Detecting Control and Response Events for AI-Enabled Radio Access Networks

תקציר מקורי באנגליתarXiv:2606.06459v2 Announce Type: replace Abstract: Next-generation wireless networks are moving toward the use of concurrent AI-driven control functions to optimize different objectives, particularly in AI-RAN and O-RAN architectures. When these functions interact, they can interfere with one another in ways that are difficult to detect from raw network data alone. A key missing piece for managing such interactions is a reliable, interpretable dependency structure that captures which control parameters are actively influencing which network performance outcomes at any given time. This paper focuses on the event-detection step needed to support such dependency learning: given noisy continuous parameter and KPI telemetry, we seek to determine when a genuine control action occurs and when a