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

2026-03-17

AI Summary

This work addresses the inefficiency and misalignment challenges of conventional beam training in near-field ultra-massive MIMO systems, where spherical wavefronts render traditional far-field assumptions invalid in complex three-dimensional low-altitude environments. To overcome these limitations, the paper introduces, for the first time, a multimodal large language model (MLLM) that integrates historical GPS data, RGB images, LiDAR point clouds, and task-oriented textual prompts. By leveraging structure-aware fusion of multi-source information and the reasoning and generalization capabilities of large language models, the proposed approach explicitly models the near-field spherical wave propagation characteristics. This paradigm transcends the constraints of conventional codebook-based beamforming in the joint angle–distance domain, significantly enhancing beam prediction accuracy and environmental understanding while substantially reducing beam training overhead in intricate 3D low-altitude scenarios.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Planning, Routing, and Scheduling: Planning with Language Models

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
• Search and Retrieval-Augmented AI: Large language models for search

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...

Problem

• near-field XL-MIMO
• beam prediction
• environmental understanding
• spherical wavefront
• 3D low-altitude environment

Innovation

• Multimodal LLM
• Near-field XL-MIMO
• Beam Prediction
• Structure-Aware Fusion
• Environmental Understanding