Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows

doi:10.1109/mwc.2026.3713130

Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows

2026-07-27

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DOI：10.1109/mwc.2026.3713130

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The escalating complexity of sixth-generation (6G) networks demands unprecedented levels of autonomy beyond the capabilities of traditional optimization-based and current AI-based resource management approaches. While agentic AI has emerged as a promising paradigm for autonomous RAN, existing frameworks remain fundamentally open-loop, excelling at reasoning and planning but lacking empirical validation mechanisms to verify decisions and learn from outcomes. This article identifies simulation-in-the-loop validation as a critical enabler for autonomous networks, where AI agents can empirically verify decisions and learn from outcomes. We present the first reflection-driven self-optimization framework that integrates agentic AI with high-fidelity network simulation in a closed-loop architecture. Our system orchestrates four specialized agents, including scenario, solver, simulation, and reflector agents, working in concert to transform unverified open-loop planning into a continuous, evidence-grounded, self-optimization workflow. Extensive experiments validate significant performance improvements over non-agentic baselines: 17.1% higher throughput in interference optimization, 67% improved user QoS satisfaction through intent recognition, and 25% reduced resource utilization during low-traffic periods while maintaining service quality.

Keywords:

6G networks

reflection-driven agentic AI

simulation-in-the-loop

autonomous RAN

Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows

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