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

• URL: http://arxiv.org/abs/2512.23493v1
• Date: Mon, 29 Dec 2025 14:32:34 GMT
• Title: Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization
• Authors: Wei Gao, Paul Zheng, Peng Wu, Yulin Hu, Anke Schmeink,
• Abstract summary: We consider an industrial internet of things (IIoT) network supporting dynamic ultra-reliable low-latency communication (URLLC) while the channel state information (CSI) is imperfect.<n>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 constraints.<n>We show that the proposed algorithm achieves faster convergence and higher sum-rate performance compared to existing solutions.
• Score: 37.86747540440919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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