论文标题
AI和数字双胞胎的相互作用：弥合数据驱动和模型驱动方法之间的差距
The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches
论文作者
论文摘要
网络虚拟化和本地人工智能（AI）范式的演变已经概念化了未来无线网络的愿景，即在数字平台上整体运行的全面实体，并与物理领域的智能互动为数字孪晶（DT）概念铺平了道路。新颖的无线技术和用例的出现激发了对DT网络的最新兴趣，这加剧了协调网络并管理其资源的复杂程度。在AI的驱动下，DT的关键原理是为物理实体和网络动力学创建虚拟双胞胎，在该二线中，将利用虚拟双胞胎来生成合成数据，并为AI模型培训提供按需平台。尽管人们普遍认为AI是DT的种子，但我们预计DT和AI将以克服其局限性并相互补充的方式相互支持。在本文中，我们深入研究了DT的基础知识，在该文章中，我们揭示了DT在模型驱动和数据驱动的方法中的作用，并探讨了DT提供的机会以实现6G网络的乐观愿景。我们进一步展现了理论基础在通过AI解锁进一步机会的基础上的重要作用，因此，我们揭示了它们对可靠，高效和低延迟DT的实现的关键影响。
The evolution of network virtualization and native artificial intelligence (AI) paradigms have conceptualized the vision of future wireless networks as a comprehensive entity operating in whole over a digital platform, with smart interaction with the physical domain, paving the way for the blooming of the Digital Twin (DT) concept. The recent interest in the DT networks is fueled by the emergence of novel wireless technologies and use-cases, that exacerbate the level of complexity to orchestrate the network and to manage its resources. Driven by AI, the key principle of the DT is to create a virtual twin for the physical entities and network dynamics, where the virtual twin will be leveraged to generate synthetic data and offer an on-demand platform for AI model training. Despite the common understanding that AI is the seed for DT, we anticipate that the DT and AI will be enablers for each other, in a way that overcome their limitations and complement each other benefits. In this article, we dig into the fundamentals of DT, where we reveal the role of DT in unifying model-driven and data-driven approaches, and explore the opportunities offered by DT in order to achieve the optimistic vision of 6G networks. We further unfold the essential role of the theoretical underpinnings in unlocking further opportunities by AI, and hence, we unveil their pivotal impact on the realization of reliable, efficient, and low-latency DT.
