For cyber-physical systems in the 6G era, semantic communications connecting distributed devices for dynamic control and remote state estimation are required to guarantee application-level performance, not merely focus on communication-centric performance. Semantics here is a measure of the usefulness of information transmissions. Semantic-aware transmission scheduling of a large system often involves a large decision-making space, and the optimal policy cannot be obtained by existing algorithms effectively. In this paper, we first investigate the fundamental properties of the optimal semantic-aware scheduling policy and then develop advanced deep reinforcement learning (DRL) algorithms by leveraging the theoretical guidelines. Our numerical results show that the proposed algorithms can substantially reduce training time and enhance training performance compared to benchmark algorithms.

翻译：针对6G时代的网络物理系统，需要为连接分布式设备的动态控制与远程状态估计设计语义通信，以保障应用层性能，而非仅仅关注通信中心性能。此处的语义是对信息传输有用性的度量。大规模系统的语义感知传输调度通常涉及巨大的决策空间，现有算法无法有效获得最优策略。本文首先研究最优语义感知调度策略的基本特性，然后利用理论指导框架开发先进的深度强化学习算法。数值结果表明，与基准算法相比，所提算法能显著缩短训练时间并提升训练性能。