sDAC—Semantic Digital Analog Converter for Semantic Communications
In this paper, we propose a novel semantic digital analog converter (sDAC) for the compatibility between semantic and digital communications. Most of the current semantic communication systems rely primarily on analog modulation, limiting their integration with digital communication systems, which are more common in practice. In fact, traditional quantization methods are unsuitable for semantic communication because they do not account for semantic information within symbols. These factors block the wide application of the semantic communication. To address these challenges, sDAC is proposed. It is a simple yet efficient and generative module used to realize digital and analog bi-directional conversion. The entire process is independent of any specific semantic model, modulation methods, or channel conditions. In the experiment section, the performance of sDAC is tested across different semantic models, semantic tasks, modulation methods, channel conditions and quantization orders. Test results show that the proposed sDAC has great generative properties and channel robustness.
Authors
- Xiaoyi Liu (ORCID: https://orcid.org/0000-0002-1760-4375)
- Cheng Guo (ORCID: https://orcid.org/0000-0002-2164-1072)
- Haotai Liang (ORCID: https://orcid.org/0000-0003-2733-4353)
- Zhicheng Bao (ORCID: https://orcid.org/0000-0002-2605-2007)
- Xiaodong Xu (ORCID: https://orcid.org/0000-0003-4245-5989)
- Hao Chen (ORCID: https://orcid.org/0000-0002-0973-1374)
- Ping Zhang (ORCID: https://orcid.org/0000-0002-0269-104X)
- Chen Dong (ORCID: https://orcid.org/0000-0002-3443-1453)
- Cong Li (ORCID: https://orcid.org/0000-0003-4420-8623)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- China Academy of Space Technology (CN)
- Peng Cheng Laboratory (CN)
Publication Details
- Journal
- IEEE Transactions on Communications
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1109/tcomm.2025.3566976
- Citations
- 5
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- National Natural Science Foundation of China
- National Key Laboratory Foundation of China
- National Key Research and Development Program of China