Beam Prediction Based on Multimodal Large Language Models

TL;DR

This paper introduces a multimodal large language model framework for beam prediction in wireless communication, leveraging sensory data like images and LiDAR to improve accuracy and robustness in dynamic environments.

Contribution

It proposes a novel multimodal LLM-based beam prediction method with specialized encoders, attention mechanisms, and a new large-scale dataset for diverse conditions.

Findings

Achieves 80.8% Top-1 beam prediction accuracy

Outperforms existing multimodal LLM methods in accuracy and gain

Reduces reliance on oracle angle-of-departure knowledge

Abstract

Accurate beam prediction is a key enabler for next-generation wireless communication systems. In this paper, we propose a multimodal large language model (LLM)-based beam prediction framework that effectively utilizes contextual information, provided by sensory data including RGB camera images and LiDAR point clouds. To effectively fuse heterogeneous modalities, we design specialized modality encoders together with a beam-guided attention masking mechanism and a high-frequency temporal alignment strategy, enabling robust cross-modal feature integration under dynamic environments. Furthermore, we construct a large-scale multimodal dataset for communication, named Multimodal-Wireless, which covers diverse weather and traffic conditions with high-fidelity ray-tracing labels. Extensive simulation results demonstrate that the proposed approach significantly reduces the reliance on oracle angle-of-departure knowledge and consistently outperforms state-of-the-art multimodal LLM-based beam prediction methods in terms of beam accuracy and communication performance, improving the average Top-1 accuracy to 80.8% and the average normalized gain to 89.1%.