Dual Mind World Model Inspired Network Digital Twin for Access Scheduling
This addresses scheduling challenges for dynamic networked systems like industrial IoT, representing an incremental improvement over existing methods.
The paper tackles the problem of intelligent scheduling for networked systems like industrial IoT by proposing a Digital Twin-enabled framework inspired by Dual Mind World Model architecture, achieving superior performance in bursty, interference-limited, and deadline-sensitive environments while maintaining interpretability and sample efficiency.
Emerging networked systems such as industrial IoT and real-time cyber-physical infrastructures demand intelligent scheduling strategies capable of adapting to dynamic traffic, deadlines, and interference constraints. In this work, we present a novel Digital Twin-enabled scheduling framework inspired by Dual Mind World Model (DMWM) architecture, for learning-informed and imagination-driven network control. Unlike conventional rule-based or purely data-driven policies, the proposed DMWM combines short-horizon predictive planning with symbolic model-based rollout, enabling the scheduler to anticipate future network states and adjust transmission decisions accordingly. We implement the framework in a configurable simulation testbed and benchmark its performance against traditional heuristics and reinforcement learning baselines under varied traffic conditions. Our results show that DMWM achieves superior performance in bursty, interference-limited, and deadline-sensitive environments, while maintaining interpretability and sample efficiency. The proposed design bridges the gap between network-level reasoning and low-overhead learning, marking a step toward scalable and adaptive NDT-based network optimization.