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

arXiv: https://arxiv.org/abs/2211.12201 · HTML: https://mlsanity.com/paper/2211.12201/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [Machine Learning for Networking: Workflow, Advances and Opportunities](https://mlsanity.com/paper/1709.08339/) (2017)
- [Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges](https://mlsanity.com/paper/1907.08965/) (2019)
- [Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings](https://mlsanity.com/paper/1807.00491/) (2018)
- [Towards 6G Networks: Use Cases and Technologies](https://mlsanity.com/paper/1903.12216/) (2019)
- [Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.09350/) (2018)
- [On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI](https://mlsanity.com/paper/2104.00331/) (2021)
- [Analyzing Grant-Free Access for URLLC Service](https://mlsanity.com/paper/2002.07842/) (2020)
- [Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems](https://mlsanity.com/paper/2109.09376/) (2021)
- [Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT: A Generative Adversarial Network Approach](https://mlsanity.com/paper/2005.04728/) (2020)

## Built on by (most cited first, 3 of 3)

- [Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks](https://mlsanity.com/paper/2302.11268/) (2023)
- [Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach](https://mlsanity.com/paper/2303.14355/) (2023)
- [A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT](https://mlsanity.com/paper/2401.06135/) (2023)

## 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.