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

Abstract

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 fundamental work, the main theme of wireless system design up until the fifth generation (5G) was the data rate maximization. In Shannon's 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 Internet-of-Things (IoT) and artificial intelligence (AI).By broadening the scope of the classic communication-theoretic 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 represent the paradigm shift in communication and computing, and define the main theme of this article. H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and "dialogue". On the other hand, M2M SemCom refers to effectiveness techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network. The first part of this article focuses on introducing the SemCom principles including encoding, layered system architecture, and two design approaches: (1) layer-coupling design; and (2) end-to-end design using a neural network. The second part focuses on discussion of specific techniques for different application areas of H2M SemCom (including human and AI symbiosis, recommendation, human sensing and care, and virtual reality (VR)/augmented reality (AR)) and M2M SemCom (including distributed learning, split inference, distributed consensus, and machine-vision cameras). Finally, we discuss the approach for designing SemCom systems based on knowledge graphs. 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 sixth generation (6G) featuring connected intelligence and integrated sensing, computing, communication, and control.

Reliable Semantic Communication System Enabled by Knowledge Graph

“…Based on the literature [ 20 ], Basu et al (2012) provided a detailed explanation of the relationship between semantic entropy and information entropy, and they defined the concepts of semantic ambiguity and semantic redundancy [ 21 ]. In [ 22 ], Lan et al (2021) proposed that semantic communication can be divided into human-to-human, human-to-machine, and machine-to-machine sub-areas, which broadened the scope of semantic communication.…”

Semantic Communications: Principles and Challenges

“…These Similarly, Zhang et al [20] proposed a new semantic system model with different layers as a Fig. 8: The novel layered architecture of semantic communication system [18].…”

“…Strinati et al [17] indicated the role of semantic communications in 6G. Lan et al [18] classified semantic communications into human-to-human (Level 2), human-to-machine (Level 2 and Level 3), and machine-tomachine (Level 3) communications. Various potential applications of semantic communications are presented.…”

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

“…We present a comprehensive analysis of current technical papers that apply SemCom in the latest 6G networks, and detail the features of SemCom and 6G. [6] Introduce the SemCom principles and two design approaches, i.e., layer-coupling design and end-to-end design using a neural network. Discuss specific techniques for different application areas of SemCom.…”