面向6G边缘智能的语义数据源
Semantic Data Sourcing for 6G Edge Intelligence
摘要：
作为6G网络的一项新功能，边缘智能是指在网络边缘无处不在地部署机器学习和人工智能（AI）算法，为从感知到自动驾驶等众多新兴应用赋能。为了支持相关的用例，包括传感、边缘学习和边缘推理，所有这些都需要通过空中传输高维数据或AI模型。为了克服这个瓶颈，我们提出了一个新的框架SEMantic数据源（SEMDAS）定位语义匹配的数据源，有效地使边缘智能操作。综合框架包括新的体系结构，协议，语义匹配技术，面向任务的无线技术的设计原则。作为SEMDAS的关键组成部分，我们讨论了一套基于机器学习的语义匹配技术，针对不同的边缘智能用例。此外，为设计面向任务的无线技术，我们讨论了不同的权衡SEMDAS系统，提出了新的概念，联合语义和信道匹配，并指出了一些研究机会。SEMDAS框架不仅克服了上述通信瓶颈，而且还解决了其他网络问题，包括长距离传输，稀疏连接，高速移动性，链路中断和安全性。此外，使用一个真实的数据集的实验结果来证明SEMDAS的性能增益。
英文摘要：
As a new function of 6G networks, edge intelligence refers to the ubiquitous deployment of machine learning and artificial intelligence (AI) algorithms at the network edge to empower many emerging applications ranging from sensing to auto-pilot. To support relevant use cases, including sensing, edge learning, and edge inference, all require transmission of high-dimensional data or AI models over the air. To overcome the bottleneck, we propose a novel framework of SEMantic DAta Sourcing (SEMDAS) for locating semantically matched data sources to efficiently enable edge-intelligence operations. The comprehensive framework comprises new architecture, protocol, semantic matching techniques, and design principles for task-oriented wireless techniques. As the key component of SEMDAS, we discuss a set of machine learning based semantic matching techniques targeting different edge-intelligence use cases. Moreover, for designing task-oriented wireless techniques, we discuss different tradeoffs in SEMDAS systems, propose the new concept of joint semantics-and-channel matching, and point to a number of research opportunities. The SEMDAS framework not only overcomes the said communication bottleneck but also addresses other networking issues including long-distance transmission, sparse connectivity, high-speed mobility, link disruptions, and security. In addition, experimental results using a real dataset are presented to demonstrate the performance gain of SEMDAS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
