Multi-user Wireless Image Semantic Transmission over MIMO Multiple Access Channels
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917982380228608 |
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| author | Xie, Bingyan Wu, Yongpeng Shu, Feng Wang, Jiangzhou Zhang, Wenjun |
| author_facet | Xie, Bingyan Wu, Yongpeng Shu, Feng Wang, Jiangzhou Zhang, Wenjun |
| contents | This paper focuses on a typical uplink transmission scenario over multiple-input multiple-output multiple access channel (MIMO-MAC) and thus propose a multi-user learnable CSI fusion semantic communication (MU-LCFSC) framework. It incorporates CSI as the side information into both the semantic encoders and decoders to generate a proper feature mask map in order to produce a more robust attention weight distribution. Especially for the decoding end, a cooperative successive interference cancellation procedure is conducted along with a cooperative mask ratio generator, which flexibly controls the mask elements of feature mask maps. Numerical results verify the superiority of proposed MU-LCFSC compared to DeepJSCC-NOMA over 3 dB in terms of PSNR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07969 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-user Wireless Image Semantic Transmission over MIMO Multiple Access Channels Xie, Bingyan Wu, Yongpeng Shu, Feng Wang, Jiangzhou Zhang, Wenjun Networking and Internet Architecture Machine Learning Signal Processing This paper focuses on a typical uplink transmission scenario over multiple-input multiple-output multiple access channel (MIMO-MAC) and thus propose a multi-user learnable CSI fusion semantic communication (MU-LCFSC) framework. It incorporates CSI as the side information into both the semantic encoders and decoders to generate a proper feature mask map in order to produce a more robust attention weight distribution. Especially for the decoding end, a cooperative successive interference cancellation procedure is conducted along with a cooperative mask ratio generator, which flexibly controls the mask elements of feature mask maps. Numerical results verify the superiority of proposed MU-LCFSC compared to DeepJSCC-NOMA over 3 dB in terms of PSNR. |
| title | Multi-user Wireless Image Semantic Transmission over MIMO Multiple Access Channels |
| topic | Networking and Internet Architecture Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2504.07969 |