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

• URL: http://arxiv.org/abs/2606.24416v1
• Date: Tue, 23 Jun 2026 10:53:12 GMT
• Title: Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
• Abstract summary: Agentic-O is a nested bilevel optimization framework that can be applied to adaptive physical layer problem configuration.<n>Agentic-O exhibits strong adaptability to dynamic operator policies and effectively enhances the system's long-term performance by 57.2% compared to traditional methods.
• Score: 56.931625543734306
• License: http://creativecommons.org/licenses/by/4.0/
• 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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