With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on a 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 the meaning of information into 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 Internet. In this paper, we first highlight the motivations and compelling reasons for SemCom in 6G. Then, we provide an overview of SemCom-related theory development. After that, we introduce three types of SemCom, i.e., semantic-oriented communication, goal-oriented communication, and semantic-aware communication. Following that, we organize the design of the communication system into three dimensions, i.e., semantic information (SI) extraction, SI transmission, and SI metrics. For each dimension, we review existing techniques and discuss their benefits and limitations, as well as the remaining challenges. Then, we introduce the potential applications of SemCom in 6G and portray the vision of future SemCom-empowered network architecture. Finally, we outline future research opportunities. In a nutshell, this paper provides a holistic review of the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues with insights for further in-depth investigations.

Metaverse enables users to experience a virtual space that resembles the real world. In the Metaverse, virtual service providers (VSPs) maintain digital twins (DTs) of real-world entities to offer innovative services such as virtual conferences and tourism. However, to provide a realistic experience, VSPs need to collect up-to-date and relevant data, which is latency-sensitive and resource-intensive, from edge devices such as unmanned aerial vehicles (UAVs). To address this challenge, we propose a semantic communication framework that leverages extraction and trading of scene graphs to reduce communication costs and motivate data transfer for Metaverse services. In the framework, we propose a semantic communication system where data for building DTs is encoded as semantic information, such as scene graphs. VSPs can use these graphs to build or update their DTs. To facilitate data trading between VSPs and edge nodes, we introduce a combinatorial auction-based incentive mechanism where we model the valuation of semantic data based on relatedness, freshness, and semantic values. We measure the relatedness based on the similarity between targeted objects of VSPs and the objects present in the real-world data. We use the age of information (AoI) to quantify data freshness and evaluate the semantic value using the information richness of the scene graphs. Simulation results show that our system can maintain the model's mean recall performance using only 12.5% of features during transmission. Our mechanism increases the average update frequency effectively. The theoretical analysis proves that the proposed mechanism guarantees the desired properties of individual rationality and incentive compatibility.

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meanings of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called “semantic model trading”. Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called “semantic information trading”. To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them to achieve higher utilities than the baseline method.