Deep Semantic Inference over the Air: An Efficient Task-Oriented Communication System
This work addresses efficiency challenges in wireless communication systems for real-world classification tasks, but it is incremental as it builds on existing ResNets and semantic communication concepts.
The paper tackles the problem of balancing classification accuracy, computational latency, and communication cost in a deep learning-based task-oriented semantic communication system, achieving over 85% of baseline accuracy on CIFAR datasets while reducing resource usage.
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.