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.

翻译：本文提出了一种愿景，即利用分布式基础设施和用户设备的多模态传感数据，持续更新物理无线环境的实时数字孪生，并基于此做出通信与感知决策。该愿景主要依托精确三维地图、多模态传感、射线追踪计算以及机器/深度学习等技术的进步得以实现。本文详细阐述了这一愿景，阐释了构建和利用这些实时数字孪生的不同方法，讨论了相关应用与开放性问题，并介绍了一个可用于探索多种数字孪生研究方向的研究平台。