AI summaryⓘ
The authors study how to better understand interactions between multiple AI-driven control functions in future wireless networks like AI-RAN and O-RAN. They focus on detecting when changes in control parameters actually affect network performance, distinguishing these from random noise in continuous data. To test their method, they create synthetic network data with known hidden dependencies, enabling them to evaluate how well their approach converts noisy data into clear signals of activity. Their results show the method can reliably find true interactions when the real signal stands out from noise, emphasizing the importance of setting the right detection thresholds. This work lays groundwork for building interpretable tools to manage complex AI controls in advanced networks.
AI-RANO-RANcontrol functionsdependency learningevent detectiontelemetry datamachine learningnetwork performancesignal-to-noise ratiothreshold calibration
Authors
Christie Djidjev, Nicholas Kaminski
Abstract
Next-generation wireless networks are expected to rely on multiple concurrent AI-driven control functions that optimize different network objectives simultaneously, particularly in AI-integrated and open radio access network architectures such as AI Radio Access Network (AI-RAN) and Open Radio Access Network (O-RAN). 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 by converting noisy continuous telemetry into binary indicators of parameter activity and KPI response. The central difficulty is that not every fluctuation in the data reflects a genuine control interaction, so the method must distinguish real parameter-outcome relationships from background variation. Because real AI-RAN traffic traces with known parameter-KPI ground truth are difficult to obtain, we introduce a synthetic closed-loop traffic generator with planted latent dependencies. We use this controlled telemetry to evaluate a machine-learning-based dependency recovery pipeline that formulates the conversion of continuous traces into binary event indicators as a significance-detection problem. Experimental evaluation shows that the proposed pipeline reliably recovers the latent dependency structure from noisy continuous traces when the signal is sufficiently separated from background variation, while highlighting threshold calibration as the key factor controlling event-detection quality. These results constitute a foundational step toward interpretable dependency learning for adaptive AI-RAN control systems.