Dual Mind World Model Inspired Network Digital Twin for Access Scheduling

TL;DR

This paper introduces a Digital Twin-inspired network scheduling framework based on a Dual Mind World Model that anticipates future network states for improved control in dynamic, interference-prone environments.

Contribution

It presents a novel DMWM architecture combining predictive planning and symbolic rollout for adaptive network scheduling, outperforming traditional methods.

Findings

Superior performance in bursty and interference-limited scenarios

Maintains interpretability and sample efficiency

Bridges network reasoning with low-overhead learning

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.