Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model provides new solutions to overcome above issues. Here, we propose a large AI model-based SC framework (LAM-SC) specifically designed for image data, where we first design the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semantic-aware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the significance of the large AI model-based KB development in future SC paradigms.

翻译：语义通信（SC）是一种新兴的智能范式，为元宇宙、混合现实及万物互联等未来应用提供了解决方案。然而，当前语义通信系统的知识库构建面临知识表示有限、知识更新频繁以及知识共享不安全等问题。幸运的是，大型AI模型的发展为克服上述难题提供了新路径。本文提出了一种专门针对图像数据的大型AI模型语义通信框架（LAM-SC）。我们首先设计了基于分割一切模型（SAM）的知识库（SKB），该知识库可通过通用语义知识将原始图像分割为不同语义片段；随后提出了基于注意力的语义集成方法（ASI），无需人工干预即可对SKB生成的语义片段进行加权，并将其整合为语义感知图像；此外，还设计了自适应语义压缩编码（ASC）以去除语义特征中的冗余信息，从而降低通信开销。最后，通过仿真验证了LAM-SC框架的有效性，并论证了基于大型AI模型的知识库开发对未来语义通信范式的重要意义。