Semantic Communications: Principles and Challenges

TL;DR

Semantic communication aims to transmit meaning rather than symbols, leveraging deep learning for system design, and presents new performance metrics beyond traditional error rates.

Contribution

The paper provides a comprehensive overview of semantic communication principles, frameworks, and system design, highlighting the role of deep learning and new performance metrics.

Findings

Semantic communication focuses on meaning transmission instead of symbol accuracy.

Deep learning enables advanced semantic communication system design.

New performance metrics are proposed for evaluating semantic communication effectiveness.

Abstract

Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of each single symbol or bit regardless of its meaning. This article provides an overview on semantic communications. After a brief review of Shannon information theory, we discuss semantic communications with theory, framework, and system design enabled by deep learning. Different from the symbol/bit error rate used for measuring conventional communication systems, performance metrics for semantic communications are also discussed. The article concludes with several open questions in semantic communications.