用多模型智能体自动把无线网络需求转成可执行方案
ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks
- 设计多智能体协作框架，分步完成问题解析与求解
- 在波束成形优化中表现媲美专家，优于单一大模型
- 适合无线网络设计自动化，尤其复杂场景
新兴的6G网络依赖复杂的跨层优化，但将高层意图手动转化为数学形式仍是瓶颈。尽管大型语言模型（LLMs）有潜力，但单体方法往往缺乏领域知识支撑、约束感知能力与验证机制。为此，我们提出ComAgent——一种基于多LLM的智能体式AI框架。该框架采用闭环感知-规划-行动-反思循环，协调文献检索、编码与评分等专用智能体，自主生成可求解的数学模型及可复现的仿真。通过迭代分解问题并自我纠错，有效弥合用户意图与执行之间的差距。评估显示，ComAgent在复杂波束成形优化中达到专家级性能，且在多种无线任务上超越单体大模型，展现了其在新兴无线网络自动化设计中的潜力。
原文摘要 · Abstract (English)
Emerging 6G networks rely on complex cross-layer optimization, yet manually translating high-level intents into mathematical formulations remains a bottleneck. While Large Language Models (LLMs) offer promise, monolithic approaches often lack sufficient domain grounding, constraint awareness, and verification capabilities. To address this, we present ComAgent, a multi-LLM agentic AI framework. ComAgent employs a closed-loop Perception-Planning-Action-Reflection cycle, coordinating specialized agents for literature search, coding, and scoring to autonomously generate solver-ready formulations and reproducible simulations. By iteratively decomposing problems and self-correcting errors, the framework effectively bridges the gap between user intent and execution. Evaluations demonstrate that ComAgent achieves expert-comparable performance in complex beamforming optimization and outperforms monolithic LLMs across diverse wireless tasks, highlighting its potential for automating design in emerging wireless networks.
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