  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.
