TelcoAgent实现5G多指标精准预测，无需本地训练且可解释。
TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability
- 基于3GPP规范构建知识图谱，自动生成可解释的网络认知框架。
- 零样本预测覆盖200个小区7项关键性能指标，准确率高。
- 适合运营商运维人员快速诊断网络问题，提升管理效率。
关键性能测量（KPM）预测对5G及下一代电信网络的主动运维至关重要。然而，现有机器学习方法在可扩展性和可解释性方面存在显著局限，制约了实际部署效果。本文提出TelcoAgent，一种基于基础模型的框架，可在不进行站点定制训练的前提下，实现跨多样化网络小区的多KPM精准、可扩展且可解释的预测。该框架包含三个核心组件：(i) 由3GPP规范文档自动生成的知识图谱构建的三智能体流水线；(ii) 基于时间序列基础模型（TSFM）的可扩展预测流水线，实现高精度零样本预测；(iii) 提供可操作、领域对齐诊断结果的推理与解释流水线。基于美国某运营商的真实城市级5G KPM数据集（为期3个月），评估显示TelcoAgent在200个小区上对全部7项考虑的KPM实现了高精度预测，同时提供可解释洞察与应对网络退化的具体建议。
原文摘要 · Abstract (English)
Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, existing machine learning (ML) approaches face significant limitations in scalability and explainability, restricting their effectiveness in real-world deployments. We propose TelcoAgent, a foundation model-based framework that enables accurate, scalable, and explainable forecasting of multiple KPMs across diverse network cells without the need for site-specific training. Specifically, the framework comprises three key components: (i) an automated three-agent pipeline that constructs a 3rd Generation Partnership Project (3GPP) knowledge graph directly from specification documents, (ii) a scalable, time-series foundation model (TSFM)-based prediction pipeline to deliver accurate, zero-shot forecasting, and finally (iii) a reasoning and explanation pipeline that provides actionable, domain-grounded diagnostics. Evaluated using a 3-month, real-world, city-scale 5G KPM dataset from a U.S.-based network operator, TelcoAgent demonstrates high forecasting accuracy for all 7 considered KPMs per cell across 200 cells, while delivering explainable insights and actionable instructions to address network degradations.
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