针对工业物联网中不完美信道状态下的超可靠低时延通信，提出联合调度与调制编码优化方法。
Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization
- 基于贝叶斯优化改进的TD3强化学习算法，动态决策设备调度顺序与调制编码方案。
- 在不完美信道信息下实现更快收敛与更高总吞吐量，相比现有方法提升显著。
- 适合工业物联网中对可靠性与时延要求极高的实时控制场景。
本文研究支持多设备动态超可靠低时延通信（URLLC）的工业物联网（IIoT）网络，考虑信道状态信息（CSI）不完善的情况。提出联合链路自适应（LA）与设备调度（含服务顺序）设计，旨在满足严格的块错误率（BLER）约束下最大化总传输速率。特别地，提出一种由贝叶斯优化（BO）驱动的改进型双延迟深度确定性策略梯度（TD3）方法，能根据不完善的CSI自适应确定设备服务顺序及相应的调制编码方案（MCS）。针对CSI不完善、URLLC网络中样本不平衡以及TD3算法参数敏感导致的收敛速度慢与可靠性差问题，引入基于贝叶斯优化的训练机制，提供更可靠的更新方向和样本选择策略以缓解样本不平衡。大量仿真表明，所提算法在收敛速度和总速率性能上均优于现有方案。
原文摘要 · Abstract (English)
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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