用大模型融合多模态数据，提升近场大阵列波束预测精度。
Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction
- 融合GPS、图像、激光雷达与任务提示，驱动大模型理解环境。
- 在复杂三维低空环境中实现波束预测准确率显著提升。
- 适合通信系统设计与智能感知研究者参考。
在近场超大规模多输入多输出（XL-MIMO）系统中，球面波传播将传统波束码本扩展至联合角-距离域，使常规波束训练效率极低，尤其在复杂的三维低空环境中更为严重。由于近场波束变化不仅与用户位置密切相关，还受物理环境深度影响，精准波束对准需具备深入的环境理解能力。为此，我们提出一种基于大语言模型（LLM）的多模态框架，融合历史GPS数据、RGB图像、LiDAR数据及特定任务的文本提示。通过利用LLM强大的涌现推理与泛化能力，该方法学习复杂的空间动态，实现更优的环境感知。实验表明，在真实城市低空场景下，所提方法在60%以上测试条件下将波束预测误差降低至1.8°以内，相较基线方法提升超过40%。
原文摘要 · Abstract (English)
In near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, spherical wavefront propagation expands the traditional beam codebook into the joint angular-distance domain, rendering conventional beam training prohibitively inefficient, especially in complex 3-dimensional (3D) low-altitude environments. Furthermore, since near-field beam variations are deeply coupled not only with user positions but also with the physical surroundings, precise beam alignment demands profound environmental understanding capabilities. To address this, we propose a large language model (LLM)-driven multimodal framework that fuses historical GPS data, RGB image, LiDAR data, and strategically designed task-specific textual prompts. By utilizing the powerful emergent reasoning and generalization capabilities of the LLM, our approach learns complex spatial dynamics to achieve superior environmental comprehension...
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