Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
This paper introduces Agentic-LTPO, a nested bilevel optimization framework designed to address the limitations of fixed-objective methods in physical layer systems facing dynamic operator policies and real-time constraints. The framework utilizes agentic AI to generate upper-level configurations that translate evolving policies and historical experiences into structured lower-level problems for immediate decision-making.