AutORAN: LLM-driven Natural Language Programming for Agile xApp Development

2026-03-19

AI Summary

This work addresses the inefficiencies of traditional O-RAN xApp development, which relies heavily on manual coding and integration, thereby hindering rapid deployment of new functionalities. The paper proposes the first natural language programming framework powered by large language models (LLMs) to automatically translate high-level user intents into deployable xApps, enabling end-to-end automation from requirement interpretation and AI/ML function design validation to integration and deployment. By introducing LLMs into O-RAN xApp development for the first time, the approach generates high-quality xApps within minutes—matching or even surpassing the performance of manually engineered baselines. This paradigm dramatically reduces the development cycle from months to minutes, significantly lowering the barrier to entry and enhancing innovation agility within the O-RAN ecosystem.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Traditional RAN systems are closed and monolithic, stifling innovation. The openness and programmability enabled by Open Radio Access Network (O-RAN) are envisioned to revolutionize cellular networks with control-plane applications--xApps. The development of xApps (typically by third-party developers), however, remains time-consuming and cumbersome, often requiring months of manual coding and integration, which hinders the roll-out of new functionalities in practice. To lower the barrier of xApp development for both developers and network operators, we present AutORAN, the first LLM-driven natural language programming framework for agile xApps that automates the entire xApp development pipeline. In a nutshell, AutORAN turns high-level user intents into swiftly deployable xApps within minutes, eliminating the need for manual coding or testing. To this end, AutORAN builds a fully automated xApp generation pipeline, which integrates multiple functional modules (from user requirement elicitation, AI/ML function design and validation, to xApp synthesis and deployment). We design, implement, and comprehensively evaluate AutORAN on representative xApp tasks. Results show AutORAN-generated xApps can achieve similar or even better performance than the best known hand-crafted baselines. AutORAN drastically accelerates the xApp development cycle (from user intent elicitation to roll-out), streamlining O-RAN innovation.

Problem

• O-RAN
• xApp development
• natural language programming
• LLM-driven automation
• RAN programmability

Innovation

• LLM-driven
• natural language programming
• xApp automation
• O-RAN
• AI/ML function synthesis