做无线网络AI控制或O-RAN优化的团队，终于有了一个从噪声数据中提取参数-KPI依赖关系的实用方法——合成数据生成器+事件检测管道可以直接复现，建议做网络智能化的开发者点开看看。
本文针对AI集成无线接入网络（AI-RAN）中多个AI控制函数相互干扰的问题，提出了一种事件检测方法。该方法将噪声连续遥测数据转换为二进制事件指示器，以识别控制参数与网络性能指标（KPI）之间的真实依赖关系。由于真实数据难以获取，作者构建了合成闭环流量生成器来模拟潜在依赖关系，并基于机器学习管道进行依赖恢复。实验表明，当信号与背景噪声充分分离时，该方法能可靠恢复依赖结构，且阈值校准是影响检测质量的关键因素。这项工作为自适应AI-RAN控制系统的可解释依赖学习奠定了基础。
Event Detection for Parameter-to-KPI Dependency Learning for AI-RAN
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.