Semantic Communications Using Foundation Models: Design Approaches and Open Issues
Peiwen Jiang, Chao-Kai Wen, Xinping Yi, Xiao Li, Shi Jin, Jun Zhang
Abstract
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.