September 28, 2025Open Access

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

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The system achieves over 85% of baseline accuracy while minimizing resource usage, highlighting its effectiveness in real-world scenarios.
Tested on CIFAR-10 and CIFAR-100 datasets, the framework balances task accuracy with communication load, enhancing performance in wireless systems.
Using ResNets-based models, the study explores split inference to optimize communication costs and computational requirements across channels.
The findings underline the potential of deep learning in transforming traditional data transmission into meaningful, efficient task-oriented exchanges.

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This framework demonstrates improved classification performance in task-oriented communication, suggesting enhanced efficiency and reduced costs.

Wang et al. (2025) studied this question.

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This framework demonstrates improved classification performance in task-oriented communication, suggesting enhanced efficiency and reduced costs.