统一语义通信与语义边缘计算，推动6G实时智能发展
Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges
- 融合语义通信与语义边缘计算，构建6G智能新范式
- 通过语义编码降低传输开销，提升通信效率与鲁棒性
- 为研究者提供跨领域技术对比与挑战分析，适合6G系统设计者
语义边缘计算（SEC）和语义通信（SemComs）被视为实现第六代（6G）无线网络中实时边缘智能的可行方案。一方面，语义通信利用深度神经网络（DNN）仅编码并传输语义信息，通过补偿无线信道畸变增强鲁棒性，从而提升通信效率；另一方面，语义边缘计算借助分布式DNN，根据设备的计算与网络约束将DNN计算任务分布于多设备间。尽管两领域已取得显著进展，但现有文献缺乏对二者的系统性整合。本文填补该空白，首次统一整合SEC与SemCom，总结其核心研究问题，全面回顾最新技术进展，重点分析各自技术优势与挑战。
原文摘要 · Abstract (English)
Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth-generation (6G) wireless networks. On one hand, SemCom leverages the strength of Deep Neural Networks (DNNs) to encode and communicate the semantic information only, while making it robust to channel distortions by compensating for wireless effects. Ultimately, this leads to an improvement in the communication efficiency. On the other hand, SEC has leveraged distributed DNNs to divide the computation of a DNN across different devices based on their computational and networking constraints. Although significant progress has been made in both fields, the literature lacks a systematic view to connect both fields. In this work, we fulfill the current gap by unifying the SEC and SemCom fields. We summarize the research problems in these two fields and provide a comprehensive review of the state of the art with a focus on their technical strengths and challenges.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。