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.