Deep Semantic Inference over the Air: An Efficient Task-Oriented Communication System

• URL: http://arxiv.org/abs/2508.12748v1
• Date: Mon, 18 Aug 2025 09:18:07 GMT
• Title: Deep Semantic Inference over the Air: An Efficient Task-Oriented Communication System
• Authors: Chenyang Wang, Roger Olsson, Stefan Forsström, Qing He,
• Abstract summary: We explore a deep learning-based task-oriented communication framework that jointly considers classification performance, computational latency, and communication cost.<n>We adopt ResNets-based models and evaluate them on the CIFAR-10 and CIFAR-100 datasets to simulate real-world classification tasks in wireless environments.<n>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.
• Score: 8.459189717032647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 adopt ResNets-based models and evaluate them 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.

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