Multi-level Reliability Interface for Semantic Communications over Wireless Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914861165838336 |
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| author | Tung, Tze-Yang Esfahanizadeh, Homa Du, Jinfeng Viswanathan, Harish |
| author_facet | Tung, Tze-Yang Esfahanizadeh, Homa Du, Jinfeng Viswanathan, Harish |
| contents | Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables semi-JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05487 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-level Reliability Interface for Semantic Communications over Wireless Networks Tung, Tze-Yang Esfahanizadeh, Homa Du, Jinfeng Viswanathan, Harish Information Theory Machine Learning Image and Video Processing Signal Processing Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables semi-JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks. |
| title | Multi-level Reliability Interface for Semantic Communications over Wireless Networks |
| topic | Information Theory Machine Learning Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2407.05487 |