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.

翻译：数字网络孪生是一项极具前景的技术，它能够实时复现现实世界中的网络，并协助下一代网络的设计、运营与管理。然而，现有方法（例如基于仿真器的方法和基于神经网络的方法）在保真度、同步性和可处理性方面均无法有效实现数字网络孪生。本文提出了oneTwin，首个用于物理层指标预测的在线数字孪生系统。我们设计了oneTwin系统的两个核心组件：一个增强型仿真器和一个神经无线辐射场。一方面，我们通过设计一种材料调优算法来实现增强型仿真器，该算法能够增量式优化建筑物材质，以最小化孪生与真实环境之间的差距。另一方面，我们通过设计一种神经学习算法来实现神经无线辐射场，该算法能够基于来自增强型仿真器的在线数据和仿真数据持续更新其深度神经网络。我们以公开蜂窝网络为背景，使用Sionna RT作为仿真器，并开发了新的深度神经网络作为神经无线辐射场，实现了oneTwin系统。大量实验结果表明，与现有最先进的解决方案相比，oneTwin能够实现实时更新（0.98秒），并且在分布内和分布外测试数据集下，孪生与真实环境之间的差距分别降低了36.39%和57.50%。