# Millimeter Wave Beam Recommendation via Tensor Completion

## TL;DR

This paper introduces a tensor completion-based method for efficient millimeter-wave beam alignment, significantly reducing training overhead while maintaining high accuracy in beam recommendation.

## Contribution

It proposes the first tensor completion approach for beam recommendation using limited measurements, improving alignment accuracy with minimal training data.

## Key findings

- Achieves 80% correct alignment with only 20% position measurements.
- Reduces trained beams to 2%, outperforming existing schemes.
- Demonstrates effectiveness using Quadriga channel simulations.

## Abstract

Accurate and fast beam-alignment is essential to cope with the fast-varying environment in millimeter-wave communications. A data-driven approach is a promising solution to reduce the training overhead by leveraging side information and on-the-field measurements. In this work, a two-stage tensor completion algorithm is proposed to predict the received power on a set of possible users' positions, given received power measurements on a small subset of positions. Based on these predictions and on positional side information, a small subset of beams is recommended to reduce the training overhead of beam-alignment. Numerical results evaluated with the Quadriga channel simulator demonstrate that the proposed algorithm achieves correct alignment with high probability using small training overhead: given power measurement on only 20% of the possible positions when using a discrete coverage area, our algorithm attains a probability of correct alignment of 80%, with only 2% of trained beams, as opposed to a state-of-the-art scheme which achieves 50% correct alignment in the same configuration. To the best of our knowledge, this is the first work to consider the beam recommendation problem based on measurements collected on a small subset of positions.