用智能体AI动态优化网络物理层配置，提升长期性能57.2%。
Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
- 上层用智能体生成可适应政策变化的配置，下层实时求解波束成形。
- 在无蜂窝MIMO场景中，相比传统方法系统长期性能提升57.2%。
- 适合需要持续适应策略与环境变化的通信系统设计者。
网络运营商政策、服务需求及严格实时约束的变化使传统固定目标与约束的方法失效。本文提出代理式长期性能优化（Agentic-LTPO），一种可应用于自适应物理层配置的嵌套双层优化框架。核心思想是利用智能体AI在双层优化结构中生成上层配置，将演进的运营商策略、环境摘要和历史经验转化为结构化的下层优化问题配置。下层基于更新后的配置实时求解物理层决策。以无蜂窝MIMO波束成形为例，我们设计了新的多智能体决策流程，上层引入检索增强的经验验证机制，下层采用闭式波束成形器。实验表明，Agentic-LTPO对动态运营商策略具有强适应性，相比传统方法系统长期性能提升57.2%。
原文摘要 · Abstract (English)
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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