# Machine Learning Techniques for Blind Beam Alignment in mmWave Massive MIMO

## TL;DR

This paper introduces machine learning techniques to efficiently align beams in mmWave MIMO systems with minimal pilot overhead.

## Contribution

A novel ML-based blind beam alignment method is proposed, reducing pilot overhead using low-complexity models.

## Key findings

- ML models accurately predict non-sounded beams using only 10% of the total beams.
- The method works across various codebook sizes from 128×128 to 1024×1024.
- Received Signal Energies are used to train models without requiring channel state information.

## Abstract

This paper proposes methods for Machine Learning (ML)-based Beam Alignment (BA), using low-complexity ML models, and achieves a small pilot overhead. We assume a single-user massive mmWave MIMO, Uplink, using a fully analog architecture. Assuming large-dimension codebooks of possible beam patterns at UE and BS, this data-driven and model-based approach aims to partially and blindly sound a small subset of beams from these codebooks. The proposed BA is blind (no CSI), based on Received Signal Energies (RSEs), and circumvents the need for exhaustively sounding all possible beams. A sub-sampled subset of beams is then used to train several ML models such as low-rank Matrix Factorization (MF), non-negative MF (NMF), and shallow Multi-Layer Perceptron (MLP). We provide an extensive mathematical description of these models and the algorithms for each of them. Our extensive numerical results show that, by sounding only 10% of the beams from the UE and BS codebooks, the proposed ML tools are able to accurately predict the non-sounded beams through multiple transmitted power regimes. This observation holds as the codebook sizes at UE and BS vary from 128×128 to 1024×1024.