Knowledge Base Enabled Semantic Communication: A Generative Perspective
This work addresses the problem of inefficient semantic communication for 6G wireless networks, though it appears incremental as it builds on existing semantic communication concepts.
The paper tackles the challenge of effective semantic representation in communication by proposing a generative semantic communication architecture using semantic knowledge bases (KB), which enhances communication efficiency by characterizing messages in low-dimensional subspaces without losing meaning, as demonstrated in a case study showing superiority over conventional methods.
Semantic communication is widely touted as a key technology for propelling the sixth-generation (6G) wireless networks. However, providing effective semantic representation is quite challenging in practice. To address this issue, this article takes a crack at exploiting semantic knowledge base (KB) to usher in a new era of generative semantic communication. Via semantic KB, source messages can be characterized in low-dimensional subspaces without compromising their desired meanings, thus significantly enhancing the communication efficiency. The fundamental principle of semantic KB is first introduced, and a generative semantic communication architecture is developed by presenting three sub-KBs, namely source, task, and channel KBs. Then, the detailed construction approaches for each sub-KB are described, followed by their utilization in terms of semantic coding and transmission. A case study is also provided to showcase the superiority of generative semantic communication over conventional syntactic communication and classical semantic communication. In a nutshell, this article establishes a scientific foundation for the exciting uncharted frontier of generative semantic communication.