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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2508.12748 |
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| _version_ | 1866910002383421440 |
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| author | Wang, Chenyang Olsson, Roger Forsström, Stefan He, Qing |
| author_facet | Wang, Chenyang Olsson, Roger Forsström, Stefan He, Qing |
| contents | Empowered by deep learning, semantic communication marks a paradigm shift from transmitting raw data to conveying task-relevant meaning, enabling more efficient and intelligent wireless systems. In this study, we explore a deep learning-based task-oriented communication framework that jointly considers classification performance, computational latency, and communication cost. We evaluate ResNets-based models on the CIFAR-10 and CIFAR-100 datasets to simulate real-world classification tasks in wireless environments. We partition the model at various points to simulate split inference across a wireless channel. By varying the split location and the size of the transmitted semantic feature vector, we systematically analyze the trade-offs between task accuracy and resource efficiency. Experimental results show that, with appropriate model partitioning and semantic feature compression, the system can retain over 85\% of baseline accuracy while significantly reducing both computational load and communication overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Semantic Inference over the Air: An Efficient Task-Oriented Communication System Wang, Chenyang Olsson, Roger Forsström, Stefan He, Qing Information Theory Machine Learning Empowered by deep learning, semantic communication marks a paradigm shift from transmitting raw data to conveying task-relevant meaning, enabling more efficient and intelligent wireless systems. In this study, we explore a deep learning-based task-oriented communication framework that jointly considers classification performance, computational latency, and communication cost. We evaluate ResNets-based models on the CIFAR-10 and CIFAR-100 datasets to simulate real-world classification tasks in wireless environments. We partition the model at various points to simulate split inference across a wireless channel. By varying the split location and the size of the transmitted semantic feature vector, we systematically analyze the trade-offs between task accuracy and resource efficiency. Experimental results show that, with appropriate model partitioning and semantic feature compression, the system can retain over 85\% of baseline accuracy while significantly reducing both computational load and communication overhead. |
| title | Deep Semantic Inference over the Air: An Efficient Task-Oriented Communication System |
| topic | Information Theory Machine Learning |
| url | https://arxiv.org/abs/2508.12748 |