Scene Understanding Enabled Semantic Communication with Open Channel Coding

• URL: http://arxiv.org/abs/2501.14520v1
• Date: Fri, 24 Jan 2025 14:23:31 GMT
• Title: Scene Understanding Enabled Semantic Communication with Open Channel Coding
• Authors: Zhe Xiang, Fei Yu, Quan Deng, Yuandi Li, Zhiguo Wan,
• Abstract summary: Traditional semantic communication faces limitations, including static coding strategies, poor generalization, and reliance on task-specific knowledge bases that hinder adaptability. We propose a novel system combining scene understanding, Large Language Models (LLMs), and open channel coding, named textbfOpenSC. Experimental results show significant improvements in both semantic understanding and efficiency, advancing the potential of adaptive, generalizable semantic communication in 6G networks.
• Score: 3.255136948268739
• License:
• Abstract: As communication systems transition from symbol transmission to conveying meaningful information, sixth-generation (6G) networks emphasize semantic communication. This approach prioritizes high-level semantic information, improving robustness and reducing redundancy across modalities like text, speech, and images. However, traditional semantic communication faces limitations, including static coding strategies, poor generalization, and reliance on task-specific knowledge bases that hinder adaptability. To overcome these challenges, we propose a novel system combining scene understanding, Large Language Models (LLMs), and open channel coding, named \textbf{OpenSC}. Traditional systems rely on fixed domain-specific knowledge bases, limiting their ability to generalize. Our open channel coding approach leverages shared, publicly available knowledge, enabling flexible, adaptive encoding. This dynamic system reduces reliance on static task-specific data, enhancing adaptability across diverse tasks and environments. Additionally, we use scene graphs for structured semantic encoding, capturing object relationships and context to improve tasks like Visual Question Answering (VQA). Our approach selectively encodes key semantic elements, minimizing redundancy and improving transmission efficiency. Experimental results show significant improvements in both semantic understanding and efficiency, advancing the potential of adaptive, generalizable semantic communication in 6G networks.

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