What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence (2110.00196v1)

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, 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.

A Comprehensive Review of Semantic Communication in Machine Intelligence

The paper "What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence" presents an in-depth examination of semantic communication (SemCom) within the context of modern wireless systems, particularly as they integrate with machine intelligence. SemCom fundamentally expands Claude Shannon's classical information theory by emphasizing the conveyance of meaning rather than just the transmission of data. This exploration into SemCom pivots on the synergy between the Internet-of-Things (IoT) and AI, which highlights a paradigm shift from the traditional focus on rate-centric metrics towards a more nuanced consideration of meaning and effectiveness.

Key Themes of the Paper

The paper delineates three essential communication modalities within SemCom: human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M). The latter two modalities represent significant paradigm shifts prompted by the integration of communication and computing. H2M SemCom focuses on facilitating meaningful interaction between humans and machines, encompassing human-AI symbiosis, recommendation systems, and virtual/augmented reality interfaces. In contrast, M2M SemCom addresses the interconnection of machines to execute computational tasks efficiently, spanning distributed learning, split inference, and distributed consensus.

The authors expound on foundational SemCom principles by introducing the notion of semantic encoding, which includes strategies such as encoding messages using context-specific semantic knowledge and employing end-to-end neural network-based designs. The paper suggests that these methodologies contrast with traditional information theory by prioritizing conveying intended meaning and effectiveness of interaction over sheer data fidelity.

Strong Numerical Results and Key Claims

The authors provide a structured approach to understanding semantic communication principles, with specific design approaches like the layer-coupling and SplitNet approaches for system architecture. While numerical results are predominantly theoretical, the models proposed offer a framework for assessing the impact of semantic layers in communication networks.

The paper stresses the significance of knowledge graphs (KGs) as a tool for enhancing the encoding and transmission process in both human-to-machine and machine-to-machine interactions. This methodology illuminates a path for future research into how KGs can structure meaning in data transmission, offering practical applications spanning virtual assistance and recommendation systems.

Implications and Future Developments

The implications of this research reach into the prospective capabilities of sixth-generation (6G) communication systems, envisioned to encompass connected intelligence and a convergence of sensing, computing, communication, and control. The paper highlights a potential shift wherein the meaning embedded in transmitted data assumes a central role, directly impacting how machines understand and interact with human operators and environments.

The paper engages with anticipated 6G technologies such as distributed artificial intelligence frameworks and adaptive resource management, suggesting that these technologies will further necessitate the integration of semantic understanding. By framing data interactions in terms of semantic interpretations, the future development of AI and communication systems can better handle the demands of immersive applications like virtual reality and remote machine operations.

Conclusion

By addressing semantic and effectiveness problems in Weaver's communication framework through the lens of machine intelligence, the authors provide a visionary perspective on the future of communication systems. The paper sets the stage for evolving communication paradigms that prioritize meaning and effectiveness, over traditional rate and reliability metrics, addressing the growing need for systems that not only transmit data but also comprehend and respond to the inherent meanings these data represent. This represents a pivotal step towards the fusion of communication with intelligent processing, heralding a transformative era in wireless technology and machine interaction.