# Grid-Free MIMO Beam Alignment through Site-Specific Deep Learning

arXiv: https://arxiv.org/abs/2209.08198 · HTML: https://mlsanity.com/paper/2209.08198/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2022.

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

- [A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning](https://mlsanity.com/paper/2505.09160/) (2025)
- [Generating High Dimensional User-Specific Wireless Channels using Diffusion Models](https://mlsanity.com/paper/2409.03924/) (2024)
- [Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks](https://mlsanity.com/paper/2604.09653/) (2026)
- [Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility](https://mlsanity.com/paper/2606.20098/) (2026)

## Abstract (arXiv)

Beam alignment is a critical bottleneck in millimeter wave (mmWave) communication. An ideal beam alignment technique should achieve high beamforming (BF) gain with low latency, scale well to systems with higher carrier frequencies, larger antenna arrays and multiple user equipments (UEs), and not require hard-to-obtain context information (CI). These qualities are collectively lacking in existing methods. We depart from the conventional codebook-based (CB) approach where the optimal beam is chosen from quantized codebooks and instead propose a grid-free (GF) beam alignment method that directly synthesizes the transmit (Tx) and receive (Rx) beams from the continuous search space using measurements from a few site-specific probing beams that are found via a deep learning (DL) pipeline. In realistic settings, the proposed method achieves a far superior signal-to-noise ratio (SNR)-latency trade-off compared to the CB baselines: it aligns near-optimal beams 100x faster or equivalently finds beams with 10-15 dB better average SNR in the same number of searches, relative to an exhaustive search over a conventional codebook.