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Towards Effective and Interpretable Semantic Communications

Youlong Wu, Yuanmin Shi, Shuai Ma, Chunxiao Jiang, Wei Zhang, Khaled B. Letaief

TL;DR

This work argues that traditional Shannon-based communication inadequately addresses modern, goal-driven tasks in ultra-low-latency 6G contexts. It introduces semantic-focused metrics—$GSE$, $ESE$, semantic distortion, and semantic communication rate—and lays out an end-to-end design guideline that combines information-theoretic losses with variational training to produce interpretable, effective semantic communications. The paper presents two concrete implementations: DT-RIB, which uses discrete semantic messages with digital modulation to improve robustness and compatibility, and CLUB-based JSCC, which integrates privacy constraints into the semantic encoding process. By bridging information theory and machine learning, the work provides a foundation for low-latency, task-oriented communications and highlights avenues for future theory (e.g., optimal semantic source/channel coding) and applications in 6G ecosystems.

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.

Towards Effective and Interpretable Semantic Communications

TL;DR

This work argues that traditional Shannon-based communication inadequately addresses modern, goal-driven tasks in ultra-low-latency 6G contexts. It introduces semantic-focused metrics—, , semantic distortion, and semantic communication rate—and lays out an end-to-end design guideline that combines information-theoretic losses with variational training to produce interpretable, effective semantic communications. The paper presents two concrete implementations: DT-RIB, which uses discrete semantic messages with digital modulation to improve robustness and compatibility, and CLUB-based JSCC, which integrates privacy constraints into the semantic encoding process. By bridging information theory and machine learning, the work provides a foundation for low-latency, task-oriented communications and highlights avenues for future theory (e.g., optimal semantic source/channel coding) and applications in 6G ecosystems.

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.
Paper Structure (16 sections, 4 figures)

This paper contains 16 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Architecture
  3. Modules
  4. Frameworks
  5. Interpretability: Measures
  6. Semantic Entropy
  7. Ground-truth Semantic Entropy
  8. Empirical Semantic Entropy
  9. Semantic Distortion
  10. Semantic Communication Rate
  11. Effectiveness of Semantic Communications: Implementations
  12. A Design Guideline for Semantic Communication
  13. Robust Information Bottleneck for Semantic Communications
  14. Privacy-preserving Semantic Communications
  15. Conclusion
  16. ...and 1 more sections

Figures (4)

  • Figure 1: An architecture of task-oriented semantic communication systems.
  • Figure 2: The RIB framework for semantic communication with discrete modulations.
  • Figure 3: Performance of the proposed DT-RIB, DeepJSCC Bourtsoulatze2019DeepJS, and VFE Shao2022LearningTC on MNIST and CIFAR-10 dataset with 8dB training peak SNR.
  • Figure 4: The CLUB-based JSCC for privacy-preservative semantic communications.