Distributionally Robust Wireless Semantic Communication with Large AI Models
Journal:
arXiv
Published Date:
May 28, 2025
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.
