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

TL;DR

This paper introduces a sparsity-aware, adaptive near-field beam training method that combines multiple beams and uses LASSO regression to improve channel estimation, reduce feedback, and enhance accuracy in multi-user environments.

Contribution

It proposes a novel multi-beam combination approach with LASSO-based sparsity exploitation for near-field beam training, including an off-grid refinement scheme for better accuracy.

Findings

Reduces feedback overhead by up to 95% with near-field codebook.

Maintains robustness under low SNR conditions.

Improves reconstruction accuracy by 69.4% with off-grid refinement.

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%.