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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.22796 |
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| _version_ | 1866918357342617600 |
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| author | Bian, Yijie Guo, Wei Yang, Jie Song, Shenghui Zhang, Jun Jin, Shi Letaief, Khaled B. |
| author_facet | Bian, Yijie Guo, Wei Yang, Jie Song, Shenghui Zhang, Jun Jin, Shi Letaief, Khaled B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22796 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-modal Data Driven Virtual Base Station Construction for Massive MIMO Beam Alignment Bian, Yijie Guo, Wei Yang, Jie Song, Shenghui Zhang, Jun Jin, Shi Letaief, Khaled B. Information Theory 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. |
| title | Multi-modal Data Driven Virtual Base Station Construction for Massive MIMO Beam Alignment |
| topic | Information Theory |
| url | https://arxiv.org/abs/2602.22796 |