S-MDMA: Sensitivity-Aware Model Division Multiple Access for Satellite-Ground Semantic Communication
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910000080748544 |
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| author | Cao, Hui Meng, Rui Han, Shujun Gao, Song Xu, Xiaodong Zhang, Ping |
| author_facet | Cao, Hui Meng, Rui Han, Shujun Gao, Song Xu, Xiaodong Zhang, Ping |
| contents | Satellite-ground semantic communication (SemCom) is expected to play a pivotal role in convergence of communication and AI (ComAI), particularly in enabling intelligent and efficient multi-user data transmission. However, the inherent bandwidth constraints and user interference in satellite-ground systems pose significant challenges to semantic fidelity and transmission robustness. To address these issues, we propose a sensitivity-aware model division multiple access (S-MDMA) framework tailored for bandwidth-limited multi-user scenarios. The proposed framework first performs semantic extraction and merging based on the MDMA architecture to consolidate redundant information. To further improve transmission efficiency, a semantic sensitivity sorting algorithm is presented, which can selectively retain key semantic features. In addition, to mitigate inter-user interference, the framework incorporates orthogonal embedding of semantic features and introduces a multi-user reconstruction loss function to guide joint optimization. Experimental results on open-source datasets demonstrate that S-MDMA consistently outperforms existing methods, achieving robust and high-fidelity reconstruction across diverse signal-to-noise ratio (SNR) conditions and user configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17731 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | S-MDMA: Sensitivity-Aware Model Division Multiple Access for Satellite-Ground Semantic Communication Cao, Hui Meng, Rui Han, Shujun Gao, Song Xu, Xiaodong Zhang, Ping Signal Processing Satellite-ground semantic communication (SemCom) is expected to play a pivotal role in convergence of communication and AI (ComAI), particularly in enabling intelligent and efficient multi-user data transmission. However, the inherent bandwidth constraints and user interference in satellite-ground systems pose significant challenges to semantic fidelity and transmission robustness. To address these issues, we propose a sensitivity-aware model division multiple access (S-MDMA) framework tailored for bandwidth-limited multi-user scenarios. The proposed framework first performs semantic extraction and merging based on the MDMA architecture to consolidate redundant information. To further improve transmission efficiency, a semantic sensitivity sorting algorithm is presented, which can selectively retain key semantic features. In addition, to mitigate inter-user interference, the framework incorporates orthogonal embedding of semantic features and introduces a multi-user reconstruction loss function to guide joint optimization. Experimental results on open-source datasets demonstrate that S-MDMA consistently outperforms existing methods, achieving robust and high-fidelity reconstruction across diverse signal-to-noise ratio (SNR) conditions and user configurations. |
| title | S-MDMA: Sensitivity-Aware Model Division Multiple Access for Satellite-Ground Semantic Communication |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.17731 |