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%.
@article{arxiv.2505.08267,
title = {Sparsity-Aware Near-Field Beam Training via Multi-Beam Combination},
author = {Zijun Wang and Rama Kiran and Jinesh Nair and Chien-Hua Chen and Tzu-Han Chou and Shawn Tsai and Rui Zhang},
journal= {arXiv preprint arXiv:2505.08267},
year = {2025}
}