Digital Twin Synchronization Over Mobile Embodied AI Network With Agentic Intelligence
For digital twin systems requiring high-fidelity synchronization, this work introduces a novel framework that leverages cooperative sensing and autonomous mobility, but the improvements are incremental over existing baselines.
This paper proposes an agentic AI-empowered mobile embodied AI network (MEAN) framework for digital twin synchronization, which minimizes the maximum twin deviation across regions by jointly optimizing topology dispatching and multidimensional resource allocation. Simulation results show the proposed algorithm significantly reduces synchronization deviation compared to baselines, with semantic compression substituting for channel resources and autonomous velocity adaptation navigating the energy-time trade-off.
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 regions, subject to heterogeneous sensing fidelity and energy budget constraints. To tackle this, we develop a hierarchical two-layer optimization algorithm, where the outer-layer refines multi-agent assignment via a dynamic matching game, and the inner-layer iteratively optimizes the continuous resources. Extensive simulation results verify the convergence of the proposed algorithm and demonstrate its substantial superiority over multiple baseline schemes in reducing synchronization deviation. Furthermore, the results reveal that semantic compression serves as a vital substitute for channel resources in latency reduction under constrained bandwidth, while autonomous velocity adaptation provides an essential degree of freedom for the system to navigate the fundamental energy-time trade-off.