Small changes in high altitude platform (HAP) attitude can cause significant deviations in HAP downlink beam directions, thereby severely degrading HAP downlink communication performance. In this paper, we develop a multimodal large language model (LLM) enabled beamforming framework to achieve robust HAP downlink communications.Specifically, we design a vision-language LLM (VL-LLM) that learns from multivariate flight telemetry to forecast short-term HAP attitudes under platform shaking and support delay-aware proactive beam steering.We design an offline forecast-error calibration procedure to obtain upper bounds on forecast errors and improve the reliability of proactive analog beam steering.Based on the attitude forecasts, we proactively update the analog beamformer and propose a QoS-driven beamforming and admission method with a lightweight feasibility-enforcement step to satisfy instantaneous transmit-power and QoS requirements.Simulation results indicate that the designed VL-LLM can accurately capture changes in the HAP attitude and the proposed beamforming method achieves a 22.1% higher user service ratio and a 12.5% higher sum-rate than representative baselines.The measured mean and p99 total latencies are 36.24 ms and 40.13 ms, respectively, supporting practical delay-aware deployment.

翻译：高空平台微小姿态变化会导致下行波束方向显著偏移，严重降低通信性能。本文提出一种基于多模态大语言模型的波束赋形框架，实现鲁棒的高空平台下行通信。具体而言，我们设计了一种视觉-语言大语言模型，通过学习多元飞行遥测数据预测平台抖动下的短期姿态，并支持时延感知的主动波束转向。我们提出离线预测误差校准流程，以获得预测误差上界并提升主动模拟波束转向的可靠性。基于姿态预测，我们主动更新模拟波束成形器，并设计了一种服务质量驱动的波束赋形与接入方法，通过轻量级可行性增强步骤满足瞬时发射功率与服务质量约束。仿真结果表明，所设计的视觉-语言大语言模型能精确捕捉高空平台姿态变化，且所提波束赋形方法在用户服务比率和总速率上分别比典型基准方案提升22.1%和12.5%。实测平均延迟与99分位延迟分别为36.24毫秒和40.13毫秒，支持实际时延感知部署。