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

arXiv: https://arxiv.org/abs/2512.23493 · HTML: https://mlsanity.com/paper/2512.23493/

## Key facts

- Cited by 1 papers 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 (most cited first, 27 of 27)

- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08914/) (2024)
- [Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services](https://mlsanity.com/paper/2202.06435/) (2022)
- [A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks](https://mlsanity.com/paper/2309.01426/) (2023)
- [Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking](https://mlsanity.com/paper/2502.18118/) (2025)
- [Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments](https://mlsanity.com/paper/2404.14140/) (2024)
- [Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05824/) (2025)
- [Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning](https://mlsanity.com/paper/2404.07450/) (2024)
- [Reinforcement Learning for Efficient and Tuning-Free Link Adaptation](https://mlsanity.com/paper/2010.08651/) (2020)
- [Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management](https://mlsanity.com/paper/2301.06510/) (2023)
- [The Bridge Toward 6G: 5G-Advanced Evolution in 3GPP Release 19](https://mlsanity.com/paper/2312.15174/) (2023)
- [A DRL Approach for RIS-Assisted Full-Duplex UL and DL Transmission: Beamforming, Phase Shift and Power Optimization](https://mlsanity.com/paper/2212.13854/) (2022)
- [Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks](https://mlsanity.com/paper/1912.04030/) (2019)
- [Resource Scheduling for UAVs-aided D2D Networks: A Multi-objective Optimization Approach](https://mlsanity.com/paper/2311.16116/) (2023)
- [Reinforcement Learning-based Joint User Scheduling and Link Configuration in Millimeter-wave Networks](https://mlsanity.com/paper/2207.03526/) (2022)
- [Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems](https://mlsanity.com/paper/1708.00994/) (2017)
- [Sequential Gaussian Processes for Online Learning of Nonstationary Functions](https://mlsanity.com/paper/1905.10003/) (2019)
- [Conservative Link Adaptation for Ultra Reliable Low Latency Communications](https://mlsanity.com/paper/1908.02227/) (2019)
- [UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2502.07388/) (2025)
- [Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method](https://mlsanity.com/paper/2507.08429/) (2025)
- [Interference Distribution Prediction for Link Adaptation in Ultra-Reliable Low-Latency Communications](https://mlsanity.com/paper/2007.00306/) (2020)
- [Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks](https://mlsanity.com/paper/2011.13634/) (2020)
- [Joint User Scheduling and Precoding for XL-MIMO Systems with Imperfect CSI](https://mlsanity.com/paper/2302.13178/) (2023)
- [Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks](https://mlsanity.com/paper/2310.00396/) (2023)
- [Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks](https://mlsanity.com/paper/2401.11377/) (2024)
- [Cellular Network Design for UAV Corridors via Data-driven High-dimensional Bayesian Optimization](https://mlsanity.com/paper/2504.05176/) (2025)

## Built on by (most cited first, 1 of 1)

- [LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN](https://mlsanity.com/paper/2606.23110/) (2026)

## 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.