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Scene Understanding Enabled Semantic Communication with Open Channel Coding by Xiang, Zhe; Yu, Fei; Deng, Quan; Li, Yuandi; Wan, Zhiguo is a scholarly article available to read on EtoBox.

What is Scene Understanding Enabled Semantic Communication with Open Channel Coding about?

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 appr

Author

Xiang, Zhe; Yu, Fei; Deng, Quan; Li, Yuandi; Wan, Zhiguo

Published

2025

Language

EN