With the exponential surge in traffic data and the pressing need for ultra-low latency in emerging intelligence applications, it is envisioned that 6G 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.

中文翻译：

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迈向有效且可解释的语义通信


随着流量数据的指数级增长以及新兴智能应用对超低延迟的迫切需求，预计6G将需要颠覆性的通信技术来促进人类社会中无处不在的智能和简洁性。语义通信是一种新颖的范例，有望通过仅传输与任务相关的信息来显着减少通信开销和延迟。尽管在语义通信的理论框架和实际实现方面做出了大量努力，但巨大的理论与实践差距使理论分析和解释变得复杂，特别是在采用黑盒机器学习技术时。本文首先深入研究了语义熵、语义扭曲和语义通信速率等信息论指标，以表征语义通信中的信息流。随后，它提供了实施语义通信的指南，以确保理论可解释性和通信有效性。