为智能无线网络冲突检测，提出可解释的运行时依赖追踪方法。
Explainable Runtime Dependency Tracking for AI-RAN Conflict Monitoring
- 用布尔矩阵表示参数与指标间的依赖关系，通过矩阵乘法验证一致性。
- 滑动窗口机制动态更新依赖模型，噪声下仍保持高精度追踪。
- 适合需要可解释性、实时监控的智能无线网络运维场景。
未来融合AI的无线接入网（AI-RAN）将结合开放可编程性与基于学习的xApps、rApps及控制功能，作用于共享参数和关键性能指标（KPI）。针对冲突监控，仅知部署应用不足，还需确认运行时诊断所依赖的参数-指标关系在当前运行环境下是否依然有效。本文提出一种轻量级监控原语：从流式遥测事件中追踪可解释的依赖表示。通过布尔矩阵表示活跃依赖关系，并利用布尔矩阵乘法检验近期参数活动与KPI响应事件是否与当前估计一致。设计滑动窗口推断过程，在依赖关系未变时复用估计值，发现结构变化时重新计算。该追踪器旨在为冲突诊断与慢环模型刷新提供可解释信号，而非自主缓解机制。在受控布尔事件流上的实验表明，该方法在依赖关系变化和布尔观测噪声下仍能实现高效准确的追踪。
原文摘要 · Abstract (English)
Future AI-integrated Radio Access Networks (AI-RAN) will combine open programmability with learning-enabled xApps, rApps, and control functions that act on shared parameters and key performance indicators (KPIs). For conflict monitoring, it is not enough to know which applications are deployed; the system must also know whether the parameter--KPI dependencies assumed by runtime diagnosis remain valid under the current operating regime. This paper studies a lightweight monitoring primitive for that purpose: tracking an interpretable dependency representation from streaming telemetry events. We represent active dependencies by a Boolean matrix and use Boolean matrix multiplication to check whether recent parameter-activity and KPI-response events are consistent with the current estimate. We propose a sliding-window inference procedure that reuses the estimate when it remains consistent and recomputes it when recent observations indicate structural change. The tracker is intended as an explainable signal for conflict diagnosis and slow-loop model refresh, not as an autonomous mitigation mechanism. Experiments on controlled Boolean event streams show efficient and accurate tracking under dependency changes and Boolean observation noise.
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