SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910628863541248 |
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| author | Yan, Yuna Li, Lixin Zhang, Xin Lin, Wensheng Cheng, Wenchi Han, Zhu |
| author_facet | Yan, Yuna Li, Lixin Zhang, Xin Lin, Wensheng Cheng, Wenchi Han, Zhu |
| contents | Most current Deep Learning-based Semantic Communication (DeepSC) systems are designed and trained exclusively for particular single-channel conditions, which restricts their adaptability and overall bandwidth utilization. To address this, we propose an innovative Semantic Adaptive Feature Extraction (SAFE) framework, which significantly improves bandwidth efficiency by allowing users to select different sub-semantic combinations based on their channel conditions. This paper also introduces three advanced learning algorithms to optimize the performance of SAFE framework as a whole. Through a series of simulation experiments, we demonstrate that the SAFE framework can effectively and adaptively extract and transmit semantics under different channel bandwidth conditions, of which effectiveness is verified through objective and subjective quality evaluations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_01597 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications Yan, Yuna Li, Lixin Zhang, Xin Lin, Wensheng Cheng, Wenchi Han, Zhu Networking and Internet Architecture Machine Learning Signal Processing Most current Deep Learning-based Semantic Communication (DeepSC) systems are designed and trained exclusively for particular single-channel conditions, which restricts their adaptability and overall bandwidth utilization. To address this, we propose an innovative Semantic Adaptive Feature Extraction (SAFE) framework, which significantly improves bandwidth efficiency by allowing users to select different sub-semantic combinations based on their channel conditions. This paper also introduces three advanced learning algorithms to optimize the performance of SAFE framework as a whole. Through a series of simulation experiments, we demonstrate that the SAFE framework can effectively and adaptively extract and transmit semantics under different channel bandwidth conditions, of which effectiveness is verified through objective and subjective quality evaluations. |
| title | SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications |
| topic | Networking and Internet Architecture Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2410.01597 |