# Deep learning-based beam alignment in mmWave vehicular networks

arXiv: https://arxiv.org/abs/1910.11402 · HTML: https://mlsanity.com/paper/1910.11402/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Massive MIMO Beam Management in Sub-6 GHz 5G NR](https://mlsanity.com/paper/2204.06064/) (2022)
- [Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems](https://mlsanity.com/paper/2103.00125/) (2021)
- [UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Bandits](https://mlsanity.com/paper/2301.03456/) (2022)
- [AI-Driven Mobility Management for High-Speed Railway Communications: Compressed Measurements and Proactive Handover](https://mlsanity.com/paper/2407.04336/) (2024)

## Abstract (arXiv)

Millimeter wave channels exhibit structure that allows beam alignment with fewer channel measurements than exhaustive beam search. From a compressed sensing (CS) perspective, the received channel measurements are usually obtained by multiplying a CS matrix with a sparse representation of the channel matrix. Due to the constraints imposed by analog processing, designing CS matrices that efficiently exploit the channel structure is, however, challenging. In this paper, we propose an end-to-end deep learning technique to design a structured CS matrix that is well suited to the underlying channel distribution, leveraging both sparsity and the particular spatial structure that appears in vehicular channels. The channel measurements acquired with the designed CS matrix are then used to predict the best beam for link configuration. Simulation results for vehicular communication channels indicate that our deep learning-based approach achieves better beam alignment than standard CS techniques that use the random phase shift-based design.