This paper introduces a novel neural network framework called M2BeamLLM for beam prediction in millimeter-wave (mmWave) massive multi-input multi-output (mMIMO) communication systems. M2BeamLLM integrates multi-modal sensor data, including images, radar, LiDAR, and GPS, leveraging the powerful reasoning capabilities of large language models (LLMs) such as GPT-2 for beam prediction. By combining sensing data encoding, multimodal alignment and fusion, and supervised fine-tuning (SFT), M2BeamLLM achieves significantly higher beam prediction accuracy and robustness, demonstrably outperforming traditional deep learning (DL) models in both standard and few-shot scenarios. Furthermore, its prediction performance consistently improves with increased diversity in sensing modalities. Our study provides an efficient and intelligent beam prediction solution for vehicle-to-infrastructure (V2I) mmWave communication systems.
@article{arxiv.2506.14532,
title = {M2BeamLLM: Multimodal Sensing-empowered mmWave Beam Prediction with Large Language Models},
author = {Can Zheng and Jiguang He and Chung G. Kang and Guofa Cai and Zitong Yu and Merouane Debbah},
journal= {arXiv preprint arXiv:2506.14532},
year = {2025}
}