{"ID":2881040,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.12748","arxiv_id":"2508.12748","title":"Deep Semantic Inference over the Air: An Efficient Task-Oriented Communication System","abstract":"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.","short_abstract":"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 p...","url_abs":"https://arxiv.org/abs/2508.12748","url_pdf":"https://arxiv.org/pdf/2508.12748v2","authors":"[\"Chenyang Wang\",\"Roger Olsson\",\"Stefan Forsström\",\"Qing He\"]","published":"2025-08-18T09:18:07Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"cs.LG\"]","methods":"[]","has_code":false}