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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.15595 |
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| _version_ | 1866909747654950912 |
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| author | Dandekar, Abhishek Thapa, Prashiddha D. Rahman, Ashrafur Schulz-Zander, Julius |
| author_facet | Dandekar, Abhishek Thapa, Prashiddha D. Rahman, Ashrafur Schulz-Zander, Julius |
| contents | Traditional standardized network interfaces face significant limitations, including vendor-specific incompatibilities, rigid design assumptions, and lack of adaptability for new functionalities. We propose a multi-agent framework leveraging large language models (LLMs) to generate control interfaces on demand between network functions (NFs). This includes a matching agent, which aligns required control functionalities with NF capabilities, and a code-generation agent, which generates the necessary API server for interface realization. We validate our approach using simulated multi-vendor gNB and WLAN AP environments. The performance evaluations highlight the trade-offs between cost and latency across LLMs for interface generation tasks. Our work sets the foundation for AI-native dynamic control interface generation, paving the way for enhanced interoperability and adaptability in future mobile networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interface on demand: Towards AI native Control interfaces for 6G Dandekar, Abhishek Thapa, Prashiddha D. Rahman, Ashrafur Schulz-Zander, Julius Networking and Internet Architecture Traditional standardized network interfaces face significant limitations, including vendor-specific incompatibilities, rigid design assumptions, and lack of adaptability for new functionalities. We propose a multi-agent framework leveraging large language models (LLMs) to generate control interfaces on demand between network functions (NFs). This includes a matching agent, which aligns required control functionalities with NF capabilities, and a code-generation agent, which generates the necessary API server for interface realization. We validate our approach using simulated multi-vendor gNB and WLAN AP environments. The performance evaluations highlight the trade-offs between cost and latency across LLMs for interface generation tasks. Our work sets the foundation for AI-native dynamic control interface generation, paving the way for enhanced interoperability and adaptability in future mobile networks. |
| title | Interface on demand: Towards AI native Control interfaces for 6G |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.15595 |