# Learning Site-Specific Probing Beams for Fast mmWave Beam Alignment

arXiv: https://arxiv.org/abs/2107.13121 · HTML: https://mlsanity.com/paper/2107.13121/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2021.

## Built on by (most cited first, 7 of 7)

- [Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems](https://mlsanity.com/paper/2507.14180/) (2025)
- [Efficient Beam Search for Initial Access Using Collaborative Filtering](https://mlsanity.com/paper/2209.06669/) (2022)
- [Hierarchical ML Codebook Design for Extreme MIMO Beam Management](https://mlsanity.com/paper/2312.02178/) (2023)
- [Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations](https://mlsanity.com/paper/2312.10490/) (2023)
- [Neural Codebook Design for Network Beam Management](https://mlsanity.com/paper/2403.03053/) (2024)
- [Physics-Informed Neural Networks for MIMO Beam Map and Environment Reconstruction](https://mlsanity.com/paper/2510.21238/) (2025)
- [Meta-Transfer Learning for mmWave Beam Alignment](https://mlsanity.com/paper/2607.00860/) (2026)

## Abstract (arXiv)

Beam alignment - the process of finding an optimal directional beam pair - is a challenging procedure crucial to millimeter wave (mmWave) communication systems. We propose a novel beam alignment method that learns a site-specific probing codebook and uses the probing codebook measurements to predict the optimal narrow beam. An end-to-end neural network (NN) architecture is designed to jointly learn the probing codebook and the beam predictor. The learned codebook consists of site-specific probing beams that can capture particular characteristics of the propagation environment. The proposed method relies on beam sweeping of the learned probing codebook, does not require additional context information, and is compatible with the beam sweeping-based beam alignment framework in 5G. Using realistic ray-tracing datasets, we demonstrate that the proposed method can achieve high beam alignment accuracy and signal-to-noise ratio (SNR) while significantly - by roughly a factor of 3 in our setting - reducing the beam sweeping complexity and latency.