Semantic Communications With AI Tasks

TL;DR

This paper introduces a semantic communication framework with AI tasks, demonstrating significant bandwidth reduction and accuracy improvements in image classification and defect detection applications.

Contribution

It proposes a novel SC-AIT architecture and implements prototypes for image classification and surface defect detection, showcasing its advantages over traditional methods.

Findings

SC-AIT reduces bandwidth requirements significantly

Achieves over 40% accuracy gains in classification tasks

Demonstrates effectiveness in surface defect detection

Abstract

A radical paradigm shift of wireless networks from ``connected things'' to ``connected intelligence'' undergoes, which coincides with the Shanno and Weaver's envisions: Communications will transform from the technical level to the semantic level. This article proposes a semantic communication method with artificial intelligence tasks (SC-AIT). First, the architecture of SC-AIT is elaborated. Then, based on the proposed architecture, we implement SC-AIT for a image classifications task. A prototype of SC-AIT is also established for surface defect detection, is conducted. Experimental results show that SC-AIT has much lower bandwidth requirements, and can achieve more than $40\%$ classification accuracy gains compared with the communications at the technical level. Future trends and key challenges for semantic communications are also identified.