用双心智世界模型构建可解释的智能调度系统，提升复杂网络的响应能力。
Dual Mind World Model Inspired Network Digital Twin for Access Scheduling
- 结合短期预测与符号化模型推演，实现对未来网络状态的预判。
- 在突发流量、干扰受限和高时延敏感场景下性能超越传统方法。
- 兼顾可解释性与样本效率，适合工业物联网等实时系统应用。
新兴网络系统如工业物联网和实时网络物理基础设施需要能够适应动态流量、截止时间与干扰约束的智能调度策略。本文提出一种受双心智世界模型（DMWM）启发的数字孪生增强型调度框架，实现基于学习与想象驱动的网络控制。不同于传统的规则驱动或纯数据驱动策略，该方法将短时程预测规划与符号化模型推演相结合，使调度器能预判未来网络状态并相应调整传输决策。我们在可配置仿真测试平台中实现了该框架，并在多种流量条件下与传统启发式算法及强化学习基线进行了对比。结果表明，DMWM在突发流量、干扰受限及截止时间敏感环境中表现更优，同时保持了可解释性与样本高效性。该设计弥合了网络层级推理与低开销学习之间的差距，标志着向可扩展、自适应的基于数字孪生的网络优化迈出关键一步。
原文摘要 · Abstract (English)
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.
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