Open RAN Conflict Agents: Detecting and Mitigating xApp Conflicts with Generative Agents

Abstract

The Open Radio Access Network (ORAN) architecture promotes flexibility and innovation by allowing independently developed xApps and rApps to control network behavior. However, this disaggregated design also introduces risks of resource conflicts that are difficult to detect and resolve, especially in unseen or dynamic environments. We propose ORCA, a conflict management framework that leverages generative LLM agents to reason about the importance of control parameters under environmental uncertainty. The framework incorporates symbolic reasoning through a novel β-Reasoning Cascade and introduces Segmented Active RAG (SA-RAG) to retrieve relevant domain knowledge dynamically. Evaluations on diverse environments show that ORCA reduces cosine error by 60.7% and NRMSE by 65.1% over the best baselines, achieving the most accurate feature importance estimates for conflict detection. Our findings demonstrate the potential of LLM-driven reasoning to manage conflicts in complex and dynamic RAN systems.