Semantic Communications using Foundation Models: Design Approaches and Open Issues

TL;DR

This paper explores how foundation models can be integrated into semantic communication systems at various levels, analyzing their benefits, challenges, and open issues related to complexity and system design.

Contribution

It provides a comprehensive analysis of integrating foundation models at different system levels and compares approaches using compact models to balance performance and complexity.

Findings

Foundation models can significantly enhance semantic extraction and reconstruction.

Different integration approaches offer trade-offs between performance and complexity.

Open issues include managing computation and memory demands in practical systems.

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 levels, considering computation and memory complexity, requires further analysis. This study focuses on integrating FMs at the effectiveness, semantic, and physical levels, 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 field that need addressing.