Dual Mind World Model Inspired Network Digital Twin for Access Scheduling

• URL: http://arxiv.org/abs/2602.04566v1
• Date: Wed, 04 Feb 2026 13:53:55 GMT
• Title: Dual Mind World Model Inspired Network Digital Twin for Access Scheduling
• Authors: Hrishikesh Dutta, Roberto Minerva, Noel Crespi,
• Abstract summary: We present a novel Digital Twin-enabled scheduling framework inspired by Dual Mind World Model (DMWM) architecture.<n>Unlike conventional rule-based or purely data-driven policies, the proposed DMWM combines short-horizon predictive planning with symbolic model-based rollout.<n>Our results show that DMWM achieves superior performance in bursty, interference-limited, and deadline-sensitive environments.
• Score: 0.904861150954008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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