When Green KPIs Lie: Uncovering Hidden Network Issues with AI-Curated RAN Data

For years, mobile operators have relied on key performance indicators (KPIs) and counters as the primary way to understand network performance. Handover success rates, drop rates, and throughput averages are all useful, but increasingly insufficient. As networks grow more complex and user expectations rise, operators are discovering green KPIs do not always mean good customer experience.

This is where NETSCOUT’s AI‑curated RAN data changes the equation.

Rather than treating network events as isolated metrics, NETSCOUT preserves correlation and causality across the full subscriber journey. The result is AI‑ready data that enables real situation awareness, accurate root cause analysis, and actionable recommendations. The following four success stories illustrate what becomes possible when RAN data is curated for intelligence and not just for reporting.

Seeing Beyond “Successful” Handovers

In the first example, traditional KPIs showed healthy handover performance between cells. On paper, everything looked green. Yet customers were experiencing call drops shortly after entering the target cell. The drops were happening after a technically successful handover, breaking the link between cause and effect in traditional analytics.

By preserving causality across events, NETSCOUT’s AI‑curated data allowed the AI models to understand that these drops were directly related to specific handover conditions. This made it possible to identify suboptimal configuration parameters and recommend precise adjustments, turning an invisible problem into a solvable one.

Eliminating Unnecessary Handovers in Busy Urban Areas

Another success story focused on unnecessary neighbor relations in dense, high‑traffic environments such as downtown corridors, highways, and commuter rail lines. Thousands of users were being handed over into an intermediate cell for just a few seconds before moving on. The network could handle the signaling, so KPIs remained green even though the cell designed to serve local businesses was overloaded with transient traffic.

NETSCOUT’s state‑machine‑based single‑call analysis, applied at scale, revealed these ultra‑short dwell patterns and their statistical significance. AI models then recommended targeted actions such as antenna down tilt to remove the unnecessary hop and restore the cell’s intended performance. This is causality in action: understanding not just what happened, but why it matters.

Reducing Ping‑Pong Handovers to Restore Capacity

Ping‑pong handovers are another classic example of “hidden harm.” Users toggle rapidly between two cells due to lack of dominance in overlap areas. Every handover succeeds, KPIs stay green, yet valuable capacity is consumed by control‑plane signaling instead of delivering data.

By correlating mobility patterns with radio measurements, NETSCOUT enables AI to detect these oscillations, identify lack of dominance as the root cause, and propose configuration changes to restore clean handovers. The outcome is not just fewer handovers, but better throughput and experience for all users served by those cells.

Optimizing Carrier Aggregation with Relevance and Enrichment

Carrier aggregation promises higher capacity, but only when user equipment (UE) capabilities, network decisions, radio conditions, and retainability align. Traditional KPI views fragment this story. NETSCOUT keeps these related datasets together, enabling AI to understand whether devices are receiving the band combinations they support and whether those combinations are sustained under real conditions.

Equally important is relevance. AI context windows are finite, and raw data volume increases cost without improving insight. NETSCOUT curates and enriches only what matters, removing redundancy while preserving correlation and causality. The result is better AI outcomes with lower total cost of ownership.

Just a Handful of What’s Possible

These four success stories are only a small sample of what becomes possible with NETSCOUT’s AI‑curated RAN data. They highlight a fundamental shift from metrics to meaning, from indicators to inference. By delivering relevant, enriched, and causality‑preserving datasets, NETSCOUT enables AI models to move beyond dashboards and into real operational intelligence.

In a world where network complexity keeps growing, the difference isn’t more data, it’s better data, curated for AI. Learn more about the use cases highlighted above.