# Beam Training based on Dynamic Hierarchical Codebook for Millimeter Wave Massive MIMO

arXiv: https://arxiv.org/abs/1901.01425 · HTML: https://mlsanity.com/paper/1901.01425/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2019.

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

- [Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers](https://mlsanity.com/paper/2301.03371/) (2022)
- [HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers](https://mlsanity.com/paper/2401.05420/) (2023)
- [GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication](https://mlsanity.com/paper/2505.17530/) (2025)

## Abstract (arXiv)

Beam training based on hierarchical codebook for millimeter wave (mmWave) massive MIMO is investigated. Unlike the existing work using the same hierarchical codebook to estimate different multi-path components (MPCs), dynamic hierarchical codebooks which are updated according to the estimated MPCs are adopted. Firstly, a generalized hierarchical codebook design method is proposed. Then based on this method, a beam training method which dynamically updates the hierarchical codebook by removing the contribution of the estimated MPCs from the codebook is proposed. Simulation results verify the effectiveness of our method and show that the proposed method outperforms the existing ones in terms of the success detection rate of beam training.