A Survey on Robust Deep Joint Source-Channel Coding for Semantic Communications
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908939652694016 |
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| author | Hong, Eunhye Park, Taewoo Kim, Yongjune |
| author_facet | Hong, Eunhye Park, Taewoo Kim, Yongjune |
| contents | Semantic communications (SCs) aim to transmit only the essential information required to perform given tasks, thereby improving communication efficiency. Deep learning-based joint source-channel coding (deep JSCC) has emerged as a promising approach for SC systems; however, its performance often degrades when the deployment channels differ from the training channel conditions, making robustness a critical requirement. This paper presents a structured overview of recent methodologies for enhancing the robustness of deep JSCC. Specifically, existing approaches are categorized into two classes: robust training approaches and adaptive approaches, with the latter further divided into adaptive semantic feature selection, physical-layer adaptation, and semantic feature adaptation. Finally, we discuss promising directions, including multi-task generalization and explainability in robust SC systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_04413 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Survey on Robust Deep Joint Source-Channel Coding for Semantic Communications Hong, Eunhye Park, Taewoo Kim, Yongjune Signal Processing Semantic communications (SCs) aim to transmit only the essential information required to perform given tasks, thereby improving communication efficiency. Deep learning-based joint source-channel coding (deep JSCC) has emerged as a promising approach for SC systems; however, its performance often degrades when the deployment channels differ from the training channel conditions, making robustness a critical requirement. This paper presents a structured overview of recent methodologies for enhancing the robustness of deep JSCC. Specifically, existing approaches are categorized into two classes: robust training approaches and adaptive approaches, with the latter further divided into adaptive semantic feature selection, physical-layer adaptation, and semantic feature adaptation. Finally, we discuss promising directions, including multi-task generalization and explainability in robust SC systems. |
| title | A Survey on Robust Deep Joint Source-Channel Coding for Semantic Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.04413 |