The Interplay of AI and Digital Twin: Bridging the Gap between
Data-Driven and Model-Driven Approaches
Abstract
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.