移动通信2026，Vol.50Issue(8)：31-40,55,11.DOI:10.3969/j.issn.1006-1010.20260602-0007
基于大语言模型的低空无人机波束预测
Beam Prediction for Low-Altitude UAVs Based on Large Language Models
摘要
Abstract
In the context of the rapidly developing low-altitude economy,the high mobility of unmanned aerial vehicles(UAVs)in three-dimensional space causes severe channel state information(CSI)aging and beam misalignment in integrated sensing and communication(ISAC)systems.To address these issues,this paper proposes an end-to-end beamforming prediction framework based on a large language model(LLM).Specifically,we first establish the channel and measurement models for a monostatic shared transceiver scenario and derive the Cramér-Rao lower bound(CRB)for multi-dimensional sensing parameters,including elevation angle,azimuth angle,delay and Doppler shift.We construct an unsupervised loss function based on the derived integrated sensing CRB and fine-tune a pre-trained LLM to extract the spatial-temporal correlations of historical CSI,thereby directly outputting the optimal beamforming vector.Simulation results demonstrate that,when confronting complex,random 3D flight trajectories and high flight speeds,the proposed scheme achieves end-to-end high-precision tracking of the highly maneuverable and non-linear UAV trajectories.Furthermore,its sensing accuracy,robustness against high-speed-induced fading,and system communication rate significantly outperform those of traditional time-series networks and baseline algorithms.关键词
深度学习/通感一体化/波束预测/无人机通信Key words
deep learning/integrated sensing and communication/predictive beamforming/unmanned aerial vehicle communication分类
信息技术与安全科学引用本文复制引用
杜星宇,何宏勇,杨平,武刚,肖悦,魏宁,Saviour ZAMMIT..基于大语言模型的低空无人机波束预测[J].移动通信,2026,50(8):31-40,55,11.基金项目
国家重点研发计划"6G多场景多业务绿色高效组网机制与方法研究"(2026YFE0104900) （2026YFE0104900）
国家科技重大专项"面向6G的多源融合智能协同通感一体关键技术研究与验证"(2025ZD1302000) 本文得到国家重点研发计划"6G多场景多业务绿色高效组网机制与方法研究"(2026YFE0104900)与Xjenza Malta(XM)项目GEMSS-6G 的部分支持. （2025ZD1302000）
