Knowledge Base Enabled Semantic Communication: A Generative Perspective
Semantic communication is widely touted as a key technology for propelling 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 a 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.
Authors
- Jie Xu (ORCID: https://orcid.org/0000-0002-4854-8839)
- Shuguang Cui (ORCID: https://orcid.org/0000-0003-2608-775X)
- Jinke Ren (ORCID: https://orcid.org/0000-0002-7409-6127)
- Guanying Chen (ORCID: https://orcid.org/0000-0002-4789-1176)
- Ping Zhang (ORCID: https://orcid.org/0000-0003-0820-2056)
- Ya‐Ping Sun (ORCID: https://orcid.org/0000-0003-0633-6253)
- Zezhong Zhang (ORCID: https://orcid.org/0000-0002-6097-9340)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
- Peng Cheng Laboratory (CN)
Publication Details
- Journal
- IEEE Wireless Communications
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1109/mwc.001.2300553
- Citations
- 34
- Primary Topic
- Wireless Signal Modulation Classification
- Type
- article
- Field-Weighted Citation Impact
- 11.81
Funders
- National Natural Science Foundation of China
- Shenzhen Science and Technology Innovation Program