oneTwin: Online Digital Network Twin via Neural Radio Radiance Field

2026-01-06

arXiv.org

AI Summary

This work addresses the longstanding challenge in digital network twinning of simultaneously achieving high fidelity, low latency, and real-time physical-layer prediction. We present oneTwin, the first online digital network twin system, which integrates an enhanced Sionna RT simulator with a Neural Radio-Frequency Radiance Field (NRRF). By employing a material parameter tuning algorithm to minimize the twin-to-reality gap and incorporating a continual learning mechanism for joint updates of simulation and live data, oneTwin achieves a remarkably low update latency of just 0.98 seconds. Experimental results demonstrate that it reduces prediction errors by 36.39% in in-distribution scenarios and by 57.50% in out-of-distribution settings, substantially outperforming existing approaches and establishing the first real-time network twin capable of both high fidelity and low latency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Digital network twin is a promising technology that replicates real-world networks in real-time and assists with the design, operation, and management of next-generation networks. However, existing approaches (e.g., simulator-based and neural-based) cannot effectively realize the digital network twin, in terms of fidelity, synchronicity, and tractability. In this paper, we propose oneTwin, the first online digital twin system, for the prediction of physical layer metrics. We architect the oneTwin system with two primary components: an enhanced simulator and a neural radio radiance field (NRRF). On the one hand, we achieve the enhanced simulator by designing a material tuning algorithm that incrementally optimizes the building materials to minimize the twin-to-real gap. On the other hand, we achieve the NRRF by designing a neural learning algorithm that continually updates its DNNs based on both online and simulated data from the enhanced simulator. We implement oneTwin system using Sionna RT as the simulator and developing new DNNs as the NRRF, under a public cellular network. Extensive experimental results show that, compared to state-of-the-art solutions, oneTwin achieves real-time updating (0.98s), with 36.39% and 57.50% reductions of twin-to-real gap under in-distribution and out-of-distribution test datasets, respectively.

Problem

• digital network twin
• fidelity
• synchronicity
• tractability
• physical layer metrics

Innovation

• digital network twin
• neural radio radiance field
• online learning
• material tuning algorithm
• real-time synchronization