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Creating a digital world that closely mimics the real world with its manycomplex interactions and outcomes is possible today through advanced emulationsoftware and ubiquitous computing power. Such a software-based emulation of anentity 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 ona vehicle and show how it speeds up directional beam selection in mobileenvironments. To achieve this, we go beyond instantiating a single twin andpropose the 'Multiverse' paradigm, with several possible digital twinsattempting to capture the real world at different levels of fidelity. Towardsthis goal, this paper describes (i) a decision strategy at the vehicle thatdetermines which twin must be used given the computational and latencylimitations, and (ii) a self-learning scheme that uses the Multiverse-guidedbeam 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 publiclyavailable RF dataset collected from an autonomous car for creating differenttwins. Second, we present a framework with continuous interaction between thereal world and Multiverse of twins at the edge, as opposed to a one-timeemulation that is completed prior to actual deployment. Results reveal thatMultiverse offers up to 79.43% and 85.22% top-10 beam selection accuracy forLOS and NLOS scenarios, respectively. Moreover, we observe 52.72-85.07%improvement in beam selection time compared to 802.11ad standard.

Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming