mlsanityData to 7 Oct 2026

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

arXiv 2211.12201 ↗First posted 22 Nov 2022cs.NI cs.LG eess.SPcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

12 of 25 references.

References · 12

  1. 2019Introduction to Multi-Armed Banditscited by 475
  2. 2013Learning to Optimize Via Posterior Samplingcited by 253
  3. 2019A Survey of Deep Learning Applications to Autonomous Vehicle Controlcited by 101
  4. 2017Machine Learning for Networking: Workflow, Advances and Opportunitiescited by 20
  5. 2019Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challengescited by 18
  6. 2018Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settingscited by 16
  7. 2019Towards 6G Networks: Use Cases and Technologiescited by 13
  8. 2018Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approachcited by 10
  9. 2021On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSIcited by 9
  10. 2020Analyzing Grant-Free Access for URLLC Servicecited by 4
  11. 2021Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systemscited by 3
  12. 2020Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT: A Generative Adversarial Network Approachcited by 1

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Cited by year

Citing papers per year: 2023: 3.

3 papers.

  1. 2023Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networkscited by 2
  2. 2023Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approachcited by 2
  3. 2023A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoTcited by 0