Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929700560961536 |
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| author | Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal |
| author_facet | Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal |
| contents | In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03949 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Signal Processing In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. |
| title | Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2502.03949 |