Massive multiple-input multiple-output (MIMO) is a key enabler for the high data rates required by the sixth-generation networks, yet its performance hinges on effective beam management with low training overhead. This paper proposes an interpretable framework to tackle beam alignment in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) propagation environments. Our approach utilizes multi-modal data to construct virtual base stations (VBSs), which are geometrically defined as mirror images of the base station across reflecting surfaces reconstructed from 3D LiDAR points. These VBSs provide a sparse and spatial representation of the dominant features of the wireless environment. Based on the constructed VBSs, we develop a VBS-assisted beam alignment scheme comprising coarse channel reconstruction followed by partial beam training. Numerical results demonstrate that the proposed method achieves near-optimal performance in terms of spectral efficiency.
@article{arxiv.2602.22796,
title = {Multi-modal Data Driven Virtual Base Station Construction for Massive MIMO Beam Alignment},
author = {Yijie Bian and Wei Guo and Jie Yang and Shenghui Song and Jun Zhang and Shi Jin and Khaled B. Letaief},
journal= {arXiv preprint arXiv:2602.22796},
year = {2026}
}