基于图的多模态语义通信中高效压缩的多级相似度

IF 4.4 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-07-04 DOI:10.1109/LCOMM.2025.3585892

Chenlin Xing;Jie Lv;Tao Luo;Zhilong Zhang

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引用次数: 0

摘要

高效、准确的多模态数据传输仍然是语义通信中的一个关键挑战。现有的研究忽略了多模态数据所固有的多层次语义相似度。在此基础上，提出了一种基于图的多模态语义通信系统。在GMSC中，三联体充分关联了模态内和跨模态的语义相似性，从而实现了冗余语义信息的综合提取。设计了一种面向任务的压缩阈值方法，以去除冗余的传输信息，提高传输效率。仿真结果表明，在相同的压缩阈值下，GMSC的任务精度提高了7%，在相同的任务精度下显著提高了传输效率。

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Multi-Level Similarity for Efficient Compression in Graph-Based Multi-Modal Semantic Communication

Efficient and accurate multi-modal data transmission remains a key challenge in semantic communications. Existing research has overlooked the multi-level semantic similarity inherent in multi-modal data. Motivated by this, a graph-based multi-modal semantic communication system (GMSC) is proposed. In GMSC, triplets fully correlate intra-modal and cross-modal semantic similarities, enabling the integrated extraction of redundant semantic information. A task-oriented compression threshold method is designed to remove redundant transmission information and enhance efficiency. Simulation results demonstrate that GMSC achieves a 7% improvement in task accuracy at the same compression threshold and significantly improves transmission efficiency under the same task accuracy.

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.