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.

翻译：在深度学习的赋能下，语义通信标志着从传输原始数据到传递任务相关意义的范式转变，从而实现了更高效、更智能的无线系统。在本研究中，我们探索了一种基于深度学习的任务导向通信框架，该框架联合考虑了分类性能、计算延迟和通信成本。我们在CIFAR-10和CIFAR-100数据集上评估基于ResNets的模型，以模拟无线环境中的现实世界分类任务。我们在模型的不同位置进行划分，以模拟跨无线信道的分割推理。通过改变分割位置和传输的语义特征向量的大小，我们系统地分析了任务准确性与资源效率之间的权衡。实验结果表明，通过适当的模型划分和语义特征压缩，该系统在显著降低计算负载和通信开销的同时，可以保持超过85%的基线准确率。