# Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding

## TL;DR

This paper introduces a deep learning-based hybrid precoding scheme for mmWave massive MIMO systems that reduces computational complexity and improves spectral efficiency compared to traditional methods.

## Contribution

It proposes a novel DNN-based hybrid precoding framework that optimizes precoder selection, outperforming existing schemes in efficiency and performance.

## Key findings

- Reduces computational complexity of hybrid precoding.
- Improves bit error ratio and spectrum efficiency.
- Outperforms conventional hybrid precoding schemes.

## Abstract

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been regarded to be an emerging solution for the next generation of communications, in which hybrid analog and digital precoding is an important method for reducing the hardware complexity and energy consumption associated with mixed signal components. However, the fundamental limitations of the existing hybrid precoding schemes is that they have high computational complexity and fail to fully exploit the spatial information. To overcome these limitations, this paper proposes, a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN). Specifically, the hybrid precoder is selected through training based on the DNN for optimizing precoding process of the mmWave massive MIMO. Additionally, we present extensive simulation results to validate the excellent performance of the proposed scheme. The results exhibit that the DNN-based approach is capable ofminimizing the bit error ratio (BER) and enhancing spectrum efficiency of the mmWave massive MIMO, which achieves better performance in hybrid precoding compared with conventional schemes while substantially reducing the required computational complexity.