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.

翻译：传统的无线接入网（RAN）系统具有封闭性和整体性，严重制约了创新。由开放无线接入网（O-RAN）所实现的开放性与可编程性有望通过控制平面应用（xApp）彻底变革蜂窝网络。然而，xApp的开发（通常由第三方开发者完成）仍然耗时且繁琐，往往需要数月的人工编码与集成工作，这阻碍了新功能在实际中的快速部署。为降低开发者和网络运营商在xApp开发中的门槛，我们提出了AutORAN——首个基于大语言模型（LLM）驱动的自然语言编程框架，支持敏捷xApp开发，并实现整个xApp开发流程的自动化。简而言之，AutORAN能在数分钟内将高层用户意图转化为可快速部署的xApp，无需人工编码或测试。为此，AutORAN构建了全自动化的xApp生成流水线，集成了从用户需求获取、AI/ML功能设计与验证，到xApp合成与部署等多个功能模块。我们在代表性xApp任务上设计、实现并全面评估了AutORAN。结果表明，AutORAN生成的xApp性能可媲美甚至超越已知的最优手工基线。AutORAN极大缩短了xApp开发周期（从用户意图获取到实际部署），加速了O-RAN创新进程。