Abstract

Scalable, interpretable, and intelligent network monitoring and management are critical for 5 G and future networks. This paper introduces Aim5B, an AI-integrated semantic framework for 5 G and beyond network management to address these challenges. Aim5B processes unstructured logs from key 5G core network functions, and transforms them into a knowledge graph aligned with the semantic structure of control-plane events. Leveraging a large language model (LLM), Aim5B enables natural language queries to be translated into Cypher graph queries, facilitating precise log retrieval, event analysis, temporal correlation, and statistical summarization-without relying on static parsing rules or predefined dashboards. Integrated on a real private 5G testbed, Aim5B achieved the full performances in precision, recall, F1, and Jaccard metrics, across various detailed, domain-specific queries per network function component. Furthermore, it significantly reduces management traffic-by approximately 99.9%-through targeted, event-specific querying. These results demonstrate the effectiveness and efficiency of Aim5B as an intelligence-driven solution for real-time, scalable network observability in future mobile systems.

Department(s)

Computer Science

Keywords and Phrases

5G/6G Network Management; AI-Assisted Querying; Event-Driven Monitoring; Graph Databases; Knowledge Graphs; Large Language Models; Network Observability; Semantic Log Analysis; Syslog

International Standard Serial Number (ISSN)

1525-3511

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2026

Download

Full Text Link

Share

 
COinS