IntentNEF: LLM-Driven Natural Language Automation of 5G Network Exposure
Configuring 5G networks through standardized exposure interfaces—the Network Exposure Function (NEF) and the Common API Framework (CAPIF)—remains operationally burdensome, requiring manual navigation of 3 GPP parameter schemas, OAuth2 authentication, and live network state. This paper presents a modular, intent-driven architecture that translates natural language operator goals into schema-compliant NEF API configurations using a compact, locally deployed Large Language Model (LLM), Qwen3-4B (4 billion parameters). A deterministic pre-classification layer reserves LLM inference for semantically complex requests, while a six-stage pipeline produces near-deterministic, conflict-aware JSON output. Two complementary validation paths are provided: a Standard Mode with closed-loop Quality of Service (QoS) feedback via a NEF emulator, and a Free5GC Mode that validates the same translation logic against a Free5GC-based experimental environment with real user-plane traffic. Demonstration across five vertical scenarios confirms end-to-end correctness in both the emulated NEF environment and the Free5GC-based experimental environment.