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

TL;DR

This paper presents a deep learning-based task-oriented communication system that optimizes semantic feature transmission for wireless classification tasks, balancing accuracy, latency, and communication costs.

Contribution

It introduces a joint framework considering classification, latency, and communication, with systematic analysis of model partitioning and semantic compression in wireless environments.

Findings

Retains over 85% accuracy with reduced resource use.

Systematic analysis of split inference trade-offs.

Effective semantic feature compression reduces communication overhead.

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 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.