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

This paper proposes a novel neural network architecture, that we call an auto-precoder. This auto-precoder network 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 signal). Simulation results show that the proposed approach can significantly reduce 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.

I. Introduction

Hybrid analog/digital architectures have attracted significant interest in the last few years thanks to their capability of achieving high data rates with energy-efficient hardware. To design the hybrid precoding matrices, however, an explicit estimation of the mmWave channel is normally required. This mmWave channel estimation is a challenging task because of the large numbers of antennas at both the transmitters and receivers, which result in high training overhead, and the strict hardware constraints on the RF chains [1], [2]. Leveraging the sparsity of the mmWave channels, several compressive sensing based channel estimation solutions have been proposed and showed promising performance [1], [3]. Essentially, these compressive sensing solutions for the mmWave channel estimation problem normally require an order of magnitude less training pilots compared to exhaustive search approaches [4]. But can we do betterƒ In this paper, we show that machine learning tools can efficiently leverage the prior observations about the channel estimates and the hybrid precoding designs to significantly reduce the training overhead associated with the mmWave channel training and precoding design problem.