Deep Learning for Massive MIMO Channel State Acquisition and Feedback

Abstract

Massive multiple-input multiple-output (MIMO) systems are a
main enabler of the excessive throughput requirements in 5G and future
generation wireless networks as they can serve many users simultaneously
with high spectral and energy efficiency. To achieve this massive
MIMO systems require accurate and timely channel state information
(CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a
training overhead, which scales with the number of antennas, users, and
subcarriers. Reducing the training overhead in massive MIMO systems
has been a major topic of research since the emergence of the concept.
Recently, deep learning (DL)-based approaches have been proposed
and shown to provide significant reduction in the CSI acquisition and
feedback overhead in massive MIMO systems compared to traditional
techniques. In this paper, we present an overview of the state-of-the-art
DL architectures and algorithms used for CSI acquisition and feedback,
and provide further research directions.