Distributionally Robust Wireless Semantic Communication with Large AI Models
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909998413512704 |
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| author | Le, Long Tan Wanasekara, Senura Hansaja Niu, Zerun Tran, Nguyen H. Vo, Phuong Saad, Walid Niyato, Dusit Han, Zhu Hong, Choong Seon Poor, H. Vincent |
| author_facet | Le, Long Tan Wanasekara, Senura Hansaja Niu, Zerun Tran, Nguyen H. Vo, Phuong Saad, Walid Niyato, Dusit Han, Zhu Hong, Choong Seon Poor, H. Vincent |
| contents | Semantic communication (SemCom) has emerged as a promising paradigm for 6G wireless systems by transmitting task-relevant information rather than raw bits, yet existing approaches remain vulnerable to dual sources of uncertainty: semantic misinterpretation arising from imperfect feature extraction and transmission-level perturbations from channel noise. Current deep learning based SemCom systems typically employ domain-specific architectures that lack robustness guarantees and fail to generalize across diverse noise conditions, adversarial attacks, and out-of-distribution data. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributionally Robust Wireless Semantic Communication with Large AI Models Le, Long Tan Wanasekara, Senura Hansaja Niu, Zerun Tran, Nguyen H. Vo, Phuong Saad, Walid Niyato, Dusit Han, Zhu Hong, Choong Seon Poor, H. Vincent Networking and Internet Architecture Emerging Technologies Information Theory Machine Learning Semantic communication (SemCom) has emerged as a promising paradigm for 6G wireless systems by transmitting task-relevant information rather than raw bits, yet existing approaches remain vulnerable to dual sources of uncertainty: semantic misinterpretation arising from imperfect feature extraction and transmission-level perturbations from channel noise. Current deep learning based SemCom systems typically employ domain-specific architectures that lack robustness guarantees and fail to generalize across diverse noise conditions, adversarial attacks, and out-of-distribution data. 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. |
| title | Distributionally Robust Wireless Semantic Communication with Large AI Models |
| topic | Networking and Internet Architecture Emerging Technologies Information Theory Machine Learning |
| url | https://arxiv.org/abs/2506.03167 |