Explainable Runtime Dependency Tracking for AI-RAN Conflict Monitoring

2026-06-04

AI Summary

This work addresses the challenge that dependencies between runtime parameters and key performance indicators (KPIs) in AI-RAN systems are highly sensitive to environmental dynamics, often leading to failure in conflict diagnosis. To overcome this, the authors propose a lightweight and interpretable dynamic dependency tracking mechanism. It models active dependencies using Boolean matrices and performs real-time inference on telemetry event streams via a sliding window. Upon detecting structural changes, the method automatically triggers recomputation while efficiently reusing historical estimates to ensure consistency. Experimental results demonstrate that the approach accurately and efficiently tracks dependencies in noisy Boolean event streams with time-varying dependency structures, thereby enabling reliable conflict diagnosis and supporting infrequent model refreshes.

Abstract

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.

Problem

• AI-RAN
• runtime dependency
• conflict monitoring
• parameter-KPI dependency
• explainable tracking

Innovation

• Explainable AI
• Runtime Dependency Tracking
• Boolean Matrix Representation
• AI-RAN Conflict Monitoring
• Sliding-Window Inference