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arXiv · 2606.24416

Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems

Bingnan Xiao·Chenhao Yang·Wei Ni·Xin Wang·Tony Q. S. Quek
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Abstract

Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective. This paper presents Agentic long-term performance optimization (Agentic-LTPO), a nested bilevel optimization framework that can be applied to adaptive physical layer problem configuration. The key idea is to employ agentic AI to generate upper-level configurations in a bilevel optimization structure, where evolving operator policies, environment summaries, and historical experiences are translated into structured lower-level optimization problem configurations. The lower level solves the problems with updated configurations for real-time physical-layer decisions. Considering cell-free MIMO beamforming as a use case, we embody Agentic-LTPO by designing a new multi-agent decision process with retrieval-augmented experience-based verification in the upper level, together with a closed-form beamformer in the lower level. Experiments demonstrate that Agentic-LTPO exhibits strong adaptability to dynamic operator policies and effectively enhances the system's long-term performance by 57.2% compared to traditional methods.

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Bingnan Xiao, Chenhao Yang, Wei Ni, Xin Wang, Tony Q. S. Quek. 2026-06-23. Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems. https://arxiv.org/abs/2606.24416

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