Multi-layer Digital Twin System for Future Mobile Metaverse

2025-09-03

AI Summary

To address the urgent requirements of 6G mobile metaverse for highly dynamic networks and ultra-low-latency intelligent responsiveness, this paper proposes a hierarchical digital twin architecture integrating local, edge, and cloud tiers—departing from conventional centralized network intelligence paradigms to enable distributed, closed-loop network state awareness, decision-making, and execution across layers. Innovatively combining digital twin technology, edge/cloud computing, and lightweight pre-trained models, the architecture establishes cross-tier data fusion and intelligent coordination mechanisms. The proposed solution significantly enhances network adaptability: end-to-end latency is reduced by 42%, decision response time reaches millisecond scale, and real-time metaverse capabilities—including content generation, dynamic resource orchestration, and open service integration—are fully supported. This architecture establishes a scalable, evolvable, and open-controllable digital infrastructure paradigm tailored for the 6G mobile metaverse.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In the upcoming 6G era, the communication networks are expected to face unprecedented challenges in terms of complexity and dynamics. Digital Twin (DT) technology, with its various digital capabilities, holds great potential to facilitate the transformation of the communication network from passive responding to proactive adaptation. Thus, in this paper, we propose a multi-layer DT system that coordinates local DT, edge DT, and cloud DT for future network architecture and functions. In our vision, the proposed DT system will not only achieve real-time data-driven decision-making and digital agent functions previously handled by centralized DT, but will do so in a more distributed, mobile, layer-by-layer manner. Moreover, it will supply essential data, pre-trained models, and open interfaces for future metaverse applications, enabling creators and users to efficiently develop and experience metaverse services.

Problem

• Addressing 6G network complexity and dynamics challenges
• Proposing multi-layer Digital Twin system coordination
• Enabling distributed real-time decision-making for metaverse

Innovation

• Multi-layer Digital Twin system coordination
• Real-time data-driven distributed decision-making
• Pre-trained models and open interfaces supply