FALCON: A Framework for Fault Prediction in Open RAN Using Multi-Level Telemetry

2025-03-08

AI Summary

In Open RAN’s multi-vendor, virtualized deployments, frequent failures across heterogeneous infrastructure, platform, and RAN layers—coupled with cross-layer root-cause attribution difficulties and delayed anomaly detection—pose significant operational challenges. Method: This paper proposes the first end-to-end fault prediction framework systematically integrating telemetry data from all three layers. It employs multi-source time-series alignment, cross-layer feature engineering, and random forest–based anomaly pattern recognition to enable minute-level early warning and interpretable fault attribution. Contribution/Results: Evaluated on a real-world O-RAN testbed, the framework achieves >98% accuracy and F1-score—substantially outperforming single-layer baselines. Its core innovation lies in establishing the first unified, full-stack fault prediction paradigm for O-RAN, overcoming the inherent layer isolation of conventional monitoring systems and enabling highly reliable, proactive network operations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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%.

Problem

• Predict faults in virtualized O-RAN deployments
• Address complexities in system integration and management
• Enhance resilience in open-source, virtualized RAN components

Innovation

• Multi-level telemetry for fault prediction
• AI/ML models for proactive fault management
• Random Forest classifier with 98% accuracy