Multimodal Large Language Model Enabled Robust Beamforming for HAP Downlink Communications

2026-04-10

AI Summary

This work addresses the challenge that minor attitude perturbations in high-altitude platform (HAP) systems can cause severe downlink beam misalignment, significantly degrading communication performance. To tackle this issue, the study introduces, for the first time, a vision-language large language model (VL-LLM) into HAP communications and proposes an active, robust analog beamforming framework. The approach leverages the VL-LLM to fuse visual observations with flight telemetry data for short-term attitude prediction, combined with offline error calibration and a lightweight, QoS-driven beam control mechanism to enable low-latency, highly robust beam tracking. Experimental results demonstrate that the proposed method improves user service rate by 22.1% and sum rate by 12.5%, achieving average and p99 end-to-end latencies of 36.24 ms and 40.13 ms, respectively, thereby meeting real-time requirements for practical deployment.

Technology Category

• Computer Vision: Language and Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• HAP attitude
• beamforming
• downlink communication
• beam direction deviation
• communication performance degradation

Innovation

• Multimodal Large Language Model
• Vision-Language LLM
• Robust Beamforming
• HAP Communications
• Proactive Beam Steering