Millimeter-wave or terahertz communications can meet demands of low-altitude economy networks for high-throughput sensing and real-time decision making. However, high-frequency characteristics of wireless channels result in severe propagation loss and strong beam directivity, which make beam prediction challenging in highly mobile uncrewed aerial vehicles (UAV) scenarios. In this paper, we employ agentic AI to enable the transformation of mmWave base stations toward embodied intelligence. We innovatively design a multi-agent collaborative reasoning architecture for UAV-to-ground mmWave communications and propose a hybrid beam prediction model system based on bimodal data. The multi-agent architecture is designed to overcome the limited context window and weak controllability of large language model (LLM)-based reasoning by decomposing beam prediction into task analysis, solution planning, and completeness assessment. To align with the agentic reasoning process, a hybrid beam prediction model system is developed to process multimodal UAV data, including numeric mobility information and visual observations. The proposed hybrid model system integrates Mamba-based temporal modelling, convolutional visual encoding, and cross-attention-based multimodal fusion, and dynamically switches data-flow strategies under multi-agent guidance. Extensive simulations on a real UAV mmWave communication dataset demonstrate that proposed architecture and system achieve high prediction accuracy and robustness under diverse data conditions, with maximum top-1 accuracy reaching 96.57%.

翻译：毫米波或太赫兹通信能够满足低空经济网络对高吞吐量感知和实时决策的需求。然而，无线信道的高频特性导致严重的传播损耗和强烈的波束方向性，这使得在高度移动的无人驾驶航空器场景中进行波束预测极具挑战性。本文采用智能体人工智能，推动毫米波基站向具身智能转型。我们创新性地设计了一种面向无人机对地毫米波通信的多智能体协同推理架构，并提出了一种基于双模态数据的混合波束预测模型系统。该多智能体架构旨在通过将波束预测分解为任务分析、方案规划和完整性评估，以克服基于大语言模型的推理在上下文窗口有限和可控性弱方面的不足。为了与智能体推理过程相匹配，开发了一个混合波束预测模型系统来处理多模态无人机数据，包括数值化的移动性信息和视觉观测数据。所提出的混合模型系统集成了基于Mamba的时序建模、卷积视觉编码和基于交叉注意力的多模态融合，并在多智能体指导下动态切换数据流策略。在真实的无人机毫米波通信数据集上进行的大量仿真实验表明，所提出的架构和系统在多样化的数据条件下实现了高预测精度和鲁棒性，最高Top-1准确率达到96.57%。