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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.18604 |
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| _version_ | 1866912973868498944 |
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| author | Li, Xin Yu, Shiming Shen, Leming Zhang, Jianing Zheng, Yuanqing Xie, Yaxiong |
| author_facet | Li, Xin Yu, Shiming Shen, Leming Zhang, Jianing Zheng, Yuanqing Xie, Yaxiong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18604 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AutORAN: LLM-driven Natural Language Programming for Agile xApp Development Li, Xin Yu, Shiming Shen, Leming Zhang, Jianing Zheng, Yuanqing Xie, Yaxiong Networking and Internet Architecture Artificial Intelligence 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. |
| title | AutORAN: LLM-driven Natural Language Programming for Agile xApp Development |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2603.18604 |