The advancements of mixed reality services, with 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 the internet-of-sensing and 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, in addition to the received sensed data from the physical twin in an on-demand manner. The available data at the twin will be the foundation for AI models training and intelligent inference process. Despite the common understanding that AI is the seed for DT, we anticipate 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. Finally, we identify the limitations of AI-DT and overview potential future research directions, to open the floor for further exploration in AI for DT and DT for AI.
翻译:随着网络虚拟化和本地人工智能(AI)模式的发展,混合现实服务的进展,随着网络虚拟化和本地人工智能(AI)模式的发展,将未来无线网络的愿景概念化为未来无线网络的愿景,作为一个在数字平台上全面运作的综合实体,与物理域进行智能互动,为数字双机制概念的开张铺平了道路;由于新颖的无线技术和使用案例的出现,使得对DT网络的兴趣更加浓厚,这加剧了协调网络和管理其资源的复杂程度。在互联网高效的概览和AI的驱动下,DT的关键原则是,为实体实体和网络动态创造虚拟的双胞胎,在这个实体和网络的动态上,将利用虚拟双胞胎来生成合成数据,同时以需求方式从物理双对齐概念概念中获取的感知数据。对DT网络的现有数据将成为AI模型培训和智能推导过程的基础。尽管人们普遍认为AI是D的种子,但是我们预计DT和AI将进一步增强对方的能力,从而克服其局限性并补充其他6个模型的效益。 在本文章中,我们挖掘和DTDDD公司的未来研究与DDD的潜能。