Detecting Control and Response Events for AI-Enabled Radio Access Networks
The paper introduces a significance-based event-detection procedure to reliably find genuine control actions and corresponding KPI responses from noisy network data in AI-RAN and O-RAN systems. It converts continuous parameter changes into binary indicators, allowing for the recovery of underlying control dependencies despite background noise.