Evolving Inspectable O-RAN Slicing xApps with LLMs
Open RAN (O-RAN) slicing xApps must adapt resource allocations to changing channel conditions and traffic demands while meeting service-level agreements (SLAs). Deep reinforcement learning can produce adaptive policies, but their allocation rules remain encoded in neural-network parameters. Our goal is to retain this adaptability while making the controller's decision logic directly inspectable and editable by operators. We use a large language model (LLM) to evolve slicing controllers as compact Python programs whose decision logic remains readable and editable after optimization. The LLM proposes and revises candidates offline, while a calibrated simulator scores them, and the selected ...
| Authors | Faezeh Dehghan Tarzjani, Bhaskar Krishnamachari |
|---|---|
| Primary category | eess.SY |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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