Batool Salehi
Ph.D. Student
Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming
In the pursuit of creating a digital world that mimics reality, ‘digital twins’ have emerged as powerful tools. This paper focuses on wireless millimeter-wave band radios mounted on vehicles, proposing a revolutionary approach called the ‘Multiverse.’ It involves multiple digital twins working in real-time to optimize directional beam selection in dynamic mobile environments.
The Multiverse Paradigm:
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- Unlike traditional singular digital twins, the ‘Multiverse’ approach creates diverse twins at different fidelity levels. This enables continuous interaction between the real world and the twin ensemble, enhancing adaptability and responsiveness.
A smart decision strategy within the vehicle selects the most suitable twin, considering computational and latency constraints. A self-learning scheme based on Multiverse outcomes enhances decision-making using machine learning over time.
Key Advantages:
Using a publicly available RF dataset collected from an autonomous car ensures realism:
- The Multiverse system achieves up to 79.43% and 85.22% top-10 beam selection accuracy for LOS and NLOS scenarios.
- 52.72-85.07% faster beam selection compared to the 802.11ad standard.
The Multiverse of digital twins holds immense promise in revolutionizing mobile communication by enabling real-time adaptability and improving directional beam selection. This research brings us closer to more advanced and reliable mobile communication systems.
Paper 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.
Source: arxiv.org
WIoT Institute Researchers Associated
Stratis Ioannidis
Associate Professor of Electrical and Computer Engineering
Kaushik Chowdhury
Professor of Electrical and Computer Engineering
Institute Associate Director