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arXiv · 2505.08267

Sparsity-Aware Near-Field Beam Training via Multi-Beam Combination

Zijun Wang·Rama Kiran·Jinesh Nair·Chien-Hua Chen·Tzu-Han Chou·Shawn Tsai·Rui Zhang
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Abstract

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%.

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Zijun Wang, Rama Kiran, Jinesh Nair, Chien-Hua Chen, Tzu-Han Chou, Shawn Tsai, Rui Zhang. 2025-05-13. Sparsity-Aware Near-Field Beam Training via Multi-Beam Combination. https://arxiv.org/abs/2505.08267

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