# Site-Specific Beam Alignment in 6G via Deep Learning

arXiv: https://arxiv.org/abs/2403.16186 · HTML: https://mlsanity.com/paper/2403.16186/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity](https://mlsanity.com/paper/2602.18151/) (2026)
- [Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks](https://mlsanity.com/paper/2604.09653/) (2026)

## Abstract (arXiv)

Beam alignment (BA) in modern millimeter wave standards such as 5G NR and WiGig (802.11ay) is based on exhaustive and/or hierarchical beam searches over pre-defined codebooks of wide and narrow beams. This approach is slow and bandwidth/power-intensive, and is a considerable hindrance to the wide deployment of millimeter wave bands. A new approach is needed as we move towards 6G. BA is a promising use case for deep learning (DL) in the 6G air interface, offering the possibility of automated custom tuning of the BA procedure for each cell based on its unique propagation environment and user equipment (UE) location patterns. We overview and advocate for such an approach in this paper, which we term site-specific beam alignment (SSBA). SSBA largely eliminates wasteful searches and allows UEs to be found much more quickly and reliably, without many of the drawbacks of other machine learning-aided approaches. We first overview and demonstrate new results on SSBA, then identify the key open challenges facing SSBA.