Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914494001709056 |
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| author | Hu, Yunhao Lyu, Xinchen Ren, Chenshan Chen, Keda Cui, Qimei Tao, Xiaofeng |
| author_facet | Hu, Yunhao Lyu, Xinchen Ren, Chenshan Chen, Keda Cui, Qimei Tao, Xiaofeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows Hu, Yunhao Lyu, Xinchen Ren, Chenshan Chen, Keda Cui, Qimei Tao, Xiaofeng Networking and Internet Architecture Multiagent Systems 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. |
| title | Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows |
| topic | Networking and Internet Architecture Multiagent Systems |
| url | https://arxiv.org/abs/2512.20640 |