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

Yijie Bian, Wei Guo, Jie Yang, Shenghui Song, Jun Zhang, Shi Jin, Khaled B. Letaief

TL;DR

An interpretable framework to tackle beam alignment in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) propagation environments with near-optimal performance in terms of spectral efficiency is proposed.

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.

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

TL;DR

An interpretable framework to tackle beam alignment in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) propagation environments with near-optimal performance in terms of spectral efficiency is proposed.

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.
Paper Structure (15 sections, 10 equations, 3 figures, 2 tables)

This paper contains 15 sections, 10 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. VBS-assisted Beam Alignment
  4. Motivations and Definition
  5. VBS Construction and Storage
  6. Geometric Information Extraction
  7. Computation of VBSs' Locations
  8. Coverage Computation and Storage
  9. VBS-assisted Beam Alignment with Coarse Channel Reconstruction
  10. Possible AoA, AoD and Distance Computation
  11. Path Loss Computation
  12. Results and Discussion
  13. Simulation Setup
  14. Results and Discussion
  15. Conclusion

Figures (3)

  • Figure 1: Illustration of 1-order VBSs with corresponding coverage areas. With the ground reflection, there are 3 VBSs in the region. Apart from the shown 2 VBSs in red and orange, the VBSs with respect to the ground are below the physical BS and are not shown.
  • Figure 2: Illustration of the VBS construction process.
  • Figure 3: SE performance versus Top-$S$ partial beam training number under the $N_\mathrm{BS}=256, N_\mathrm{UE}=16$ configuration.

Theorems & Definitions (1)

  • Remark 1