# Two-Stage Hierarchical Beam Training for Near-Field Communications

arXiv: https://arxiv.org/abs/2302.12511 · HTML: https://mlsanity.com/paper/2302.12511/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases](https://mlsanity.com/paper/2405.20568/) (2024)
- [Near-field Beam training for Extremely Large-scale MIMO Based on Deep Learning](https://mlsanity.com/paper/2406.03249/) (2024)
- [Structure-Aware Multimodal LLM Framework for Trustworthy Near-Field Beam Prediction](https://mlsanity.com/paper/2603.16143/) (2026)

## Abstract (arXiv)

Extremely large-scale array (XL-array) has emerged as a promising technology to improve the spectrum efficiency and spatial resolution of future wireless systems. However, the huge number of antennas renders the users more likely to locate in the near-field (instead of the far-field) region of the XL-array with spherical wavefront propagation. This inevitably incurs prohibitively high beam training overhead since it requires a two-dimensional (2D) beam search over both the angular and distance domains. To address this issue, we propose in this paper an efficient two-stage hierarchical beam training method for near-field communications. Specifically, in the first stage, we employ the central sub-array of the XL-array to search for a coarse user direction in the angular domain with conventional far-field hierarchical codebook. Then, in the second stage, given the coarse user direction, we progressively search for the fine-grained user direction-and-distance in the polar domain with a dedicatedly designed codebook. Numerical results show that our proposed two-stage hierarchical beam training method can achieve over 99% training overhead reduction as compared to the 2D exhaustive search, yet achieving comparable rate performance.