Semantic Communications With AI Tasks
This work addresses bandwidth efficiency for AI-driven applications in wireless networks, representing an incremental advancement in semantic communication methods.
The paper tackles the problem of high bandwidth requirements in wireless networks by proposing a semantic communication method for AI tasks, achieving over 40% classification accuracy gains compared to traditional technical-level communications.
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.