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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.08267 |
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| _version_ | 1866910941266837504 |
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| author | Wang, Zijun Kiran, Rama Nair, Jinesh Chen, Chien-Hua Chou, Tzu-Han Tsai, Shawn Zhang, Rui |
| author_facet | Wang, Zijun Kiran, Rama Nair, Jinesh Chen, Chien-Hua Chou, Tzu-Han Tsai, Shawn Zhang, Rui |
| contents | This paper proposes an adaptive near-field beam training method to enhance performance in multi-user and multipath environments. The approach identifies multiple strongest beams through beam sweeping and linearly combines their received signals - capturing both amplitude and phase - for improved channel estimation. Two codebooks are considered: the conventional DFT codebook and a near-field codebook that samples both angular and distance domains. As the near-field basis functions are generally non-orthogonal and often over-complete, we exploit sparsity in the solution using LASSO-based linear regression, which can also suppress noise. Simulation results show that the near-field codebook reduces feedback overhead by up to 95% compared to the DFT codebook. The proposed LASSO regression method also maintains robustness under varying noise levels, particularly in low SNR regions. Furthermore, an off-grid refinement scheme is introduced to enhance accuracy especially when the codebook sampling is coarse, improving reconstruction accuracy by 69.4%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparsity-Aware Near-Field Beam Training via Multi-Beam Combination Wang, Zijun Kiran, Rama Nair, Jinesh Chen, Chien-Hua Chou, Tzu-Han Tsai, Shawn Zhang, Rui Signal Processing This paper proposes an adaptive near-field beam training method to enhance performance in multi-user and multipath environments. The approach identifies multiple strongest beams through beam sweeping and linearly combines their received signals - capturing both amplitude and phase - for improved channel estimation. Two codebooks are considered: the conventional DFT codebook and a near-field codebook that samples both angular and distance domains. As the near-field basis functions are generally non-orthogonal and often over-complete, we exploit sparsity in the solution using LASSO-based linear regression, which can also suppress noise. Simulation results show that the near-field codebook reduces feedback overhead by up to 95% compared to the DFT codebook. The proposed LASSO regression method also maintains robustness under varying noise levels, particularly in low SNR regions. Furthermore, an off-grid refinement scheme is introduced to enhance accuracy especially when the codebook sampling is coarse, improving reconstruction accuracy by 69.4%. |
| title | Sparsity-Aware Near-Field Beam Training via Multi-Beam Combination |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2505.08267 |