We investigate the problem of supporting Industrial Internet of Things user
equipment (IIoT UEs) with intent (i.e., requested quality of service (QoS)) and
random traffic arrival. A deep reinforcement learning (DRL) based centralized
dynamic scheduler for time-frequency resources is proposed to learn how to
schedule the available communication resources among the IIoT UEs. The proposed
scheduler leverages an RL framework to adapt to the dynamic changes in the
wireless communication system and traffic arrivals. Moreover, a graph-based
reduction scheme is proposed to reduce the state and action space of the RL
framework to allow fast convergence and a better learning strategy. Simulation
results demonstrate the effectiveness of the proposed intelligent scheduler in
guaranteeing the expressed intent of IIoT UEs compared to several traditional
scheduling schemes, such as round-robin, semi-static, and heuristic approaches.
The proposed scheduler also outperforms the contention-free and
contention-based schemes in maximizing the number of successfully computed
tasks.

支持工业物联网用户设备（IIoT UEs）根据所需服务质量（QoS）和随机流量到达的问题进行调查，提出了一种基于深度强化学习（DRL）的集中式动态调度器，用于学习如何在可用通信资源之间安排 IIoT UEs 的通信资源，通过图形化的简化方案以实现快速收敛和更好的学习策略，模拟结果证明了所提出的智能调度器在保证 IIoT UEs 的期望意图方面的有效性，并与传统调度方案（如轮询、半静态和启发式方法）以及无冲突和基于竞争的方案相比具有更好的性能，可以最大化成功计算任务的数量。