# A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT

arXiv: https://arxiv.org/abs/2401.06135 · HTML: https://mlsanity.com/paper/2401.06135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/1803.04623/) (2018)
- [Matroid Bandits: Fast Combinatorial Optimization with Learning](https://mlsanity.com/paper/1403.5045/) (2014)
- [Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale](https://mlsanity.com/paper/1801.01270/) (2018)
- [5G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technology](https://mlsanity.com/paper/1806.06898/) (2018)
- [Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning](https://mlsanity.com/paper/1901.04292/) (2018)
- [Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks](https://mlsanity.com/paper/2002.11045/) (2020)
- [Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation](https://mlsanity.com/paper/2009.08346/) (2020)
- [Low-latency Networking: Where Latency Lurks and How to Tame It](https://mlsanity.com/paper/1808.02079/) (2018)
- [Analyzing Grant-Free Access for URLLC Service](https://mlsanity.com/paper/2002.07842/) (2020)
- [Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2211.12201/) (2022)
- [Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks](https://mlsanity.com/paper/2302.11268/) (2023)
- [Adaptive Rate NOMA for Cellular IoT Networks](https://mlsanity.com/paper/2108.08235/) (2021)
- [A New Scheduler for URLLC in 5G NR IIoT Networks with Spatio-Temporal Traffic Correlations](https://mlsanity.com/paper/2302.12681/) (2023)

## Abstract (arXiv)

Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes underlying the production chains. However, standard protocols for allocating wireless resources may not optimize the latency-reliability trade-off, especially for uplink communication. For example, centralized grant-based scheduling can ensure almost zero collisions, but introduces delays in the way resources are requested by the User Equipments (UEs) and granted by the gNB. In turn, distributed scheduling (e.g., based on random access), in which UEs autonomously choose the resources for transmission, may lead to potentially many collisions especially when the traffic increases. In this work we propose DIStributed combinatorial NEural linear Thompson Sampling (DISNETS), a novel scheduling framework that combines the best of the two worlds. By leveraging a feedback signal from the gNB and reinforcement learning, the UEs are trained to autonomously optimize their uplink transmissions by selecting the available resources to minimize the number of collisions, without additional message exchange to/from the gNB. DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm, which has been further extended to admit multiple parallel actions. We demonstrate the superior performance of DISNETS in addressing URLLC in IIoT scenarios compared to other baselines.