TokCom-UEP: Semantic Importance-Matched Unequal Error Protection for Resilient Image Transmission

TL;DR

This paper introduces TokCom-UEP, a novel semantic importance-matched unequal error protection framework for resilient image transmission in 6G networks, addressing the limitations of uniform token importance assumptions in existing systems.

Contribution

It proposes a new UEP coding scheme that leverages semantic importance hierarchies of tokens, improving resilience and efficiency over traditional equal error protection methods.

Findings

TokCom-UEP outperforms EEP in semantic restoration metrics.

It enhances spectral efficiency under low-overhead conditions.

The framework effectively prioritizes critical tokens for recovery.

Abstract

Based on the provided LaTeX code, here is the metadata for the submission form: Title: TokCom-UEP: Semantic Importance-Matched Unequal Error Protection for Resilient Image Transmission Author(s): Kaizheng Zhang, Zuolin Jin, Zhihang Cheng, Ming Zeng, Li Qiao, Zesong Fei Abstract: Token communication (TokCom), an emerging semantic communication framework powered by Large Multimodal Model (LMM), has become a key paradigm for resilient data transmission in 6G networks. A key limitation of existing TokCom designs lies in the assumption of uniform token importance, which leads to the adoption of equal error protection (EEP). However, compressed one-dimensional (1D) token sequences inherently exhibit heterogeneous semantic importance hierarchies, rendering EEP schemes suboptimal. To address this, this paper proposes TokCom-UEP, a novel semantic importance-matched unequal error protection (UEP) framework designed for resilient image transmission. TokCom-UEP integrates rateless UEP coding with the non-uniform semantic importance of tokens by partitioning source tokens into nested expanding windows, assigning higher selection probabilities to windows containing critical tokens to ensure their prioritized recovery. Simulation results demonstrate that TokCom-UEP outperforms EEP schemes in terms of three core semantic restoration metrics and spectral efficiency under low-overhead conditions.