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

TL;DR

This paper introduces a novel multimodal LLM framework that integrates diverse data sources to improve near-field beam prediction in complex 3D environments, enhancing environmental understanding and beam alignment accuracy.

Contribution

It presents a new LLM-based multimodal approach combining GPS, RGB, LiDAR, and textual prompts for better environmental comprehension in XL-MIMO systems.

Findings

Improved beam prediction accuracy in complex environments.

Effective integration of multimodal data for environmental understanding.

Enhanced robustness of beam alignment in near-field scenarios.

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...