mlsanityData to 14 Oct 2026

Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization

arXiv 2512.23493 ↗First posted 29 Dec 2025cs.LG cs.AIcited by 1

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Abstract (arXiv)

In this article, we consider an industrial internet of things (IIoT) network supporting multi-device dynamic ultra-reliable low-latency communication (URLLC) while the channel state information (CSI) is imperfect. A joint link adaptation (LA) and device scheduling (including the order) design is provided, aiming at maximizing the total transmission rate under strict block error rate (BLER) constraints. In particular, a Bayesian optimization (BO) driven Twin Delayed Deep Deterministic Policy Gradient (TD3) method is proposed, which determines the device served order sequence and the corresponding modulation and coding scheme (MCS) adaptively based on the imperfect CSI. Note that the imperfection of CSI, error sample imbalance in URLLC networks, as well as the parameter sensitivity nature of the TD3 algorithm likely diminish the algorithm's convergence speed and reliability. To address such an issue, we proposed a BO based training mechanism for the convergence speed improvement, which provides a more reliable learning direction and sample selection method to track the imbalance sample problem. Via extensive simulations, we show that the proposed algorithm achieves faster convergence and higher sum-rate performance compared to existing solutions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 92 references that are in the map. First posted on arXiv 29 Dec 2025.

Builds on

27 of 92 references.

References · 27

  1. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  2. 20216G Internet of Things: A Comprehensive Surveycited by 39
  3. 2024Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learningcited by 16
  4. 2022Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Servicescited by 12
  5. 2023A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networkscited by 12
  6. 2025Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networkingcited by 11
  7. 2024Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environmentscited by 10
  8. 2025Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learningcited by 9
  9. 2024Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learningcited by 8
  10. 2020Reinforcement Learning for Efficient and Tuning-Free Link Adaptationcited by 7
  11. 2023Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Managementcited by 7
  12. 2023The Bridge Toward 6G: 5G-Advanced Evolution in 3GPP Release 19cited by 7
15 more
  1. 2022A DRL Approach for RIS-Assisted Full-Duplex UL and DL Transmission: Beamforming, Phase Shift and Power Optimizationcited by 5
  2. 2019Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networkscited by 4
  3. 2023Resource Scheduling for UAVs-aided D2D Networks: A Multi-objective Optimization Approachcited by 4
  4. 2022Reinforcement Learning-based Joint User Scheduling and Link Configuration in Millimeter-wave Networkscited by 3
  5. 2017Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systemscited by 2
  6. 2019Sequential Gaussian Processes for Online Learning of Nonstationary Functionscited by 2
  7. 2019Conservative Link Adaptation for Ultra Reliable Low Latency Communicationscited by 2
  8. 2025UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learningcited by 2
  9. 2025Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Methodcited by 2
  10. 2020Interference Distribution Prediction for Link Adaptation in Ultra-Reliable Low-Latency Communicationscited by 1
  11. 2020Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networkscited by 1
  12. 2023Joint User Scheduling and Precoding for XL-MIMO Systems with Imperfect CSIcited by 1
  13. 2023Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networkscited by 1
  14. 2024Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networkscited by 1
  15. 2025Cellular Network Design for UAV Corridors via Data-driven High-dimensional Bayesian Optimizationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RANcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Intent-Aware DRL-Based NOMA Uplink Dynamic Scheduler for IIoT
  2. 2025AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach
  3. 2026Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks
  4. 2025CQI-Based Interference Prediction for Link Adaptation in Industrial Sub-networks ↗
  5. 2024Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario
  6. 2024Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things
  7. 2021Average Rate Analysis of RIS-aided Short Packet Communication in URLLC Systems ↗
  8. 2025Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning