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.

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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

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article

sDAC—Semantic Digital Analog Converter for Semantic Communications

Xiaoyi Liu, Cheng Guo, Haotai Liang, Zhicheng Bao et al.
5 citations
IEEE Transactions on Communications
Neural Networks and Applications
12.40
article

sDAC—Semantic Digital Analog Converter for Semantic Communications

Xiaoyi Liu, Cheng Guo, Haotai Liang, Zhicheng Bao, Xiaodong Xu, Hao Chen, Ping Zhang, Chen Dong, Cong Li
article en
5 citations

Abstract

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.

IEEE Transactions on CommunicationsVol. 73(11)
Beijing University of Posts and Telecommunications (CN), China Academy of Space Technology (CN), Peng Cheng Laboratory (CN)
National Natural Science Foundation of China, National Key Laboratory Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 2%
Neural Networks and Applications
12.40
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