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.

翻译：大规模多输入多输出（MIMO）是实现第六代网络所需高数据速率的关键技术，但其性能依赖于低训练开销的有效波束管理。本文提出一种可解释的框架，以解决混合视距（LoS）与非视距（NLoS）传播环境中的波束对准问题。我们的方法利用多模态数据构建虚拟基站（VBS），其几何定义为基站在由3D LiDAR点云重建的反射面上的镜像。这些VBS提供了无线环境主导特征的稀疏空间表征。基于所构建的VBS，我们开发了一种VBS辅助的波束对准方案，包含粗略信道重建和部分波束训练两个步骤。数值结果表明，所提方法在频谱效率方面实现了接近最优的性能。