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<p>Semantic communication is the key to enable 6G communication, from sending raw data to conveying meaningful information. In contrast, current models fail to process multimodal information in a structured, context-aware manner. To tackle this problem, the proposed Multi-modal Knowledge-Graph Guided Graph Transformer (MKG-GT) combines text and image modalities into a unified heterogeneous graph equipped with a prior knowledge graph. It employs a dual-stage fusion process that captures semantics and structural relationships with Graph Attention Networks (GAN) and Graph Kernel Attention Transformers (GKAT), respectively, and a lightweight semantic compression unit to encode the representation for efficient 6G transmission. Experiments on Flickr30K achieve 0.95 accuracy, 0.94 F1, and 0.97 macro F1, while improving the performance of the current baselines. The model’s semantic pipeline inference time is 1.05ms, supporting low-latency, real-time 6G applications with pre-computed features. Overall, the results show that graph reasoning, knowledge augmentation and compact semantic encoding are a strong method for next-generation multimodal communication.</p>

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Graph Transformers-Enhanced Semantic Encoding for Context-Aware 6g Communications

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • T. Balamurugan,
  • G. Dhevanandhini,
  • R. Durga

摘要

Semantic communication is the key to enable 6G communication, from sending raw data to conveying meaningful information. In contrast, current models fail to process multimodal information in a structured, context-aware manner. To tackle this problem, the proposed Multi-modal Knowledge-Graph Guided Graph Transformer (MKG-GT) combines text and image modalities into a unified heterogeneous graph equipped with a prior knowledge graph. It employs a dual-stage fusion process that captures semantics and structural relationships with Graph Attention Networks (GAN) and Graph Kernel Attention Transformers (GKAT), respectively, and a lightweight semantic compression unit to encode the representation for efficient 6G transmission. Experiments on Flickr30K achieve 0.95 accuracy, 0.94 F1, and 0.97 macro F1, while improving the performance of the current baselines. The model’s semantic pipeline inference time is 1.05ms, supporting low-latency, real-time 6G applications with pre-computed features. Overall, the results show that graph reasoning, knowledge augmentation and compact semantic encoding are a strong method for next-generation multimodal communication.