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

2025-12-29

AI Summary

This paper addresses joint device scheduling and link adaptation (modulation and coding scheme selection) for multi-device ultra-reliable low-latency communication (URLLC) in industrial IoT (IIoT) under imperfect channel state information (CSI). The objective is to maximize the aggregate transmission rate subject to stringent block error rate (BLER) constraints. To overcome slow convergence and unreliable decision-making caused by sample imbalance, CSI distortion, and hyperparameter sensitivity in the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, we innovatively integrate Bayesian optimization into the TD3 framework. The resulting method enables robust and efficient joint optimization. Simulation results demonstrate significantly accelerated convergence, superior aggregate throughput compared to state-of-the-art baselines, and strict adherence to URLLC BLER requirements.

Technology Category

• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Maximizes total transmission rate under strict BLER constraints in URLLC IIoT networks
• Addresses imperfect CSI and error sample imbalance to improve algorithm convergence
• Determines device scheduling order and adaptive MCS using a DRL-based method

Innovation

• Bayesian optimization driven TD3 method for joint link adaptation and scheduling
• BO-based training mechanism to improve convergence speed and reliability
• Adaptive MCS and device order determination under imperfect CSI