLC, especially in case of flexible/unpredictable traffic.To alleviate this burden, we study a distributed, user-centric scheme based onmachine learning in which UEs autonomously select their uplink radio resourceswithout the need to wait for scheduling grants or preconfiguration ofconnections. Using simulation, we demonstrate that a Multi-Armed Bandit (MAB)approach represents a desirable solution to allocate resources with URLLC inmind in an IIoT environment, in case of both periodic and aperiodic traffic,even considering highly populated networks and aggressive traffic.

Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach