Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous computing power. Such a software-based emulation of an entity that exists in the real world is called a 'digital twin'. In this paper, we consider a twin of a wireless millimeter-wave band radio that is mounted on a vehicle and show how it speeds up directional beam selection in mobile environments. To achieve this, we go beyond instantiating a single twin and propose the 'Multiverse' paradigm, with several possible digital twins attempting to capture the real world at different levels of fidelity. Towards this goal, this paper describes (i) a decision strategy at the vehicle that determines which twin must be used given the computational and latency limitations, and (ii) a self-learning scheme that uses the Multiverse-guided beam outcomes to enhance DL-based decision-making in the real world over time. Our work is distinguished from prior works as follows: First, we use a publicly available RF dataset collected from an autonomous car for creating different twins. Second, we present a framework with continuous interaction between the real world and Multiverse of twins at the edge, as opposed to a one-time emulation that is completed prior to actual deployment. Results reveal that Multiverse offers up to 79.43% and 85.22% top-10 beam selection accuracy for LOS and NLOS scenarios, respectively. Moreover, we observe 52.72-85.07% improvement in beam selection time compared to 802.11ad standard.

翻译：通过先进的仿真软件与泛在计算能力，如今我们能够创建高度模拟真实世界及其复杂交互与结果的数字世界。这种基于软件的、对真实世界实体的仿真被称为"数字孪生"。本文研究了安装在车辆上的毫米波频段无线电设备的孪生体，并展示了其如何加速移动环境中的定向波束选择。为此，我们超越了单一孪生体的实例化，提出了"多宇宙"范式，即使用多个不同保真度的数字孪生体来捕捉真实世界。围绕这一目标，本文描述了：(i) 车辆端根据计算与延迟限制决定选用哪个孪生体的决策策略，以及(ii) 一种自学习方案，该方案利用多宇宙引导的波束选择结果，逐步增强真实世界中基于深度学习的决策能力。本研究与先前工作的区别在于：首先，我们使用从自动驾驶汽车采集的公开射频数据集来构建不同的孪生体；其次，我们提出了一个框架，实现真实世界与边缘多宇宙孪生体之间的持续交互，而非实际部署前的一次性仿真。结果表明，在视距与非视距场景下，多宇宙分别实现了高达79.43%和85.22%的前十波束选择准确率。此外，与802.11ad标准相比，波束选择时间提升了52.72%-85.07%。