Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond
This vision addresses the need for enhanced wireless communication and sensing in future 6G systems, but it is incremental as it builds on existing technologies like 3D maps and ray-tracing.
The paper tackles the challenge of creating real-time digital twins for wireless environments by leveraging multi-modal sensing and machine learning, proposing a vision for their use in communication and sensing decisions in 6G networks, with a research platform presented for investigation.
This article presents a vision where \textit{real-time} digital twins of the physical wireless environments are continuously updated using multi-modal sensing data from the distributed infrastructure and user devices, and are used to make communication and sensing decisions. This vision is mainly enabled by the advances in precise 3D maps, multi-modal sensing, ray-tracing computations, and machine/deep learning. This article details this vision, explains the different approaches for constructing and utilizing these real-time digital twins, discusses the applications and open problems, and presents a research platform that can be used to investigate various digital twin research directions.