TWIST: Closed-Loop token Synchronization for Application-Aware Wireless Digital Twins

2026-05-26

AI Summary

This work addresses the challenge of efficiently synchronizing the semantic state of a physical environment with its digital twin under limited and time-varying wireless resources, where conventional pixel-level or uniformly protected transmission proves suboptimal. To this end, the authors propose the TWIST framework, which encodes observations into semantic tokens grouped by task relevance and dynamically applies non-uniform error protection conditioned on channel states and application priorities. TWIST further incorporates a confidence-guided token erasure and semantic completion mechanism to enable closed-loop synchronization. Experimental results demonstrate that TWIST significantly improves traffic state inference accuracy and semantic synchronization performance in dynamic road scenarios while reducing average synchronization overhead.

Abstract

Wireless digital twins require repeated synchronization between a time-evolving physical scene and its digital counterpart under limited and time-varying communication resources. For perception-centric twins, pixel-domain transmission or uniformly protected bitstreams can be mismatched to the semantic state consumed by twin-side applications. This paper proposes TWIST, a closed-loop token synchronization framework for application-aware wireless digital twins. TWIST represents each physical observation as a token and synchronizes this state over a wireless link, rather than optimizing visual reconstruction. Token positions are grouped by task relevance and protected through mode-conditioned unequal error protection under low-, medium-, and high-synchronization modes. At the twin side, decoding confidence converts unreliable hard token decisions into erasures, which are restored by a completion model before updating the semantic twin state. The recovered state supports traffic-state inference and generates compact feedback statistics, including channel quality, receiver uncertainty, semantic drift, and application priority, for subsequent mode adaptation. Experiments on a dynamic road-scene digital-twin scenario show that TWIST improves traffic-state inference and semantic twin-state synchronization compared with fixed-mode and channel-only adaptation strategies, while reducing the average synchronization cost relative to always-high transmission.

Problem

• wireless digital twins
• semantic synchronization
• application-aware communication
• token representation
• time-varying resources

Innovation

• token synchronization
• application-aware digital twins
• unequal error protection
• semantic state recovery
• closed-loop adaptation