Foundation models (FMs), including large language models, have become increasingly popular due to their wide-ranging applicability and ability to understand human-like semantics. While previous research has explored the use of FMs in semantic communications to improve semantic extraction and reconstruction, the impact of these models on different system layers, considering computation and memory complexity, requires further analysis. This study focuses on integrating FMs at the task application, semantic coding, and physical transmission layers, using universal knowledge to profoundly transform system design. Additionally, it examines the use of compact models to balance performance and complexity, comparing three separate approaches that employ FMs. Ultimately, the study highlights unresolved issues in the fields that need addressing.

中文翻译：

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使用基础模型的语义通信：设计方法和开放问题


基础模型（FM），包括大型语言模型，由于其广泛的适用性和理解类人语义的能力而变得越来越流行。虽然之前的研究已经探索了在语义通信中使用 FM 来改进语义提取和重建，但考虑到计算和内存复杂性，这些模型对不同系统层的影响需要进一步分析。本研究的重点是在任务应用、语义编码和物理传输层集成FM，利用通用知识深刻改变系统设计。此外，它还研究了使用紧凑模型来平衡性能和复杂性，比较了使用 FM 的三种不同方法。最终，该研究强调了需要解决的领域中尚未解决的问题。