  Digital twin networks (DTNs) are real-time replicas of physical networks.
They are emerging as a powerful technology for design, diagnosis, simulation,
what-if-analysis, and artificial intelligence (AI)/machine learning (ML) driven
real-time optimization and control of the sixth-generation (6G) wireless
networks. Despite the great potential of what digital twins can offer for 6G,
realizing the desired capabilities of 6G DTNs requires tackling many design
aspects including data, models, and interfaces. In this article, we provide an
overview of 6G DTNs by presenting prominent use cases and their service
requirements, describing a reference architecture, and discussing fundamental
design aspects. We also present a real-world example to illustrate how DTNs can
be built upon and operated in a real-time reference development platform -
Omniverse.
