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.

翻译：开放无线接入网（O-RAN）的无线接入网智能控制器（RIC）承载数据驱动的xApps和rApps以优化网络性能。然而，基于机器学习的xApp/rApp开发面临两大挑战：（i）接口延迟导致的关键性能指标（KPM）数据稀缺性，以及（ii）在现网直接测试和验证AI模型带来的网络中断风险。我们提出OpenTwin，这是一个基于开源O-RAN仿真器（ns-O-RAN-flexRIC）及通过O1接口进行KPM流式传输构建的数字孪生框架，部署于非实时RIC中。OpenTwin采用两步式机器学习方法：首先通过XGBoost模型学习时变网络行为以生成仿真器配置参数，随后采用时间感知递归最小二乘（RLS）调谐器持续修正孪生体与真实测量值之间的KPM偏差。基于偏差感知的评分机制监控孪生体保真度，并在检测到网络漂移时自动触发重新同步。我们通过一个节能型xApp验证OpenTwin：该xApp在虚拟空间中验证控制策略后再对物理网络进行重配置。实验结果表明，OpenTwin对真实世界KPM的镜像准确率高达96%，并使xApp在无需中断现网运营的情况下显著降低能耗。