Towards Effective and Interpretable Semantic Communications

TL;DR

This paper explores the theoretical foundations and practical guidelines for semantic communication in 6G networks, aiming to reduce latency and overhead by transmitting task-relevant information while ensuring interpretability.

Contribution

It introduces information-theoretic metrics for semantic communication and offers guidelines to bridge the gap between theory and practice, enhancing interpretability and effectiveness.

Findings

Semantic entropy and distortion metrics characterize information flow.

Guidelines improve interpretability and practical implementation.

Bridges the theory-practice gap in semantic communications.

Abstract

With the exponential surge in traffic data and the pressing need for ultra-low latency in emerging intelligence applications, it is envisioned that 6G networks will demand disruptive communication technologies to foster ubiquitous intelligence and succinctness within the human society. Semantic communication, a novel paradigm, holds the promise of significantly curtailing communication overhead and latency by transmitting only task-relevant information. Despite numerous efforts in both theoretical frameworks and practical implementations of semantic communications, a substantial theory-practice gap complicates the theoretical analysis and interpretation, particularly when employing black-box machine learning techniques. This article initially delves into information-theoretic metrics such as semantic entropy, semantic distortions, and semantic communication rate to characterize the information flow in semantic communications. Subsequently, it provides a guideline for implementing semantic communications to ensure both theoretical interpretability and communication effectiveness.