Semantic communications, semantic edge computing, and semantic caching

TL;DR

This paper explores the integration of semantic communication with edge computing and caching strategies to improve efficiency and reduce resource consumption in domain-specific applications like the Metaverse.

Contribution

It proposes a semantic caching model in edge computing that stores domain and user-specific models to enhance semantic communication efficiency.

Findings

Reduces time to establish knowledge bases for semantic communication.

Improves accuracy of semantic message interpretation.

Enhances resource efficiency in edge computing environments.

Abstract

The increasing popularity of applications like the Metaverse has led to the exploration of new, more effective ways of communication. Semantic communication, which focuses on the meaning behind transmitted information, represents a departure from traditional communication paradigms. As mobile devices become increasingly prevalent, it is important to explore the potential of edge computing to aid the semantic encoding/decoding process, which requires significant computing power and storage capabilities. However, establishing knowledge bases (KBs) for domain-oriented communication can be time-consuming. To address this challenge, this paper proposes a semantic caching model in edge computing system that caches domain-specialized general models and user-specific individual models. This approach has the potential to reduce the time and resources required to establish individual KBs while accurately capturing the semantics behind users' messages, ultimately leading to more efficient and accessible semantic communication.