面向URLLC工业物联网网络的联合链路自适应与设备调度方法：一种基于深度强化学习及贝叶斯优化的方法 (Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization)

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.

翻译：本文研究支持多设备动态超可靠低时延通信（URLLC）的工业物联网网络，其中信道状态信息（CSI）存在不完整性。我们提出了一种联合链路自适应（LA）与设备调度（包括服务次序）的设计方案，旨在满足严格误块率（BLER）约束下最大化总传输速率。具体而言，本文提出了一种贝叶斯优化（BO）驱动的双延迟深度确定性策略梯度（TD3）方法，该方法能够基于不完整的CSI自适应地确定设备服务次序序列及相应的调制与编码方案（MCS）。值得注意的是，CSI的不完整性、URLLC网络中错误样本的不平衡性，以及TD3算法固有的参数敏感性，均可能降低算法的收敛速度与可靠性。为解决此问题，我们提出了一种基于BO的训练机制以提升收敛速度，该机制通过提供更可靠的学习方向与样本选择方法来追踪不平衡样本问题。大量仿真结果表明，与现有解决方案相比，所提算法实现了更快的收敛速度与更高的总速率性能。