O-RAN has brought in deployment flexibility and intelligent RAN control for mobile operators through its disaggregated and modular architecture using open interfaces. However, this disaggregation introduces complexities in system integration and network management, as components are often sourced from different vendors. In addition, the operators who are relying on open source and virtualized components -- which are deployed on commodity hardware -- require additional resilient solutions as O-RAN deployments suffer from the risk of failures at multiple levels including infrastructure, platform, and RAN levels. To address these challenges, this paper proposes FALCON, a fault prediction framework for O-RAN, which leverages infrastructure-, platform-, and RAN-level telemetry to predict faults in virtualized O-RAN deployments. By aggregating and analyzing metrics from various components at different levels using AI/ML models, the FALCON framework enables proactive fault management, providing operators with actionable insights to implement timely preventive measures. The FALCON framework, using a Random Forest classifier, outperforms two other classifiers on the predicted telemetry, achieving an average accuracy and F1-score of more than 98%.

翻译：O-RAN通过其采用开放接口的解耦模块化架构，为移动运营商带来了部署灵活性与智能化的无线接入网络控制能力。然而，这种解耦架构也因组件常来自不同供应商而引入了系统集成与网络管理的复杂性。此外，依赖于部署在商用硬件上的开源与虚拟化组件的运营商，由于O-RAN部署面临基础设施层、平台层及无线接入网络层等多层级故障风险，需要额外的弹性解决方案。为应对这些挑战，本文提出FALCON——一种面向O-RAN的故障预测框架，该框架利用基础设施层、平台层及无线接入网络层遥测数据来预测虚拟化O-RAN部署中的故障。通过采用人工智能/机器学习模型聚合并分析来自不同层级各组件指标，FALCON框架实现了主动式故障管理，为运营商提供可操作的洞察以实施及时预防措施。基于随机森林分类器的FALCON框架在预测遥测数据上优于其他两种分类器，平均准确率与F1分数均超过98%。