Multi-modal Data Driven Virtual Base Station Construction for Massive MIMO Beam Alignment

TL;DR

This paper introduces a novel multi-modal data-driven framework for efficient beam alignment in Massive MIMO systems, leveraging virtual base stations constructed from 3D LiDAR data to improve spectral efficiency in complex environments.

Contribution

It presents an interpretable approach that constructs virtual base stations from multi-modal data, enhancing beam alignment in mixed LoS/NLoS environments with low training overhead.

Findings

Achieves near-optimal spectral efficiency performance.

Utilizes 3D LiDAR data to construct virtual base stations.

Reduces beam training overhead significantly.

Abstract

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.