Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach
This addresses scheduling challenges for distributed devices in 6G cyber-physical systems, but it is incremental as it builds on existing DRL methods with theoretical enhancements.
The paper tackles the problem of semantic-aware transmission scheduling for large cyber-physical systems in 6G, where optimal policies are hard to compute, by developing deep reinforcement learning algorithms guided by theoretical analysis; the results show substantial reductions in training time and performance improvements compared to benchmarks.
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.