LLM-Driven Observability for Private 5G Networks: A Modular Platform for Industrial Environments - Patricio, Maria C. Z.; Kilmer, Luis; Pamplona, Vitor Z.; Silva, Rilbert L. da; Dias, Michel C.; Gomes, Ruan D. (Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2026)
Private 5G networks in industrial environments demand low-latency, trustworthy observability of standardized Key Performance Indicators (KPIs), such as those defined in ETSI TS 128 554. However, existing literature rarely addresses the unified integration of on-premises telemetry pipelines with natural-language analytics and the systematic validation of LLM-generated SQL against complex observability schemas. This paper proposes an on-premises observability architecture that orchestrates the ingestion of ETSI/3GPP-aligned KPIs from an Open5GS/OpenAirInterface testbed into a time-series database via a scalable Prometheus–Telegraf–Kafka pipeline. Furthermore, we introduce the Onion Validation framework, a multi-layer inference protocol that enforces intent classification, schema grounding, syntactic correctness, execution-plan conformance, and result-level verification with full auditability. An experimental evaluation was conducted using five 8-bit quantized Large Language Models (2B–8B parameters) processed against 130 domain-specific queries, comparing unified versus partitioned execution architectures. The empirical results reveal a maximum functional correctness of 27.7% in Answerable queries, highlighting the strictness of the validation protocol. The findings demonstrate that while current edge-deployed quantized models exhibit reasoning limitations, the proposed layered validation mechanism is essential to mitigate operational risks, thereby delineating the critical trade-off between on-premises data sovereignty and the semantic accuracy of automated analytics.