# Complex-BP-Neural-Network-based Hybrid Precoding for Millimeter Wave Multiuser Massive MIMO Systems

## TL;DR

This paper introduces a complex neural network-based hybrid precoding method for millimeter wave multiuser massive MIMO systems, aiming to enhance energy efficiency and performance over traditional algorithms.

## Contribution

It proposes a novel BP-neural-network-enabled hybrid precoding algorithm with complex neural networks, improving approximation of zero-forcing precoding in mmWave MIMO systems.

## Key findings

- Achieves near-optimal performance in approximating ZF precoding.
- Reduces computational complexity compared to conventional methods.
- Enhances energy efficiency in massive MIMO systems.

## Abstract

The high energy consumption of massive multi-input multi-out (MIMO) system has become a prominent problem in the millimeter wave(mm-Wave) communication scenario. The hybrid precoding technology greatly reduces the number of radio frequency(RF) chains by handing over part of the coding work to the phase shifting network, which can effectively improve energy efficiency. However, conventional hybrid precoding algorithms based on mathematical means often suffer from performance loss and high computational complexity. In this paper, a novel BP-neural-network-enabled hybrid precoding algorithm is proposed, in which the full-digital zero-forcing(ZF) precoding is set as the training target. Considering that signals at the base station are complex, we choose the complex neural network that has a richer representational capacity. Besides, we present the activation function of the complex neural network and the gradient derivation of the back propagation process. Simulation results demonstrate that the performance of the proposed hybrid precoding algorithm can optimally approximate the ZF precoding.