智能超表面赋能语义通信系统研究综述

Objective The proliferation of sixth-Generation (6G) wireless network technologies has led to an exponential demand for intelligent devices, such as autonomous transportation, environmental monitoring, and consumer robotics. These applications will generate vast amounts of data, reaching zetta-bytes in scale. Furthermore, they require support for massive connectivity over limited spectrum resources, and low latency, presenting critical challenges to traditional source-channel coding methods. Therefore, the 6G architecture is shifting from a traditional framework focused on high transmission rates to a novel paradigm centered on the intelligent interconnection of all things. Semantic Communication (SemCom) is considered an extension of the Shannon communication paradigm, aiming to extract the meaning from data and filter out unnecessary, irrelevant, or unessential information. As a core paradigm in 6G, SemCom enhances transmission accuracy and spectral efficiency, optimizing service quality. Despite its significant potential, challenges remain in implementing SemCom systems. Reconfigurable Intelligent Surfaces (RIS) are seen as key enablers for 6G networks. RIS can be dynamically deployed in wireless environments to manipulate electromagnetic wave characteristics (such as frequency, phase, and polarization) via programmable reflection and refraction, reshaping wireless channels to amplify signal strength, extend coverage, and optimize performance. Integrating RIS into SemCom systems helps address limitations like coverage voids while enhancing the precision and efficiency of semantic information delivery. This paper proposes an RIS-enabled SemCom framework, with numerical simulations validating its effectiveness in improving system accuracy and robustness. Methods This paper integrates RIS into the SemCom system. The transmitted signal reaches the receiver through both the direct link and the RIS-reflected link, mitigating communication interruptions caused by obstructions. Additionally, the Bilingual Evaluation Understudy (BLEU) metric is used to evaluate performance. Simulations compare RIS-enhanced channels with conventional channels (e.g., AWGN and Rayleigh), demonstrating the performance gain of RIS in SemCom systems. Results and Discussions A positive correlation is observed between Signal-To-Noise Ratio (SNR) increases and improvements in the BLEU score, where higher BLEU scores indicate better text reconstruction fidelity to the source content, reflecting enhanced semantic accuracy and communication quality (Fig. 4). Under RIS-enhanced channel conditions, SemCom systems not only show higher BLEU scores but also exhibit greater stability, with reduced sensitivity to SNR fluctuations. This validates the advantages of RIS channels in semantic information recovery. The performance gap between RIS and conventional channels widens significantly under low SNR conditions, suggesting that RIS-enabled systems maintain robust communication quality and semantic fidelity even with signal degradation, highlighting their stronger practical competitiveness. Additionally, the comparative analysis shows performance differences across N-gram models (Figs. 4(a) and (b)). Practical implementations, therefore, require model selection based on computational constraints and task requirements, with potential for exploring higher-order N-gram architectures. Conclusions This paper systematically examines the evolution of SemCom and the theoretical foundations of RIS. SemCom, aimed at overcoming the bandwidth limitations of traditional systems and enabling natural human-machine interactions, has shown transformative potential across various domains. At the same time, the paper highlights RIS’s advantages in improving wireless system performance and its potential integration with SemCom paradigms. A novel RIS-enabled SemCom architecture is proposed, with experimental validation confirming its effectiveness in enhancing information recovery accuracy. Additionally, the paper outlines future research directions for RIS-enhanced SemCom, urging the research community to address emerging challenges. Prospects Current research on RIS-enabled SemCom is still in its early stages, primarily focusing on resource allocation, performance enhancement, and architectural design. However, it faces fundamental challenges, such as the lack of Shannon-like theoretical foundations and vulnerabilities in knowledge base synchronization and updating. Three critical challenges emerge: (1) Cross-modal semantic fusion architecture, which requires adaptive frameworks to support diverse 6G services beyond single-modality paradigms; (2) Dynamic knowledge base optimization, requiring efficient update mechanisms to balance semantic consistency with computational and communication overhead; (3) Semantic-aware security protocols, which must incorporate hybrid defenses against AI-specific attacks (e.g., adversarial perturbations) and RIS-enabled channel manipulation threats.