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

TL;DR

This paper proposes a distributed, machine learning-based resource allocation scheme for URLLC in IIoT networks, using Multi-Armed Bandit algorithms to enable autonomous uplink resource selection without centralized scheduling, improving efficiency and reliability.

Contribution

It introduces a novel distributed resource allocation method using Multi-Armed Bandit algorithms for URLLC in IIoT, reducing reliance on centralized scheduling and preconfiguration.

Findings

MAB approach effectively allocates resources for URLLC in IIoT.

Method performs well with both periodic and aperiodic traffic.

Suitable for highly populated networks with aggressive traffic.

Abstract

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.