X-RIC: An Agentic RAN-Intelligent Controller Benchmark for Enhancing Reinforcement Learning

Key points

• The O-RAN ALLIANCE introduces architectural flexibility through disaggregation and AI-driven control.1
• While the RAN Intelligent Controller (RIC) enables centralized optimization, current AI approaches lack reasoning capabilities and explainability.1
• X-RIC combines reactive Reinforcement Learning (RL) with strategic agentic reasoning through: (i) automated Behavioral Cloning (BC) using domain heuristics for sample-efficient training, (ii) a Telecom Language Model (TLM), a transformer-based reasoning module providing contextual abstraction and interpretability, and (iii) closed-loop co-adaptation within the Near-RT RIC control loop enabling continuous policy refinement.1
• To ensure deployment readiness, we introduce BARX (Benchmarking Agentic Reasoning for RAN), a domain-specific evaluation framework assessing agentic control across latency, compliance, and determinism metrics.1

Sources (1)

• [1]X-RIC: An Agentic RAN-Intelligent Controller Benchmark for Enhancing Reinforcement Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:34 PM

  We propose eXplainable RIC (X-RIC), a novel framework integrating agentic AI into O-RAN architectures.

  The O-RAN ALLIANCE introduces architectural flexibility through disaggregation and AI-driven control.

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