oneTwin: Online Digital Network Twin via Neural Radio Radiance Field

TL;DR

oneTwin is an innovative online digital network twin system that combines an enhanced simulator and neural radiance fields to accurately and efficiently predict physical layer metrics in real-time, improving fidelity and synchronization.

Contribution

This paper introduces the first online digital twin system for networks, integrating a material-tuned simulator and neural radiance fields for real-time, high-fidelity network metric prediction.

Findings

Achieves real-time updates in under 1 second.

Reduces twin-to-real gap by over 36% in-distribution.

Reduces twin-to-real gap by over 57% out-of-distribution.

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.