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

Abstract

With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemComempowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AIenabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semanticaware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction.

Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks

“…Beyond the scope of Shannon's theory, a tightly coupled synchronization between sender and receiver is essential for successfully retrieving semantics at both ends. In contrast to the recent developments [2], [7], [13]- [17], detailed later in Sec. I-B and II-A, our objective is to develop an accurate semantic extraction approach for SC to facilitate joint optimization (information gathering, information dissemination, and decision-making) over 6G networks.…”

“…Conventional communication systems' actual transmission rates are getting close to the Shannon limit, and the remaining spectrum resources are being increasingly scarce [7], [10]. The conversion of the transmission-before-understanding communication paradigm to the understanding-before-transmission paradigm will be the key to SC's success in addressing bandwidth bottlenecks and spectrum scarcity.…”

“…This is refer to as Understand-Before-Talk (UBT) in this paper. By doing this, semantic extraction can be included into the communication model to create SC for 6G [11], which only permits the transmission of information that is relevant to the recipient and hides or reduces redundant data, relieving bandwidth pressure and improving privacy protection [7], [12]. However, accuracy of semantic extraction has been highly challenging and nontrivial task in SC engineering.…”

“…However, it also increases training complexity [28]. Training such a large model from start for a high-dimensional job becomes a significantly challenging task [7]. Furthermore, semantic information will be distinct for different tasks and therefore deep and reinforcement learning based methods are not applicable for multitask scenarios (may introduce irrelevant information and needs to be retrained, compromising system performance and resources) [31]- [33].…”

“…In 6G networks, inheriting these excellent human brain mechanisms given by nature, should include semanticization and effectiveness in communications as the fundamental components for imminent innovations [7]. As shown in Fig.…”