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arXiv · 2603.16143

Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction

Mengyuan Li·Qianfan Lu·Jiachen Tian·Hongjun Hu·Yu Han·Xiao Li·Chao-kai Wen·Shi Jin
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Abstract

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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Mengyuan Li, Qianfan Lu, Jiachen Tian, Hongjun Hu, Yu Han, Xiao Li, Chao-kai Wen, Shi Jin. 2026-03-17. Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction. https://arxiv.org/abs/2603.16143

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