Advanced AI Service Provisioning in O-RAN through LLM Engine Integration
The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual. Large Language Models (LLMs) offer strong reasoning and code-generation capabilities but are unsuited for the fast, deterministic inference required in real-time RAN control. We present a proof-of-concept Dual-Brain architecture that combines both strengths: an LLM-based orchestrator translates operator intents into data-collection policies and deployment code, while an automated ML engine, NeuralSmith, trains lightweight c...
| Authors | Seyed Bagher Hashemi Natanzi, Pranshav Gajjar, Bo Tang et al. |
|---|---|
| Primary category | eess.SY |
| Published | 22 May 2026 |
| Last updated at source | 3 June 2026 |
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