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

arXiv: https://arxiv.org/abs/2305.10350 · HTML: https://mlsanity.com/paper/2305.10350/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 63 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Out-of-Distribution Generalization via Risk Extrapolation (REx)](https://mlsanity.com/paper/2003.00688/) (2020)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [The Risks of Invariant Risk Minimization](https://mlsanity.com/paper/2010.05761/) (2020)
- [Adaptive Gradient-Based Meta-Learning Methods](https://mlsanity.com/paper/1906.02717/) (2019)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://mlsanity.com/paper/2106.05370/) (2021)
- [Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin](https://mlsanity.com/paper/1907.01523/) (2019)
- [LIDAR Data for Deep Learning-Based mmWave Beam-Selection](https://mlsanity.com/paper/1908.07488/) (2019)
- [Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network](https://mlsanity.com/paper/2201.04712/) (2022)
- [3D Scene Based Beam Selection for mmWave Communications](https://mlsanity.com/paper/1911.08409/) (2019)
- [Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation](https://mlsanity.com/paper/2102.11593/) (2021)
- [Machine Learning on Camera Images for Fast mmWave Beamforming](https://mlsanity.com/paper/2102.07337/) (2021)
- [Real-Time Millimeter-Wave MIMO Channel Sounder for Dynamic Directional Measurements](https://mlsanity.com/paper/1807.11921/) (2018)
- [Energy and Latency of Beamforming Architectures for Initial Access in mmWave Wireless Networks](https://mlsanity.com/paper/2001.02034/) (2020)
- [Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems](https://mlsanity.com/paper/2302.05073/) (2023)
- [Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar](https://mlsanity.com/paper/1712.02453/) (2017)

## Built on by (most cited first, 5 of 5)

- [Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates](https://mlsanity.com/paper/2312.12625/) (2023)
- [Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning](https://mlsanity.com/paper/2406.15819/) (2024)
- [Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning](https://mlsanity.com/paper/2504.06173/) (2025)
- [Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Network](https://mlsanity.com/paper/2512.15109/) (2025)
- [Provable Performance Bounds for Digital Twin-driven Deep Reinforcement Learning in Wireless Networks: A Novel Digital-Twin Bisimulation Metric](https://mlsanity.com/paper/2502.17983/) (2025)

## Abstract (arXiv)

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.