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

2026-02-26

AI Summary

This work addresses the high training overhead and low efficiency of beam alignment in massive MIMO systems operating in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) environments. To this end, the authors propose a virtual base station construction method that integrates 3D LiDAR point clouds with multimodal sensing. By reconstructing environmental reflectors and generating mirror images of the base station, the approach establishes a sparse geometric representation of the propagation space, embedding structural environmental information into the beam alignment process. This enables interpretable coarse channel reconstruction and facilitates co-optimization with partial beam training. The proposed method significantly reduces training overhead while achieving near-optimal spectral efficiency, thereby enhancing both the efficiency and robustness of beam alignment in complex propagation scenarios.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Massive MIMO
• beam alignment
• multi-modal data
• LoS/NLoS propagation
• training overhead

Innovation

• virtual base station
• multi-modal data fusion
• beam alignment
• massive MIMO
• 3D LiDAR reconstruction