OpenTwin: Digital Twin Driven Closed Loop KPM Inference and Control for Open RAN

2026-05-23

AI Summary

This work addresses the challenges of sparse Key Performance Metric (KPM) data caused by interface latency in O-RAN and the risk of service disruption from in-situ AI testing. To overcome these issues, the authors propose OpenTwin, a digital twin framework built upon an open-source O-RAN simulator. OpenTwin employs a two-stage machine learning approach: it first uses XGBoost to accurately infer missing KPMs and then applies a time-aware Recursive Least Squares (RLS) tuner for closed-loop control. A novel bias-aware synchronization mechanism dynamically models network behavior and automatically re-synchronizes the twin with the physical network to maintain fidelity. Experimental results on the ns-O-RAN-flexRIC platform demonstrate that OpenTwin achieves 96% accuracy in KPM reconstruction while significantly reducing energy consumption—all without introducing any interference to live network operations.

Abstract

The open radio access network (O-RAN) RAN intelligent controller (RIC) hosts data-driven xApps and rApps to optimize network performance. However, two challenges hinder ML-driven xApp/rApp development: (i) key performance metric (KPM) data scarcity caused by interface latency, and (ii) network disruption risks when testing and validating AI models directly on live networks. We develop OpenTwin, a digital twin framework built on an open-source O-RAN simulator (ns-O-RAN-flexRIC) and KPM streaming via the O1 interface, deployed within the non-RT RIC. OpenTwin uses a two-step ML approach: an XGBoost model that learns time-varying network behavior to generate simulator configuration parameters, followed by a time-aware recursive least squares (RLS) tuner that continuously corrects KPM deviations between the twin and real-world measurements. A deviation-aware scoring mechanism monitors twin fidelity and automatically triggers resynchronization upon detecting network drift. We demonstrate OpenTwin with an energy-saving xApp that validates control policies in the virtual space before applying reconfigurations to the physical network. Experimental results show that OpenTwin mirrors real-world KPMs with up to 96% accuracy and enables the xApp to significantly reduce energy consumption without disrupting live operations.

Problem

• Open RAN
• KPM data scarcity
• network disruption
• AI model validation
• RAN intelligent controller

Innovation

• Digital Twin
• O-RAN
• KPM Inference
• Closed-loop Control
• Machine Learning