# Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning

## TL;DR

This paper introduces a deep learning-based beamforming neural network for large-scale antenna arrays, improving spectral efficiency and robustness under hardware constraints and imperfect channel information in millimeter wave systems.

## Contribution

It presents a novel deep learning approach and neural network architecture for beamforming design, addressing hardware limitations and CSI imperfections in large-scale antenna arrays.

## Key findings

- Significant performance improvement over conventional algorithms
- Strong robustness to imperfect channel state information
- Effective optimization of spectral efficiency

## Abstract

Beamforming (BF) design for large-scale antenna arrays with limited radio frequency chains and the phase-shifter-based analog BF architecture, has been recognized as a key issue in millimeter wave communication systems. It becomes more challenging with imperfect channel state information (CSI). In this letter, we propose a deep learning based BF design approach and develop a BF neural network (BFNN) which can be trained to learn how to optimize the beamformer for maximizing the spectral efficiency with hardware limitation and imperfect CSI. Simulation results show that the proposed BFNN achieves significant performance improvement and strong robustness to imperfect CSI over the conventional BF algorithms.