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.

翻译：网络虚拟化和原生人工智能（AI）范式的演进，将未来无线网络的愿景概念化为一个在数字平台上整体运行的综合实体，通过与物理领域的智能交互，为数字孪生（DT）概念的蓬勃发展铺平了道路。近期的DT网络兴趣是由新型无线技术和用例的出现所驱动的，这些技术和用例加剧了网络编排和资源管理的复杂程度。在AI的驱动下，DT的核心原则是为物理实体和网络动态创建一个虚拟孪生，该虚拟孪生将用于生成合成数据，并为AI模型训练提供按需平台。尽管普遍认为AI是DT的种子，但我们预期DT和AI将相互赋能，以克服彼此的局限性并互补优势。本文深入探讨DT的基础原理，揭示了DT在统一模型驱动和数据驱动方法中的作用，并探索了DT为实现6G网络乐观愿景所提供的机遇。我们进一步阐明了理论基石在通过AI解锁更多机遇中的关键作用，从而揭示了它们对实现可靠、高效、低延迟DT的重要影响。