场景理解赋能的语义通信与开放信道编码
Scene Understanding Enabled Semantic Communication with Open Channel Coding
AI总结：
提出OpenSC系统，结合场景理解、大语言模型和开放信道编码，利用场景图结构化语义编码，实现自适应、可泛化的语义通信，提升VQA任务性能与传输效率。
摘要：
随着通信系统从符号传输向传递有意义的信息转变，第六代（6G）网络强调语义通信。这种方法优先考虑高层语义信息，提高鲁棒性，并减少文本、语音和图像等模态中的冗余。然而，传统语义通信面临局限性，包括静态编码策略、泛化能力差以及对阻碍适应性的任务特定知识库的依赖。为克服这些挑战，我们提出了一种结合场景理解、大语言模型（LLMs）和开放信道编码的新型系统，命名为OpenSC。传统系统依赖固定的领域特定知识库，限制了其泛化能力。我们的开放信道编码方法利用共享的、公开可用的知识，实现灵活、自适应的编码。这种动态系统减少了对静态任务特定数据的依赖，增强了在不同任务和环境中的适应性。此外，我们使用场景图进行结构化语义编码，捕获对象关系和上下文，以改进视觉问答（VQA）等任务。我们的方法选择性地编码关键语义元素，最小化冗余并提高传输效率。实验结果表明，在语义理解和效率方面均有显著提升，推进了6G网络中自适应、可泛化语义通信的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
