QoEReasoner: An Agentic Reasoning Framework for Automated and Explainable QoE Diagnosis in RANs
2026-06-01 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors created a system called QoEReasoner to help diagnose problems in cellular networks more quickly and accurately. They combined powerful language models with fixed rules and expert knowledge to better understand network performance data and find the root causes of issues. Their system works faster and more reliably than previous methods, providing clear explanations for its findings. It was tested on real network data and showed significant improvements in both speed and accuracy.
Quality-of-Experience (QoE)Radio Access Networks (RAN)Large Language Models (LLMs)Key Performance Indicators (KPIs)fault localizationcausal tracingknowledge baseanomaly detectiontelemetry data
Authors
Qizhe Li, Haolong Chen, Shan Dai, Zhuo Li, Zhiwei Hu, Xuan Li, Guangxu Zhu, Qingjiang Shi
Abstract
Diagnosing Quality-of-Experience (QoE) degradations in operational Radio Access Networks (RANs) is a critical but notoriously complex task, traditionally requiring labor-intensive expert analysis over high-dimensional, cross-layer telemetry. While Large Language Models (LLMs) offer unprecedented reasoning capabilities, they are fundamentally unsuited for raw RANs troubleshooting: they fail at numeric time-series analysis, hallucinate protocol-violating causal links, and lack the stateful rigor required for multi-step fault localization. To bridge this gap, we present QoEReasoner, an end-to-end, LLM-driven agentic system designed for automated and explainable QoE diagnosis. QoEReasoner tames the inherent unpredictability of LLMs by grounding their reasoning in the physical realities of the network. It employs deterministic tools to reliably translate raw numeric KPIs into structured evidence, enforces protocol-consistent fault propagation through a domain-specific Knowledge Base, and leverages a Historical Bank of expert-validated cases to guide hypothesis generation. A stateful central planner orchestrates this closed-loop process across anomaly detection, causal tracing, and root-cause localization. Evaluations on real-world operational RANs datasets demonstrate that QoEReasoner outperforms strong baselines by 18\%-40\% in accuracy across multiple diagnostic tasks. Furthermore, it reduces diagnostic time from approximately 30 minutes of manual expert analysis to just 3 minutes per session, delivering highly interpretable, expert-grade reports while remaining robust across diverse LLM backbones.