エージェント型AIによるポリシー駆動物理層システムの二段階長期最適化
Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
ネットワーク運用者のポリシー変化やリアルタイム制約に対応するため、エージェント型AIを用いた二段階最適化フレームワークを提案し、セルフリーMIMOビームフォーミングで長期性能を57.2%向上させた。
著者: Bingnan Xiao, Chenhao Yang, Wei Ni, Xin Wang, Tony Q. S. Quek
分類: cs.AI
原文アブストラクト
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.