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

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 ' $\MV$ ' 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 latency limitation, and (ii) a self-learning scheme that uses the $\MV$ -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 $\MV$ of twins at the edge, as opposed to a one-time emulation that is completed prior to actual deployment. Results reveal that $\MV$ offers up to $79.43\%$ and $85.22\%$ top- $10$ beam selection accuracy for LOS and NLOS scenarios, respectively. Moreover, we observe $67.70-90.79\%$ improvement in beam selection time compared to 802.11ad standard and 5G-NR standards.