Digital Twin Synchronization Over Mobile Embodied AI Network With Agentic Intelligence
Zhouxiang Zhao, Jiaxiang Wang, Yahao Ding, Yinchao Yang, Zhaohui Yang, Mohammad Shikh-Bahaei, Julie A. McCann, Zhaoyang Zhang, Kaibin Huang

TL;DR
This paper introduces a novel agentic AI-powered mobile network framework for digital twin synchronization, optimizing resource allocation and mobility to reduce deviation and latency in virtual representations.
Contribution
It proposes a hybrid architecture with a hierarchical optimization algorithm combining dynamic matching and resource tuning for improved DT synchronization.
Findings
The algorithm converges and outperforms baseline schemes in reducing twin deviation.
Semantic compression reduces bandwidth needs while maintaining latency.
Autonomous velocity adaptation balances energy consumption and latency.
Abstract
Efficient digital twin (DT) synchronization relies on maintaining high-fidelity virtual representations with minimal age of information (AoI). However, the synergistic potential of cooperative sensing and autonomous mobility of the sensing agent remains underexplored in existing DT synchronization frameworks. In this paper, we propose an agentic AI-empowered mobile embodied AI network (MEAN) framework for DT synchronization. In the proposed hybrid architecture, the base station (BS) conducts global orchestration, while the agents autonomously execute a five-stage closed-loop workflow: move-to-sense, cooperative sensing, onboard semantic processing, channel-aware mobility, and uplink transmission. To optimize synchronization performance, we formulate a joint topology dispatching and multidimensional resource allocation problem aimed at minimizing the maximum twin deviation across…
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