Title: Distributionally Robust Wireless Semantic Communication with Large AI Models Show Chinese title

Title: 具有大型人工智能模型的分布鲁棒无线语义通信

Abstract: 6G wireless systems are expected to support massive volumes of data with ultra-low latency. However, conventional bit-level transmission strategies cannot support the efficiency and adaptability required by modern, data-intensive applications. The concept of semantic communication (SemCom) addresses this limitation by focusing on transmitting task-relevant semantic information instead of raw data. While recent efforts incorporating deep learning and large-scale AI models have improved SemCom's performance, existing systems remain vulnerable to both semantic-level and transmission-level noise because they often rely on domain-specific architectures that hinder generalizability. In this paper, a novel and generalized semantic communication framework called WaSeCom is proposed to systematically address uncertainty and enhance robustness. In particular, Wasserstein distributionally robust optimization is employed to provide resilience against semantic misinterpretation and channel perturbations. A rigorous theoretical analysis is performed to establish the robust generalization guarantees of the proposed framework. Experimental results on image and text transmission demonstrate that WaSeCom achieves improved robustness under noise and adversarial perturbations. These results highlight its effectiveness in preserving semantic fidelity across varying wireless conditions. Show Chinese abstract

Abstract: 6G无线系统有望支持海量数据传输且具备超低延迟。 然而，传统的比特级传输策略无法满足现代数据密集型应用所需的效率和适应性。 语义通信（SemCom）的概念通过专注于传输与任务相关的语义信息而非原始数据来解决这一局限性。 尽管最近结合深度学习和大规模AI模型的努力提升了SemCom的性能，但现有系统仍容易受到语义层面和传输层面噪声的影响，因为它们通常依赖于特定领域的架构，从而限制了通用性。 本文提出了一种名为WaSeCom的新颖且通用的语义通信框架，以系统地应对不确定性并增强鲁棒性。 特别是采用了Wasserstein分布鲁棒优化，以提供对语义误解和信道扰动的韧性。 对该框架进行了严格的理论分析，以建立其鲁棒泛化保证。 图像和文本传输的实验结果表明， WaSeCom在噪声和对抗性扰动下的鲁棒性有所提高。 这些结果突显了它在不同无线条件下保持语义保真度的有效性。