KPI aware anomaly detection improves 5G and Open RAN monitoring
KPI-Aware Multimodal Anomaly Detection for 5G and Open RAN Telemetry
Machine Learning
Summary
Detecting problems in complex 5G networks is hard because there are many different signals (KPIs) to watch and very few real issues to catch. The authors propose a new method called KAC that uses feedback from simple operator summaries to better spot anomalies across many data types. This approach combines detailed number tracking with clear, focused reasoning that operators can understand and trust. They tested KAC on real and simulated telecom data, showing it works well even with unbalanced data and low false alarms.
What this means in practice
- •For telecom network operators: Improve automated monitoring of 5G and Open RAN networks by using KPI-aware anomaly detection to reduce missed real issues under strict alert budgets.
- •For cloud platform engineers: Integrate continuous anomaly scoring within cloud-based KPI construction pipelines to assist large-scale telecom network observability operations.
Authors
Chiman Salavati, Liang Wu, Kelly Wan, Mayank Darbari, Liangjie Hong
Abstract
Reliable anomaly detection is a deployment bottleneck for 5G/Open RAN observability and emerging self-optimizing 6G systems, where hundreds of heterogeneous KPIs, severe class imbalance, and strict alert budgets make purely numerical scores brittle. We present KAC, a deployment-oriented KPI-aware multimodal anomaly-detection framework that uses KPI-level residual-text alignment to turn compact operator-style summaries into a learnable feedback signal over full-KPI temporal evidence. This gives the detector broad numerical coverage like modern time-series models, while retaining the sparse KPI-specific reasoning that makes operator feedback sustainable in a future operator-facing workflow. Across public telecom telemetry, enterprise-scale Open RAN observability data, and a Nokia benchmark built from real 5G RAN production packet traces with realism-hardened injected anomalies, KAC achieves strong threshold-based detection performance while remaining competitive on threshold-free ranking metrics. On TelecomTS, at a fixed 2.5 percent false-positive-rate operating point, KAC achieves the highest recall among the evaluated residual-based methods while maintaining comparable precision; across the evaluated benchmarks, it remains robust under substantial class imbalance. KAC is integrated in pre-production shadow mode with Nokia's GKE-based KPI construction pipeline, where it continuously scores telemetry windows alongside an internal packet-level anomaly detector for operational evaluation.