Semantic Feature Division Multiple Access for Multi-user Digital Interference Networks
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929417718071296 |
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| author | Ma, Shuai Zhang, Chuanhui Shen, Bin Wu, Youlong Li, Hang Li, Shiyin Shi, Guangming Al-Dhahir, Naofal |
| author_facet | Ma, Shuai Zhang, Chuanhui Shen, Bin Wu, Youlong Li, Hang Li, Shiyin Shi, Guangming Al-Dhahir, Naofal |
| contents | With the ever-increasing user density and quality of service (QoS) demand,5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extracts multi-user semantic information into discrete representations in distinguishable semantic subspaces, which enables multiple users to transmit simultaneously over the same time-frequency resources. Furthermore, based on a robust information bottleneck, we design a SFDMA based multi-user digital semantic interference network for inference tasks, which can achieve approximate orthogonal transmission. Moreover, we propose a SFDMA based multi-user digital semantic interference network for image reconstruction tasks, where the discrete outputs of the semantic encoders of the users are approximately orthogonal, which significantly reduces multi-user interference. Furthermore, we propose an Alpha-Beta-Gamma (ABG) formula for semantic communications, which is the first theoretical relationship between inference accuracy and transmission power. Then, we derive adaptive power control methods with closed-form expressions for inference tasks. Extensive simulations verify the effectiveness and superiority of the proposed SFDMA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08424 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic Feature Division Multiple Access for Multi-user Digital Interference Networks Ma, Shuai Zhang, Chuanhui Shen, Bin Wu, Youlong Li, Hang Li, Shiyin Shi, Guangming Al-Dhahir, Naofal Signal Processing With the ever-increasing user density and quality of service (QoS) demand,5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extracts multi-user semantic information into discrete representations in distinguishable semantic subspaces, which enables multiple users to transmit simultaneously over the same time-frequency resources. Furthermore, based on a robust information bottleneck, we design a SFDMA based multi-user digital semantic interference network for inference tasks, which can achieve approximate orthogonal transmission. Moreover, we propose a SFDMA based multi-user digital semantic interference network for image reconstruction tasks, where the discrete outputs of the semantic encoders of the users are approximately orthogonal, which significantly reduces multi-user interference. Furthermore, we propose an Alpha-Beta-Gamma (ABG) formula for semantic communications, which is the first theoretical relationship between inference accuracy and transmission power. Then, we derive adaptive power control methods with closed-form expressions for inference tasks. Extensive simulations verify the effectiveness and superiority of the proposed SFDMA. |
| title | Semantic Feature Division Multiple Access for Multi-user Digital Interference Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2407.08424 |