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

TL;DR

This paper introduces the Multiverse paradigm, utilizing multiple digital twins of a wireless vehicle system to improve beamforming accuracy and speed in real-time, leveraging self-learning and decision strategies.

Contribution

It proposes a novel Multiverse framework with multiple digital twins for wireless beamforming, including a decision strategy and self-learning scheme, using real RF data from autonomous cars.

Findings

Achieves up to 79.43% top-10 beam selection accuracy in LOS scenarios.

Achieves up to 85.22% top-10 beam selection accuracy in NLOS scenarios.

Improves beam selection time by 52.72-85.07% over 802.11ad standard.

Abstract

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.