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

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 KPI exhibits a corresponding control-induced response. The difficulty is that KPI fluctuations may also arise from background or exogenous variation, so observed changes cannot be treated directly as control events. To address this challenge, we develop a significance-based event-detection procedure that converts continuous parameter and KPI increments into binary control-activity and KPI-response indicators. To evaluate this procedure, we construct a controlled closed-loop telemetry generator with planted parameter--KPI dependencies and tunable background variation. Experiments show that the proposed procedure reliably detects control-induced events and recovers the underlying dependency structure, outperforming alternative event-detection methods across a range of background-variation levels.