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

The paper introduces Agentic long-term performance optimization (Agentic-LTPO), a bilevel optimization framework aimed at improving adaptive physical layer configurations in response to changing network policies and real-time constraints. It employs a multi-agent decision process for upper-level configuration generation and a closed-form beamformer for lower-level optimization, achieving a 57.2% improvement in long-term performance over traditional methods in a cell-free MIMO beamforming scenario. This approach is significant for practitioners as it enhances system adaptability and efficiency in dynamic network environments.