oneTwin：通过神经射频场实现在线数字网络双胞胎
oneTwin: Online Digital Network Twin via Neural Radio Radiance Field
AI总结：
oneTwin通过增强模拟器和神经射频场实现在线数字网络双胞胎，有效提升物理层指标预测的实时性和准确性。
摘要：
数字网络双胞胎是一种有前景的技术，能够实时复制现实世界网络并协助下一代网络的设计、操作和管理。然而，现有方法（例如基于模拟器和神经网络的方法）无法有效实现数字网络双胞胎，在保真度、同步性和可操作性方面存在不足。本文提出oneTwin，首个在线数字双胞胎系统，用于物理层指标的预测。我们构建oneTwin系统有两个主要组件：增强的模拟器和神经射频场（NRRF）。一方面，我们通过设计材料调谐算法，逐步优化建筑材料以最小化双胞胎与现实之间的差距。另一方面，我们通过设计神经学习算法，根据增强模拟器的在线和模拟数据持续更新其DNNs。我们使用Sionna RT作为模拟器，并开发新的DNNs作为NRRF，在公共蜂窝网络下实施oneTwin系统。大量实验结果表明，与最先进的解决方案相比，oneTwin实现了实时更新（0.98秒），在分布内和分布外测试数据集上分别实现了36.39%和57.50%的双胞胎与现实之间差距的减少。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
