Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach
It addresses the inefficiency of centralized resource allocation for URLLC in IIoT, offering a solution for industrial applications requiring reliable, low-latency communication.
This paper tackles the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in 6G Industrial Internet of Things (IIoT) networks by proposing a distributed, user-centric scheme using Multi-Armed Bandit (MAB) for autonomous uplink resource allocation, demonstrating its effectiveness through simulation for both periodic and aperiodic traffic in highly populated networks.
This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-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 be disseminated to the User Equipments (UEs) before uplink transmissions, which is not efficient for URLLC, especially in case of flexible/unpredictable traffic. To alleviate this burden, we study a distributed, user-centric scheme based on machine learning in which UEs autonomously select their uplink radio resources without the need to wait for scheduling grants or preconfiguration of connections. Using simulation, we demonstrate that a Multi-Armed Bandit (MAB) approach represents a desirable solution to allocate resources with URLLC in mind in an IIoT environment, in case of both periodic and aperiodic traffic, even considering highly populated networks and aggressive traffic.