标题： 什么是语义通信？ 机器智能时代传达意义的视角 显示英文标题

标题： What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence

摘要： 在20世纪40年代，克劳德·香农发展了信息论，专注于量化通信信道能够支持的最大数据速率。 在这一理论的指导下，直到5G为止，无线系统设计的主要主题是最大化数据速率。 在他的理论中，信息的语义方面和意义被视作与通信 largely 不相关。 经典理论在机器智能的现代时代开始显现出其局限性，这由物联网和人工智能之间的协同作用组成。 通过拓宽经典框架的范围，本文提出了一种语义通信（SemCom）的观点，并通过通信系统传递意义。 我们讨论了三种通信模式，人与人（H2H）、人与机器（H2M）和机器与机器（M2M）通信。 后两种是本文的主要主题，代表了通信和计算范式的转变。 H2M SemCom 指的是用于传递人类和机器都能理解的意义的语义技术，以便它们可以进行交互。 M2M SemCom 指的是用于有效连接机器的高效技术，以便它们可以在无线网络中有效地执行特定的计算任务。 文章的第一部分介绍了SemCom原则，包括编码、系统架构、层耦合以及端到端设计方法。 第二部分重点介绍了H2M（人与人工智能共生、推荐等）和M2M SemCom（分布式学习、分割推理等）应用领域的具体技术。 最后，我们讨论了用于设计SemCom系统的知识图谱方法。 我们认为，这一全面的介绍将为SemCom这一新兴领域提供有用的指导，该领域预计将在具有连接智能和集成感知、计算、通信和控制的6G中发挥重要作用。 显示英文摘要

摘要： In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this, the main theme of wireless system design up until 5G was the data rate maximization. In his theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between IoT and AI. By broadening the scope of the classic framework, in this article we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities, human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two, the main theme of the article, represent the paradigm shift in communication and computing. H2M SemCom refers to semantic techniques for conveying meanings understandable by both humans and machines so that they can interact. M2M SemCom refers to effectiveness techniques for efficiently connecting machines such that they can effectively execute a specific computation task in a wireless network. The first part of the article introduces SemCom principles including encoding, system architecture, and layer-coupling and end-to-end design approaches. The second part focuses on specific techniques for application areas of H2M (human and AI symbiosis, recommendation, etc.) and M2M SemCom (distributed learning, split inference, etc.) Finally, we discuss the knowledge graphs approach for designing SemCom systems. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in 6G featuring connected intelligence and integrated sensing, computing, communication, and control.