Abstract
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 DTNs requires tackling many design aspects including data, models, and interfaces. In this article, we provide an overview of 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.
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