用深度学习实现无线通信中的语义高效传输，兼顾精度与效率。
Deep Semantic Inference over the Air: An Efficient Task-Oriented Communication System
- 模型分层传输，只发任务相关语义特征，减少通信量。
- 在CIFAR数据集上保持85%以上原始精度，大幅降低计算与通信开销。
- 适合边缘计算、物联网等资源受限的实时识别场景。
得益于深度学习，语义通信实现了从传输原始数据到传递任务相关语义的范式转变，使无线系统更高效智能。本文提出一种基于深度学习的任务导向通信框架，综合考虑分类性能、计算延迟和通信成本。在CIFAR-10和CIFAR-100数据集上评估基于ResNet的模型，模拟无线环境中的真实分类任务。通过在不同位置分割模型，模拟跨无线信道的分布式推理。改变分割点与传输语义特征向量大小，系统分析任务准确率与资源效率的权衡。实验表明，通过合理的模型分割与语义特征压缩，系统可在保留超过85%基线准确率的同时，显著降低计算负载与通信开销。
原文摘要 · Abstract (English)
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.
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