# Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems

## TL;DR

This paper introduces a deep learning-based auto-precoder that jointly senses millimeter wave channels and designs hybrid precoding matrices with minimal training, significantly reducing overhead in massive MIMO systems.

## Contribution

It proposes a novel neural network architecture for joint channel sensing and hybrid precoding design, leveraging prior observations and environment awareness to improve efficiency.

## Key findings

- Requires only 8-16 pilots for near-optimal performance in 64x64 MIMO systems.
- Reduces training overhead compared to classical solutions.
- Achieves high achievable rates with minimal training data.

## Abstract

This paper proposes a novel neural network architecture, that we call an auto-precoder, and a deep-learning based approach that jointly senses the millimeter wave (mmWave) channel and designs the hybrid precoding matrices with only a few training pilots. More specifically, the proposed machine learning model leverages the prior observations of the channel to achieve two objectives. First, it optimizes the compressive channel sensing vectors based on the surrounding environment in an unsupervised manner to focus the sensing power on the most promising spatial directions. This is enabled by a novel neural network architecture that accounts for the constraints on the RF chains and models the transmitter/receiver measurement matrices as two complex-valued convolutional layers. Second, the proposed model learns how to construct the RF beamforming vectors of the hybrid architectures directly from the projected channel vector (the received sensing vector). The auto-precoder neural network that incorporates both the channel sensing and beam prediction is trained end-to-end as a multi-task classification problem. Thanks to this design methodology that leverages the prior channel observations and the implicit awareness about the surrounding environment/user distributions, the proposed approach significantly reduces the training overhead compared to classical (non-machine learning) solutions. For example, for a system of 64 transmit and 64 receive antennas, with 3 RF chains at both sides, the proposed solution needs only 8 or 16 channel training pilots to directly predict the RF beamforming/combining vectors of the hybrid architectures and achieve near-optimal achievable rates. This highlights a promising solution for the channel estimation and hybrid precoding design problem in mmWave and massive MIMO systems.