BostonTwin: the Boston Digital Twin for Ray-Tracing in 6G Networks
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Abstract (arXiv)
Digital twins are now a staple of wireless networks design and evolution. Creating an accurate digital copy of a real system offers numerous opportunities to study and analyze its performance and issues. It also allows designing and testing new solutions in a risk-free environment, and applying them back to the real system after validation. A candidate technology that will heavily rely on digital twins for design and deployment is 6G, which promises robust and ubiquitous networks for eXtended Reality (XR) and immersive communications solutions. In this paper, we present BostonTwin, a dataset that merges a high-fidelity 3D model of the city of Boston, MA, with the existing geospatial data on cellular base stations deployments, in a ray-tracing-ready format. Thus, BostonTwin enables not only the instantaneous rendering and programmatic access to the building models, but it also allows for an accurate representation of the electromagnetic propagation environment in the real-world city of Boston. The level of detail and accuracy of this characterization is crucial to designing 6G networks that can support the strict requirements of sensitive and high-bandwidth applications, such as XR and immersive communication.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2024.
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Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2025Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6Gcited by 2
- 2025AIRMap: AI-Generated Radio Maps for Wireless Digital Twinscited by 1
- 2026Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Controlcited by 0
- 2026Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modelingcited by 0
- 2026Neural Electromagnetic Fields for High-Resolution Material Parameter Reconstructioncited by 0
- 2026KODAMA: Multimodal Digital Twin Reconstruction for Urban RF Propagation Modellingcited by 0
- 2026KODAMA: Multimodal Digital Twin Reconstruction for Urban RF Propagation Modelling7 Sep 2026
- 2026Neural Electromagnetic Fields for High-Resolution Material Parameter Reconstruction3 Mar 2026
- 2026Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling2 Mar 2026
- 2026Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Control27 Jan 2026
- 2025AIRMap: AI-Generated Radio Maps for Wireless Digital Twins28 Oct 2025
- 2025Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6G11 Jul 2025