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

TL;DR

This paper introduces a reflection-driven self-optimization framework for 6G RAN, integrating agentic AI with simulation-in-the-loop workflows to enable autonomous, self-improving network management.

Contribution

It presents the first framework combining agentic AI with high-fidelity simulation for self-optimization in 6G networks, including a novel multi-agent architecture.

Findings

17.1% higher throughput in interference optimization

67% improved user QoS satisfaction through intent recognition

25% reduced resource utilization during low-traffic periods

Abstract

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, current frameworks provide sophisticated reasoning capabilities but lack mechanisms for empirical validation and self-improvement. This article identifies simulation-in-the-loop validation as a critical enabler for truly 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 agentic AI into a self-correcting system capable of escaping local optima, recognizing implicit user intent, and adapting to dynamic network conditions. Extensive experiments validate significant performance improvements over non-agentic approaches: 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.