TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability

2026-06-18

AI Summary

This study addresses the limitations of existing machine learning approaches in predicting 5G Key Performance Metrics (KPMs), which suffer from poor scalability and insufficient interpretability, thereby hindering their practical deployment in network operations. To overcome these challenges, this work proposes the first foundation-model-based end-to-end framework that leverages a three-agent system to automatically translate 3GPP specifications into a knowledge graph. Integrated with a time series foundation model (TSFM), the framework enables zero-shot, cross-base-station multi-KPM prediction. Furthermore, it introduces a knowledge graph–driven reasoning mechanism to generate actionable diagnostic explanations. Evaluated on a real-world 5G dataset spanning 200 base stations over three months, the method achieves high prediction accuracy across seven KPMs, significantly enhancing both model generalization and operational utility.

Abstract

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.

Problem

• KPM forecasting
• scalability
• explainability
• 5G networks
• machine learning

Innovation

• foundation model
• 3GPP knowledge graph
• zero-shot forecasting
• explainable AI
• multi-KPM prediction