Autonomous Radio Network Intelligence: Architectural Principles of Large Language Model-Driven Closed-Loop 5G Management Systems

Abstract

Large language models (LLMs) are emerging as cognitive engines within telecommunications infrastructure, introducing a new paradigm for radio access network (RAN) design and management. Traditional methodologies for spectral efficiency and diagnostics have relied on manual workflows that are increasingly insufficient for the complexity of fifth‑generation (5G) and evolving sixth‑generation (6G) systems. This article presents architectural principles for autonomous network intelligence, focusing on closed‑loop orchestration through Reasoning and Acting (ReAct) frameworks, deterministic tool invocation, and stateful memory management. Core concepts, like thread-aware state machines, pipeline toolchains, and geospatial infrastructure for data, facilitate the performance of diagnostics by intelligent agents and the execution of physical layer optimizations at expert levels. In the context of the enterprise architecture, factors are brought into consideration, particularly how a flexible design that supports LLMs' reasoning capabilities can be balanced with deterministic safety requirements. The resulting architecture provides a scalable and implementation-oriented foundation for autonomous closed-loop management in next-generation telecommunications networks. The proposed architecture further incorporates policy-driven automation layers, KPI-aware monitoring pipelines, and telemetry-assisted orchestration aligned with Open RAN and near-real-time radio intelligent controller (Near-RT RIC) environments. Network indicators, including Signal-to-Interference-plus-Noise Ratio (SINR), Block Error Rate (BLER), Channel Quality Indicator (CQI), and Physical Resource Block (PRB) utilization, are continuously correlated to support anomaly detection and adaptive remediation procedures. Deterministic authorization controls, rollback safeguards, and distributed telemetry processing are integrated to maintain operational reliability during autonomous optimization procedures.